duke-industrystatisticssymposium (diss2016 ......diss2016,durham,nc shortcourses–september14,2016...

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Duke-Industry Statistics Symposium

(DISS 2016)

PrecisionMedicine in Cancer Research

https://sites.duke.edu/diss2016

DukeUniversity Department of Biostatistics and Bioinformatics

September 14-16, 2016

MillenniumHotel, 2800 CampusWalk Ave, Durham, NC

DISS2016, Durham, NC

KEY EVENTSAND SPONSORS

DukeUniversity Department of Biostatistics and Bioinformatics 2


FOREWORD

Dear Colleagues,

The annual symposium is organized by the Department of Biostatistics and Bioinformatics,

Duke University School ofMedicine, and co-sponsored by Amgen, Boehringer-Ingelheim,

ICSA, NC-ASAChapter, PAREXEL, Quintiles and SAS. It was established 4 years ago to

discuss challenging issues and recent advances related to the clinical development of drugs

and devices and to promote research and collaboration among statisticians from industry,

academia, and regulatory agencies.

The theme of the DISS 2016 is “PrecisionMedicine in Cancer Research.” The first day is

devoted to six short courses. The 2nd daywill start with opening remarks and keynote

speech. Dr Eric Peterson, the Director of Duke Clinical Research Institute, and the

representatives from the co-sponsors will give the opening remarks. Dr Lisa LaVange, the

Director of theOffice of Biostatistics from FDA/CDER, will present the keynote speech on

“PrecisionMedicine Initiatives at FDA.” The rest of the 2nd day and the 3rd daymorning

consist of 17 parallel scientific sessions. A poster session will also be held.

We hope you find the symposium informative and useful.

Sincerely yours,

TheOrganizing Committee of DISS2016

Duke University Department of Biostatistics and Bioinformatics 3


EXECUTIVE COMMITTEEANDORGANIZERS

EXECUTIVE COMMITTEE

Elizabeth DeLong, Chair (Duke) Stephen George (Duke)

René Kubiak (Boehringer-Ingelheim) Kerry Lee (Duke)

Steve Snapinn (Amgen) Fred Snikeris (PAREXEL)

Terry Sosa (Quintiles) Maura Stokes (SAS)

Yi Tsong (FDA)

ORGANIZINGCOMMITTEE

Cliburn Chan (Duke) Shein-Chung Chow, Co-Chair (Duke)

Terry Hyslop (Duke) Qi Jiang (Amgen)

René Kubiak (Boehringer-Ingelheim) DebbieMedlin (Duke)

Marlina Nasution (PAREXEL) Kouros Owzar (Duke)

Frank Rockhold (Duke) Terry Sosa (Quintiles)

Yi Tsong (FDA) Sharon Updike (Duke)

XiaofeiWang, Co-Chair (Duke) YuanWu (Duke)

...

Special thanks to John Bauman fromQuintiles and Craig Ou, Fan Li, Jingyi Lin, Ke Song,

NancyQi, YanlinMa and Yiling Liu fromDuke B&B.



LETTER FROMDEANANDREWS



KEYNOTE SPEECH

...

Title: PrecisionMedicine Initiatives at FDA

Time: September 15 9:30-10:30

Speaker: Lisa LaVange, PhD

Director Office of Biostatistics

Office of Translational Sciences

Center for Drug Evaluation and Research (CDER)

US FDA

Lisa LaVange, PhD, is Director of theOffice of Biostatistics in the Center for Drug Evaluation

and Research (CDER), US Food andDrug Administration (FDA). As Director, she oversees

approximately 195 statistical reviewers and staff members involved in the development

and application of statistical methodology for drug regulation. Prior to joining the FDA, Dr

LaVangewas Professor andDirector of the Collaborative Studies Coordinating Center (CSCC)

in the Department of Biostatistics, Gillings School of Global Public Health at the University

of North Carolina at Chapel Hill (UNC), where she served as Principal Investigator (PI) of

the coordinating centers for several large-scale multi-center clinical trials, epidemiology

studies, and patient registries. Before joining academia, Dr LaVange spent 10 years in the

pharmaceutical industry and 16 years in non-profit research. She is a Fellow of the American

Statistical Association, served as President of the Eastern North American Region of the

International Biometric Society (IBS; 2007), and served on the IBS Executive Board (2013-2014).

She was formerly co-editor of the Journal of Pharmaceutical Statistics and editor-in-chief of

the ASA-SIAMbook series



SHORTCOURSES – SEPTEMBER 14, 2016

Short Courses Time Room

Registration & Light Breakfast 8:00-9:00 Foyer (3rd)

C1: Adaptive Clinical Trial Design - Case Studies 9:00-12:00 Brightleaf E (3rd)

Shiowjen Lee (FDA), Annie Lin (FDA)

C2: Statistical Procedures for Interim 9:00-12:00 Brightleaf F (3rd)

Analysis in Clinical Trials

Gordon Lan (JnJ)

C3: Biomarker Utilities in Adaptive Trials 9:00-12:00 Brightleaf G (3rd)

Robin Bliss (Veristat), JingWang (Gilead)

Boxed Lunches 12:00-1:00 Foyer (3rd)

C4: Adaptive Designs for Dose-Finding Studies 1:00-4:00 Brightleaf E (3rd)

SandeepMenon (Pfizer), Inna Perevozskaya (Pfizer)

C5: Patient-ReportedOutcomes: 1:00-4:00 Brightleaf F (3rd)

Measurement, Implementation and Interpretation

Joseph Cappelleri (Pfizer)

C6: Analytical Similarity Assessment 1:00-4:00 Brightleaf G (3rd)

Shein-Chung Chow (Duke), Yi Tsong (FDA)



C1: ADAPTIVE CLINICAL TRIALDESIGN – CASE STUDIES

...

Title: Adaptive Clinical Trial Design – Case Studies


Instructors: Shiowjen Lee (FDA) and Annie Lin (FDA)

There has been considerable interest among pharmaceutical and other medical product developers

in adaptive clinical trials, in which knowledge learned during the course of a trial affects ongoing

conduct or analysis of the trial. Following the release of the FDA draft Guidance document on

adaptive design clinical trials in early 2010, expectations of an increase in regulatory submissions

involving adaptive design features, particularly for confirmatory trials, were high. There are

indeed concerns regarding the statistical issues and operational challenges in conducting

adaptive design clinical trials. Wewill share our experiences in the reviews of adaptive design

proposals, including surveys performed regarding regulatory submissions of adaptive design

proposals as well as case studies which have been reviewed. Wewill also provide general

recommendations for developing proposals for such trials. Ourmotivation in instructing

this short course is to encourage the best study design proposals to be submitted to FDA.

Sometimes these can be adaptive and sometimes a simpler design is most efficient.

C2: STATISTICAL PROCEDURES FOR INTERIMANALYSIS IN CLINICAL TRIALS

...

Title: Statistical Procedures for Interim Analysis in Clinical Trials


Instructors: Gordon Lan (Johnson & Johnson)

This short course provides an introduction to the design and interim analyses of clinical trials

with the following topics: Sample size and information, Some fundamental statistical tools for

interim data analyses (The trend of the data, Conditional power, Group sequential methods),

Use of theWisconsin software to design sequential trials, Adaptive designs,Survival data

analysis, and Design of multiregional clinical trials (Time permitting).



C3: BIOMARKERUTILITIES INADAPTIVE TRIALS

...

Title: Biomarker Utilities in Adaptive Trials


Instructors: Robin Bliss (Veristat) and JingWang (Gilead)

In this short course, wewill discuss the opportunities and challenges in biomarker utilization

and personalizedmedicine, covering both classical and adaptive designs with biomarkers.

Wewill discuss the design options for biomarkers with very strong credentials, strong credentials

andweak credentials. Related statistical theories and analysis strategies will be coveredwith

case studies. By attending this session, participants will learn some recent developments

in biomarker study from statistical perspective and share their experiences and practical

problems concerning the biomarker utility in drug development.

C4: ADAPTIVEDESIGNS FORDOSE-FINDING STUDIES

...

Title: Adaptive Designs for Dose-Finding Studies


Instructors: SandeepMenon (Pfizer) and Inna Perevozskaya (Pfizer)

Adaptive designs have been increasing in popularity over the past decade. FDA has released

its draft guidance on adaptive designs in 2010, in which it particularly encouraged the use of

“well understood” designs in exploratory space (i.e. Phase I and Phase II studies). Such studies

are often referred to as adaptive dose-escalation and adaptive dose-response designs, respectively.

When carefully planned and used appropriately, theymay bemore efficient than traditional

designs in determining the target dose given limited budget.

C5: PATIENT-REPORTEDOUTCOMES

...

Title: Patient-ReportedOutcomes: Measurement, Implementation and

Interpretation


Instructors: Joseph C. Cappelleri (Pfizer)



This short course will provide an exposition on healthmeasurement scales – specifically, on

patient-reported outcomes. Some key elements in the development of a patient-reported

outcome (PRO)measure will be noted. The core topics of validity and reliability of a PRO

measure will be discussed. Exploratory factor analysis and confirmatory factor analysis, mediation

modeling, item response theory, longitudinal analysis, andmissing data will among the topics

considered. Approaches to interpret PRO results will be elucidated in order tomake results

useful andmeaningful. Illustrations will be provided through real-life examples and also through

simulated examples using SAS.

C6: ANALYTICAL SIMILARITYASSESSMENT

...

Title: Analytical Similarity Assessment


Instructors: Shein-Chung Chow (Duke) and Yi Tsong (FDA)

For assessment of biosimilarity of biosimilar products, the United States (US) Food andDrug

Administration (FDA) proposed a stepwise approach for providing totality-of-the-evidence

of similarity between a proposed biosimilar product and a US-licensed (reference) product.

The stepwise approach starts with assessment of critical quality attributes that are relevant

to clinical outcomes in structural and functional characterization inmanufacturing process of

the proposed biosimilar product. FDA suggests that these critical quality relevant attributes

be identified and classify into three tiers depending their criticality or risking ranking. To

assist the sponsors, FDA also suggests some statistical approaches for assessment of analytical

similarity for critical quality attributes (CQAs) from different tiers, namely equivalence test

for Tier 1, quality range approach for Tier 2, and descriptive raw data and graphical comparison

for Tier 3. In this short course, wewill give an overview of the equivalence tests in terms of

bioequivalence and biosimilarity and therapeutically equivalence and focus on analytical

similarity assessment for identified CQAs at various stages of manufacturing process of the

proposed biosimilar product. In addition, challenging issues such as (i) sample size determination

for reference and test lots required for a valid and reliable equivalence test, (ii) fixed criterion

versus random criterion approach, (iii) alternativemethods to the FDA’s recommended tiered

approaches (mainly for CQAs from Tier 1 and Tier 2) are discussed.



SYMPOSIUM - SEPTEMBER 15, 2016

Time Room Events

8:00-9:00 Foyer (3rd) Registration & Light Breakfast

9:00-9:30 Greenbriar Ballroom (2nd) Welcome

Elizabeth DeLong (Duke B&B)

Opening Remarks

Eric Peterson (DCRI)

Terry Sosa (Quintiles)

James Love (Boehringer-Ingelheim)

9:30-10:30 Greenbriar Ballroom (2nd) Keynote Address:

PrecisionMedicine Initiatives at FDA

Lisa LaVange (FDA)

10:30-10:45 Foyer (3rd) Coffee Break

10:45-12:00 Brightleaf E (3rd) Parallel Session S1A

Basket Designs in Oncology Trials

Brightleaf F (3rd) Parallel Session S1B

Multi-Regional Clinical Trials (MRCTs)

Brightleaf G (3rd) Parallel Session S1C

Methods in Cancer Pharmacogenomics

12:00-1:30 Foyer (3rd) Lunch and Poster Session


Adaptive Designs for Clinical Trials


Collaboration to Accelerate Drug Development


Methods for Electronic Health Records

2:45-3:00 Foyer (3rd) Coffee Break


Application of Genetic Information in Trials


Current Issues in Cancer Phase II Trials


Discovery Science for Immunotherapy Trials

5:00-7:00 Greenbriar (2nd) Social Mixer and Poster Session

...

Poster Session will be on Sep. 15, 12:00-1:30 and 5:00-7:00 in Greenbriar (2nd) Foyer.

Social Mixer will be on Sep. 15, 5:00-7:00 in Greenbriar Ballroom (2nd).



PARALLEL SESSIONS - SEPTEMBER 15, 10:45-12:00

...

S1A: Opportunities and Challenges with Basket Designs in Oncology Trials

• Organizers: Marlina Nasution (PAREXEL), XiaofeiWang (Duke)

• Speakers: Richard Simon (NCI), Amanda Redig (DFCI)

• Discussant: Daniel Sargent (Mayo)

S1B: Key Consideration onMulti-Regional Clinical Trials (MRCTs)

• Organizers: Qi Jiang (Amgen)

• Speakers: Steven Snapinn (Amgen), Gordon Lan (JnJ), Bruce Binkowitz (Merck)

S1C: Principles andMethods in Cancer Pharmacogenomics

• Organizers: Kouros Owzar (Duke)

• Speakers: Jichun Xie (Duke), Raluca Gordan (Duke), Federico Innocenti (UNC)




...

S2A: Recent Advances in Adaptive Designs for Clinical Trials


• Speakers: JingWang (Gilead), Qi Jiang (Amgen), Yeh-Fong Chen (FDA)

S2B: Collaboration to Accelerate Development of EffectiveOncologyMedications

• Organizers: Marlina Nasution (PAREXEL), XiaofeiWang (Duke)

• Speakers: Rajeshwari Sridhara (FDA), SharonMurray (PAREXEL), Daniel Sargent

(Mayo)

S2C:Methods and Applications for Electronic Health Records

• Organizers: Terry Sosa (Quintiles), John Bauman (Quintiles)

• Speakers: Walter Boyle (Sutherland Healthcare), Joseph Lucas (Duke), Ben

Goldstein (Duke)




...

S3A: Application of Genetic Information in Oncology Clinical Trial Design

• Organizers: Renè Kubiak (Boehringer-Ingelheim), Marlina Nasution (PAREXEL)

• Speakers: Suzanne Dahlberg (DFCI), SumithraMandrekar (Mayo), Yijing Shen

(Genentech)

S3B: Current Issues in Cancer Phase II Trials

• Organizers: Terry Sosa (Quintiles), XiaofeiWang (Duke)

• Speakers: Sin-Ho Jung (Duke), Ilya Lipkovich (Quintiles), Kevin Liu (JnJ)

• Discussant: Gary Koch (UNC)

S3C: Discovery Science for Immunotherapy Trials

• Organizers: Terry Sosa (Quintiles), Cliburn Chan (Duke)

• Speakers: KentWeinhold (Duke), Eric Groves (Quintiles), Lynn Lin (Penn State),

Radleigh Santos (TPIMS)



SYMPOSIUM - SEPTEMBER 16, 2016

Time Room Events

8:00-9:00 Foyer (3rd) Registration & Light Breakfast


Addendum to Statistical Principles for Clinical Trials (ICH E9)


Causal Inference in Cancer Clinical Research


NewDevelopments in Survival Analysis for Cancer Research

Greenbriar A (2nd) Parallel Session S4D

Opportunities and Challenges in Design and Analysis

of Immunotherapies Trials

10:15-10:30 Coffee Break


Safety and Benefit Risk Analysis in Drug Development


Statistical Issues Related to Progression-free Survival

andOverall Survival


Current Issues in Biosimilar Studies

Greenbriar A (2nd) Parallel Session S5D

Exposure ResponseModeling in the Pharmaceutical Industry




...

S4A: Addendum to Statistical Principles for Clinical Trials (ICH E9): Choosing

Appropriate Estimands andDefining Sensitivity Analyses in Clinical Trials

• Organizers: Terry Sosa (Quintiles), John Bauman (Quintiles)

• Speakers: CraigMallinckrodt (Eli Lilly), Bohdana Ratitch (Quintiles), Devan

Mehrotra (Merck)

S4B: Causal Inference in Cancer Clinical Research

• Organizers: XiaofeiWang (Duke), Terry Hyslop (Duke)

• Speakers: Donglin Zeng UNC), XiaofeiWang (Duke), Jeremy Taylor (UMICH)

S4C: NewDevelopments in Survival Analysis for Cancer Research

• Organizers: YuanWu (Duke)

• Speakers: Danyu Lin (UNC), Butch Tsiatis (NCSU), Jason Fine (UNC)

S4D: Opportunities and Challenges in the Design and Analysis of Immunotherapies

Trials

• Organizers: Susan Halabi (Duke)

• Speakers: Kay Tatsuoka (BMS), Susan Halabi (Duke), PralayMukhopadhyay

(AstraZeneca)




...

S5A: Safety and Benefit Risk Analysis in Drug Development


• Speakers: Frank Rockhold (Duke), Chunlei Ke (Amgen), OlgaMarchenko

(Quintiles)

S5B: Statistical Issues Related to Progression-free Survival andOverall Survival

• Organizers: Renè Kubiak (Boehringer-Ingelheim), XiaofeiWang (Duke)

• Speakers: Jim Love (Boehringer-Ingelheim), Richard Cook (Waterloo), Terry

Therneau (Mayo)

• Discussant: Stephen George (Duke)

S5C: Current Issues in Biosimilar Studies

• Organizers: Shein-Chung Chow (Duke),Yi Tsong (FDA)

• Speakers: XiaoyuDong (FDA), Meiyu Shen (FDA), Aili Cheng (Pifzer)

S5D: Exposure ResponseModeling in the Pharmaceutical Industry

• Organizers: Dalong Huang (FDA)

• Speakers: Yaming Hang (Biogen), BretMusser (Merck), Dalong Huang (FDA)



PARALLEL SESSIONS - ABSTRACTS

S1A: OPPORTUNITIES ANDCHALLENGESWITHBASKETDESIGNS

INONCOLOGYTRIALS

Richard Simon (NCI) – A Bayesian Design for Basket Clinical Trials

Amajor focus of oncology drug development involves use of tumor genomics to guide the use

of molecularly targeted drugs. In some cases the anti-tumor effect of a drug is mediated by its

effect on a de-regulatedmolecular target whose role in the pathophysiology of the tumor is

well understood. In those cases the development of the drug and a companion diagnostic in

a histologic type of cancer is relatively straightforward. Activity of a drug against tumors of

a histologic type bearing a genomic alteration does not always, however, imply that the drug

will be active against tumors of other histologic types bearing the same alteration. Also, even

for a single histologic type, theremay bemultiple alterations in the same pathway (or gene)

of interest and performing a separate clinical trial for each alteration is generally not feasible.

These uncertainties must generally be resolved in earlier phase clinical trials. For this reason,

a new type of early phase clinical trial has arisen, the “basket trial”. The basket trial represents

an early phase II discovery trial in which patients with defined genomic alterations but multiple

histologic types of tumors are selected to discover in which histologic types of tumors the targeted

drug is active. If the selection includes a variety of types of genomic alterations or a variety of

mutated genes, the basket trial may also be designed to determine which alterations in which

genes sensitize the tumor to the drug. Basket trials are discovery trials rather than hypothesis

testing trials; promising results of drug activity for a subset should be confirmed in amore focused

follow-up trial. Here I will describe a design for planning, monitoring and analyzing basket trials.

A website for using the new design is available at https://brpnci.shinyapps.io/BasketTrials/ and

the software is available at GitHub in the “Basket Trials” repository of account brbnci.

Amanda Redig (DFCI) – Precisionmedicine and the evolution of clinical trial design

Scientific advances of themodern era have begun to challenge earlier views of oncology in

which patients were treated with an exclusive focus on a tumor’s tissue of origin. The translation

of next-generation sequencing (NGS) into oncology practice has begun to demonstrate that

while a tumor’s primary site of origin matters, so too does its genetic landscape. However, despite

the tremendous promise of this new era of oncology, several challenges have emerged in the

translation of these new developments to the clinical trials arena. First, a genetic classification

and treatment strategymay not always follow the traditional boundaries of histopathology.

Yet how are such patients to be identified and directed towards appropriate clinical trials in

a way that provides clinically meaningful endpoints? Second, despite increasing recognition

of the importance of genomic analysis in oncology practice, evaluating targeted therapies can

present a formidable challenge when themutation(s) in question are rare and found across



disease types. Some eminently targetable mutationsmay be so rare they are only discovered

in the context of a negative trial. As our ability to probe the genome of an individual tumor

continues to expand, so toomust strategies for clinical trial design. Basket trials are a new and

evolving form of clinical trial design and are predicated on the hypothesis that the presence of

amolecular marker predicts response to a targeted therapy independent of tumor histology.

In many cases, a basket trial may actually contain several independent and parallel phase II

trials. However, the success of a basket trial depends in large part upon the strength of the data

linking target and targeted therapy. For this trial design to work, two key conditionsmust be

met: the tumormust depend upon the target pathway and the targeted therapymust reliably

inhibit the target. Several ongoing and recently published basket trials illustrate boEth the

strengths andweakness of this approach and provide insight into ways to improve trial design

while also serving as an important reminder that applying precisionmedicine sometimesmeans

looking for patient benefit when the n = 1.

S1B: KEY CONSIDERATIONONMULTI-REGIONAL CLINICAL TRIALS (MRCTS)

Steve Snapinn (Amgen) – Some Thoughts on Subgroup Analyses,With Emphasis on

Regional Subgroups inMultiregional Trials

Subgroup analyses have always been an important part of the analysis of nearly every clinical

trial. However, as long as they have been done they have faced sharp criticism, particularly

due to their relatively small sample size and their great number, leading to high rates of type I

error as well as type II error. One common approachwhen evaluating subgroup analyses is to

assume that the treatment effect is consistent across subgroups unless there is strong evidence

to the contrary, typically based on a significant treatment-by-subgroup interaction. However,

increasingly, many stakeholders find this approach to be fundamentally flawed. Notably, when

presentedwith the results of a multi-regional trial, many regional regulatory authorities want

direct evidence that the treatment is safe and effective in their region, rather than lack of evidence

that its efficacy varies across regions. In this presentation I will provide some thoughts on this

issue, touching on topics such as the goal of a subgroup analysis, the definition of region, and

proper role of an assessment of consistency.

Gordon Lan (JnJ) –MRCT designmodels and drop-min data analysis

In recent years, developing pharmaceutical products via amultiregional clinical trial (MRCT)

has becomemore popular. Many studies with proposals on design and evaluation ofMRCTs

under the assumption of a common treatment effect across regions have been reported in the

literature. However, heterogeneity among regions causes concern that the fixed effects model

for combining informationmay not be appropriate forMRCT. In this presentation, wewill discuss:

The use of the fixed effect model, the continuous random effect model, and the discrete random

effect model for the design and data analysis ofMRCTs. Numerical examples will be provided to

illustrate the fundamental differences among these threemodels. Consistency and inconsistency:



Wewill provide examples of inconsistency, and discuss the use of drop-the-min data analysis

when the region withminimum treatment effect is excluded from theMRCT data analysis. We

provide a solution first formulated within the fixed effects framework, and then extend it to

discrete random effects model.

Bruce Binkowitz (Merck) – Regions in PrecisionMedicine Clinical Trials – Complementary

or Superfluous?

Precisionmedicine is an approach to disease treatment and prevention that seeks tomaximize

effectiveness by taking into account individual variability in genes, environment, and lifestyle.

Multiregional clinical trials are an efficient way to examine disease treatments and prevention

across a diverse population including ethnic factors, culture, and other intrinsic and extrinsic

factors with region standing in as a surrogate for some combination of these factors. This presentation

will discuss the relationship between the concepts, and discuss the concept of region in a future

setting of precisionmedicine research.

S1C: PRINCIPLES ANDMETHODS IN CANCER PHARMACOGENOMICS

Jichun Xie (Duke) –Multiple Testing of General Dependence byQuantile-Based Contingency

Tables with an Application in Identifying Gene Co-expression Network Change Associated

with Cancer Survival

Gene co-expression networks describe the interactions among a set of regulators and other

substances in the cell to govern the gene expression levels of mRNA and proteins. One popular

way to estimate them is to infer the network between gene expression levels, which can be

formulated as a high dimensional network estimation/testing problem. The existingmethods

focus on networksmeasuring linear dependence or rank association, which cannot always represent

gene regulatorymechanisms. Without parametric assumptions on themarginal distributions

of continous random variables and their dependence structure, we propose a test statistic to

test whether the expression levels of two genes are independent. Based on the test statistic,

we further propose amultiple testing procedure that can simultaneously test independence

between all pairs of variables conditioning on other covariates. The numerical experiments

show that the performance of our method significantly outperform other methods when complex

dependence structures exist. Evenwhen non-linear or non-rank-associated dependence exists,

the proposedmethod performsmuch better in both validity and efficiency. Theoretically, we

prove the proposedmethod can control false discovery rate (FDR) under the desired level.

We apply this method on a gastric cancer data to investigate the change in gene co-expression

networks of patients with early or late prognostic stages, and show that ourmethod can identify

more changes that relate to the survival of the patients.

Raluca Gordan (Duke) – Assessing the effect of somatic mutations in non-coding regions

Recent whole-genome sequencing studies of paired normal-tumor samples revealed that the



majority of somatic mutations fall within non-coding genomic regions, where they have the

potential to affect regulation of gene expression. Here, we present a newmethod for assessing

the impact of non-codingmutations on the DNA-binding level of regulatory proteins called

transcription factors (TFs). TFs bind short DNA sites, typically in the neighborhood of the regulated

genes, and promote or repress gene expression. To quantitatively predict the effect of mutations

on TF-DNA binding, we use high-throughput in vitro data from protein-bindingmicroarray

(PBM) experiments for hundreds of mammalian TFs. Each PBMdata set is specific to one TF,

and it contains quantitativemeasurements of the biding specificity of that TF for 40,000 60-bp

DNA sequences. We use PBMdata to develop k-mer-based linear regressionmodels using

ordinary least squares (OLS).We use the estimated regression coefficients, as well as the variance-

covariancematrix, to compute: 1) a quantitative prediction of the change in TF binding due to

eachmutation, and 2) our confidence that the changewe predict is significant, given themodel

characteristics. Our approach is novel because, by using OLS and the variance in the coefficient

estimates, our predictions of the effects of mutations on TF-DNA binding implicitly take into

account the quality of the training data andmodel. As a result, in the case of poor data that

leads to poor predictivemodels, we require a larger change in TF binding level for amutation

to be called significant. To validate the quality of our predictions, we leverage high-throughput

enhancer assays where all possible single base-pair mutations in specific regulatory regions

have been tested directly for their effect on gene expression. We find that our TF-DNA binding

models can explain about 50% of the effect on gene expression caused by nucleotidemutations

in regulatory regions. Thus, we are confident that our newmethodwill be instrumental in prioritizing

non-coding somatic mutations for further computational and experimental analyses. This is a

joint work with Jingkang Zhao, Dongshunyi Li and AndrewAllen fromDuke.

Federico Innocenti (UNC) – Cancer treatment and use of germline genetics for determining

precision in drug therapy

The field of pharmacogenomics is focused on the characterization of genetic factors contributing

to the response of patients to pharmacological interventions. Drug response and toxicity are

complex traits; therefore the effects are likely due tomultiple genes. The investigation of the

genetic basis of drug response has evolved from a focus on single genes to relevant pathways

to the entire genome. The scope of this talk is to provide the current status of germline genomic

studies in cancer patients treated with chemotherapy, themethods for discovery and implications

for patient care. Large clinical trials enrichedwith genome-wide association studies (GWAS)

provide an unprecedented opportunity for a comprehensive and unbiased assessment of the

heritable factors associated with drug response. In oncology, germline genomics is still relatively

unexplored, particularly in reference to biomarkers of patient survival. In this presentation,

results fromGWAS efforts in oncology and the use of somatic information from the cancer

genome are presented, within the large context of achieving precision in treatment. The focus

of the GWAS discoveries in relation to overall survival will be on VDR and gemcitabine in pancreatic



cancer, as well as AXIN1 and colorectal cancer patients treated with combination of chemotherapy

and targeted therapies. Moreover, prospective validation for dose assignment will be also presented

for irinotecan based upon UGT1A1 genetics, based upon results if phase I trials where dosing

has been individualized on the basis of the UGT1A1*28 allele. Challenges and opportunities in

translating heritable genomic discoveries to patients are also discussed.

S2A: RECENTADVANCES INADAPTIVEDESIGNS FORCLINICAL TRIALS

JingWang (Gilead) – Biomarker-driven adaptive designs for precisionmedicine

Precisionmedicine has paved the way for a new era of delivering tailored treatment options to

patients according to their biological profiles. In combination with innovative adaptive design,

this has presented drug developers unprecedented opportunities to engage novel thinking to

accelerate drug discovery. Adaptive design options such as: adaptive accrual design, adaptive

threshold design, adaptive signature design, and cross-validated adaptive signature design will

be discussed. Related statistical theories and analysis strategies will also be coveredwith case

studies.

Qi Jiang (Amgen) – Key Statistical Consideration on PlatformDesign

A draft adaptive design guidance was released by FDA in 2010, and utilization of adaptive designs

in drug development has been increasing. Still, even greater use of adaptive designs is well

needed, especially in terms of less well-understood andmore complicated adaptations. In this

presentation we’ll present key statistical issues and consideration on platform design. This is a

joint work with Dr Chunlei Ke (Amgen).

Yeh-Fong Chen (FDA) – Development of GeneralMethodology for NASH Studies

Utilizing the Seamless Adaptive Design

The prevalence of non-alcoholic fatty liver disease including non-alcoholic steatohepatitis (NASH)

is increasing worldwide. NASH is the secondmost common indication for liver transplantation

and is expected to be the leading indication by 2020. In light of the increasing prevalence and

burden of disease, it is imperative to develop therapeutic strategies for patients with NASH.

Although it is feasible to conduct clinical trials with liver transplantation or death as the clinical

outcome endpoint, it may take 10 to 20 years for NASH patients to develop cirrhosis or other

liver-relatedmorbidity andmortality. In addition, evenwith the accelerated regulatory approval

to use surrogate endpoints, a study needs to be at least one year long to detect clinically meaningful

effectiveness of a study drug. Seamless adaptive clinical designs by rolling over patients from

phase 3 to phase 4 can be adopted to shorten the duration of a new drug development program

for NASH. In this presentation, wewill focus on the statistical considerations and the application

of the seamless design for NASH and hopefully stimulate broader discussions about the advantages

and drawbacks of other innovative designs. This is a joint work with Dr Shein-chung Chow

(Duke).



S2B: COLLABORATIONTOACCELERATEDEVELOPMENTOF EFFECTIVE

ONCOLOGYMEDICATIONS

Rajeshwari Sridhara (FDA) – Regulatory collaborations with academia, industry and

international regulatory agencies

FDA collaborates with external stakeholders onmultiple issues to promote science based drug

development. In many of these collaborations FDA and in particular CDER statisticians play

an important role. This presentation will include examples of collaborations between CDER

statisticians and external stakeholders including academia and pharmaceutical/biotech industry

focusing on oncology/hematology drug development.

SharonMurray (PAREXEL) –Working Together ToOperationalize Complicated Phase

I/II Studies: An Innovative Collaboration between Sponsor and CRO

PAREXEL is working collaboratively with various pharmaceutical companies on a number of

Phase I/II studies with complex study designs andmultiple data reviews during the course of the

study. These range from first-time-in-human trials to first-time-in-combination trials or phase

IIb trials withmultiple interims. Studies may havemore than one decision gate or study part,

including for example a dose selection phase, a verification phase to confirm the selected dose,

and a third phase where subjects are randomized to the experimental therapy or a comparator.

Data reviews are required at each decision gate, prior tomoving to the next phase. This talk

will describe the responsibilities of the sponsor and the CRO (PAREXEL) prior to study start-up,

during study conduct, and at the analysis stage using one of the studies as an example. Suggestions

will be provided for recommendedways of working to ensure successful delivery.

Daniel Sargent (Mayo) – Research Collaboration in Oncology: Academic perspectives

Success in today’s clinical research environment requires multiple levels of collaboration, between

different disciplines, institutions, and funding sources. In this talk I will present experiences

and perspectives on oncology clinical trials collaboration from an academic perspective, which

must satisfy multiple stakeholders including patients, funders, the scientific community, and

regulatory agencies. Issues that will be highlighted are the ever-evolving academic/industry

relationship, the growing role of large, collaborative academic-based (but often industry funded)

groups, and the rapidly growing emphasis on data sharing of individual patient data from completed

clinical trials.

S2C:METHODSANDAPPLICATIONS FOR ELECTRONICHEALTHRECORDS

Walter Boyle (SutherlandHealthcare) - The Promise and Pitfalls of ElectronicMedical

Records

To researchers and analysts in the healthcare field, the ElectronicMedical Record (EMR) is one

of themost exciting sources of data available. At first glance an EMRwould seem to have the



potential to give an almost holistic representation of a patient’s health. However, that potential

comes hand in handwith a variety of pitfalls which can stop a research project before it even

gets started. This presentation will use a previous research project rooted in EMR data to discuss

some of these issues and help you to understandwhy they happen and to even potentially overcome

them.

Joseph Lucas (Duke) - Tracking the Effects of Healthcare Innovations through the use

of electronic health records

Changes in incentives and business models are leading health systems to rapidly innovatemany

aspects of care. However, the effects of those innovations are often not being tracked. In scientific

terms, this is like running an experiment without paying any attention to the outcome. Without

understanding the effect of healthcare innovation on both patient health and efficiency, we

lose the opportunity to learn from the changes that are implemented. Changes in healthcare

delivery should be treated as a full-fledged experiment; a hypothesis should be generated,

tools for measuring the outcome should be developed and the implementation of the changes

should be conducted in a way to allow testing of the hypothesis. In this talk wewill discuss some

tools we have developed for ongoing tracking of healthcare innovations. Our approach uses a

Bayesian framework to continuously update our understanding of the effects of the innovation

and presents those results in results in a context that is useful for supporting decisionmaking by

health system administrators.

BenGoldstein (Duke) - Informed Presence in the Analysis of Electronic Health Records

An increasingly popular data source for clinical analyses are Electronic Health Records (EHRs).

They are often readily available and contain detailed information on large amounts of patients.

Owing to this density of information problems of unmeasured covariates is often a secondary

concern. Instead, a challenge in the analysis EHRs is a form of selection bias: Informed Presence.

As others have noted patients in EHRs are sicker than the general population. Moreover, since

individuals are only observedwhen they have amedical encounter, i.e. sick, there is high potential

for selection effects and bias in association studies. In this talk wewill discuss different forms

of informed presence and illustrate how they can bias typical analyses. Wewill also discuss

different means of addressing these biases.

S3A: APPLICATIONOFGENETIC INFORMATION INONCOLOGYCLINICAL

TRIALDESIGN

SuzanneDahlberg (DFCI) - Incorporation of Genetic Information in Clinical Trials

The discovery of driver oncogenes has dramatically impacted clinical research, placing great

focus on the biological causes of dramatic responses to therapy among populations that do not

derive treatment benefit on average. The corresponding successful development of therapies

targeting those genetic abnormalities has prompted us to investigate tumor biology routinely as



integral or integrated biomarkers in the trial setting. How to incorporate the science statistically

is not a one-size-fits-all approach, particularly as the field rapidly transitions its focus to the

development of immune checkpoint inhibitors; study design depends in part on the phase of

the trial, the prevalence and strength of evidence of the biomarker, preferred endpoints, and

the assay or test used to detect it. Using examples from the oncology literature, I will discuss

how each of these considerations impacts the design and conduct of a clinical trial.

SumithraMandrekar (Mayo) - Clinical Trial Designs in the Era of PrecisionOncology

Clinical trial design strategies have evolved as ameans to accelerate the drug development

process so that the right therapies can be delivered to the right patients. With these changes

in the science of oncology have come changes to the waywe design and perform clinical trials.

Increasingly common are trials tailored to detect enhanced efficacy in a patient subpopulation,

e.g., patients with a known biomarker value or whose tumors harbor a specific genetic mutation.

Earlier biomarker-based designs typically assessed a single targeted therapy in a single disease

typewith 1 or 2molecular groups. These include enrichment, marker-stratified, andmarker

strategy designs. Newer biomarker-based designs expand on the earlier ones by includingmultiple

targeted therapies, multiple disease types, and/or multiple molecular groups. These include

modified strategy designs, umbrella trials, Bayesian biomarker-adaptive designs, and basket

trials. In this talk, I will discuss a number of trials that are examples of these biomarker-based

designs either in a proof of concept early phase setting or in amore definitive Phase III setting.

These include the National Cancer Institute’s precisionmedicine initiative trials such as the

ALCHEMIST, and LungMAP, as well as other trials such as SHIVA,Matrix, the FrenchNational

Cancer Institute’s AcSé (Secured Access to Innovative Therapies) program, and ASCO’s Targeted

Agent and Profiling Utilization Registry trial.

Yijing Shen (Genentech) –Operational Adaptive Biomarker Development Strategy

for the AtezolizumabNSCLC Program

Identifying accurate diagnostic cutoffs and suitable endpoints to use in pivotal oncology studies

is often challenging, especially in the immunotherapy setting where the early efficacy endpoints

such as ORR and PFSmay not be strongly associated with OS. In addition, delayed treatment

effect in this setting requires larger studies and longer follow up to estimate the treatment

benefit to inform pivotal study designs. This abstract describes the clinical development strategy

for the second/third line AtezolizumabNSCLC program conducted by Genentech/Roche. Atezolizumab

is a humanized anti-PDL1 antibody that inhibits the binding of PD-L1 to PD-1 and B7.1. A Phase

Ia trial showed single-agent activity in NSCLC patients with the objective response rate (ORR)

associated with PD-L1 expression on tumor-infiltrating immune cells (IC) and/or tumor cells

(TC). After atezolizumab demonstrated single agent activity with differential ORR by PD-L1

expression level, the atezolizumab clinical and biomarker development plan was designedwith

limited atezolizumab randomized data and from in-class agents whichmade evaluation of efficacy

endpoints and optimal PD-L1 expression cutoff challenging, particularly in the context of accelerated



drug development. Four parallel 2L+NSCLC studies were designed to provide dynamic information

regarding the optimal diagnostic tissue type, cutoffs, and endpoints. Two of these were phase 2

trials (one PD-L1 selected single arm and one all comer randomized) designed to better estimate

the treatment benefit and inform potential changes of the other two registrational study designs

(one PD-L1 selected single arm and one all comer randomized). This operational adaptive strategy

provided flexibilities and helped refine the final registrational trial designs. In the presentation,

strategic context, design details, andmodification scenarios of the 2L+NSCLCAtezolizumab

studies will be discussed. This is a joint work with Zhengrong Li, Pei He and Jing Yi.

S3B: CURRENT ISSUES IN CANCER PHASE II TRIALS

Sin-Ho Jung (Duke) – Statistical Issues for Design and Analysis of Phase II Cancer

Clinical Trials

Phase II trials have been very widely conducted and published every year for cancer clinical

research. In spite of the fast progress in design and analysis methods, single-arm two-stage

design is still themost popular for phase II cancer clinical trials. Because of their small sample

sizes, statistical methods based on large sample approximation are not appropriate for design

and analysis of phase II trials. As a prospective clinical research, the analysis method of a phase

II trial is predetermined at the design stage and it is analyzed during and at the end of the trial

as planned by the design. The analysis method of a trial should bematchedwith the design

method. For two-stage single arm phase II trials, Simon’s method has been the standards for

choosing an optimal design, but the resulting data have been analyzed and published ignoring

the two-stage design aspect with small sample sizes. In this talk, I review analysis methods

that exactly get along with the exact two-stage designmethod. I also discuss some statistical

methods to improve the existing design and analysis methods for single-arm two-stage phase II

trials.

Ilya Lipkovich (Quintiles) – Biomarker-driven seamless phase II/III trials for a rare

disease

In this presentation we consider a seamless Phase II/III design that was recently used in a clinical

trial in patients withmesothelioma, a rare cancer found in the lining surrounding the lungs and

other organs. The new experimental treatment was compared to placebo using an adaptive

biomarker-driven design. After the first stage of the seamless trial (end of Phase II) has been

completed, data are evaluated and themost promising biomarker that helps predict treatment

response is identified using the SIDESmethodology (Lipkovich et al, 2011 and Lipkovich and

Dmitrienko, 2014). Based on predictive power evaluated after the first stage, a decision is made

whether to terminate the trial for futility, continue the trial without any changes in the overall

patient population, adjust the sample size (target number of events) in the same population, or

focus on a subset of the overall population based on the selected biomarker (biomarker -positive

subpopulation), possibly in combination with testing the overall effect via an appropriate multiple



testing procedure. We present the results of a simulation study that evaluates key operating

characteristics of this design under different scenarios. A joint work with Alex Dmitrienko (Mediana).

Kevin Liu (JnJ) – Biomarker Enrichment Design for Early PhaseOncology Studies

Oncology drug development has been increasingly shaped bymolecularly targeted agents

(MTAs), which often demonstrate differential effectiveness driven by the expression profile

of their molecular targets in tumors. Innovative statistical designs have been proposed to tackle

this new challenge, e.g., Freidlin (2005), Jiang (2007) and Freidlin (2010). All of these are essentially

adaptive confirmatory Phase 3 designs that combine the testing of treatment effectiveness in

the overall population with a possible pathway to identify a potential sensitive subpopulation.

We argue that, in cases that there are strong biological rationale and preclinical evidence to

support that aMTAmay provide differential benefit a general patient population, it is imperative

that early phase POC studies be designed to specifically address the biomarker-related questions,

e.g., subgroup selection, biomarker threshold evaluation, in order to improve the efficiency of

development. In this presentation, wewill discuss statistical enrichment strategies for Phase

2 oncology designs, both in single-arm and in randomized settings. Also to be discussed is the

likely challenge of the lack of a reliable assay in the earlier development phases. To this extent,

wewill discuss the impact of measurement error on the operation characteristics of these designs,

which are evaluated through simulations. This is a joint work with Dr Hong Tian from JnJ.

S3C: DISCOVERY SCIENCE FOR IMMUNOTHERAPY TRIALS

KentWeinhold (Duke) – The Immunologic Basis for Current Cancer Immunotherapies

Despite concerted efforts over the past 30+ years, it was not until very recently that immunotherapeutic

approaches against cancer began to showmore than just rare anecdotal successes. Much of the

current success is the result of technological advances that have re-shaped our understanding

of the patient’s immune response to their actively growing tumor. This, coupled with the development

of novel biological treatmentmodalities, has brought about a revolution in cancer immunotherapy

that has, in certain instances, resulted in response rates exceeding 30-40%. The challenges

brought about by this ‘paradigm shift’ are now centered on: 1) the identification of baseline

‘pharmacodynamicmarkers’ that predict which patients would benefit from a specific immunotherapeutic

approach, and 2) the profiling of immunologic components within both the peripheral circulation

and the tumormicroenvironment in search of specific biomarkers that track with clinical efficacy.

This introductory overview presentation will focus on the process of T cell activation, maturation,

exhaustion, and regulation as well as effector T cell responses to highly conserved tumor associated

antigens (TAA) and neoantigens generated by somatic mutations. Finally, the concept of ‘hot’

versus ‘cold’ tumormicroenvironments and their impact on specific immunotherapeutic strategies

will be discussed.



Eric Groves (Quintiles) – Immunotherapies: Opportunities and Challenges

Until recently therapies that manipulate the immune system to achieve anti-cancer benefits

have had limited success. Coley’s toxin, IL-2, Interferon, various vaccines, all have produced

tantalizing but infrequent results. But with the advent of CTLA-4, PD-1 and PD-L1 inhibitor

therapies, dramatic results have been observedwith evidence of long term benefit. This discussion

will focus on a brief historic review, present some current data and then discuss the opportunities

and challenges that future development of these and additional novel immune therapies may

offer.

Lynn Lin (Penn State) – Characterizing Antigen-specific T-cell Functional Diversity in

Single-cell Expression Data

Rapid advances in flow cytometry and other single-cell technologies have enabled high-dimensional

measurement of individual cells in a high-throughput manner so that many new and long-standing

questions about cell population heterogeneity can now be addressed. One specific hypothesis

is that some characteristic or quality of a subset of antigen-specific T cells involved in immune

function (in particular, “olyfunctional T-cells” which are capable of simultaneously producing

multiple effector cytokines) is associated with protective immunity from infectious diseases.

During this talk, I will present a novel statistical framework for unbiased polyfunctionality analysis

of antigen-specific T-cell subsets defined from high-dimensional single-cell assays and demonstrate

how it can be used to comprehensively unravel rare signals associated with vaccine efficacy

frommultiple clinical datasets that would bemissed by traditional analyses.

Radleigh Santos (TPIMS) – Using Fusion of ResponseMetrics andMonte Carlo Simulation

to Determine Immune Response in Cancer Immunotherapy Patients

Determining immune responders in the post-treatment clinical context of cancer immunotherapy,

in which patients are treated with one ormore antigens for the purpose of eliciting an immune

response against the cancer can be challenging. In general, the effectiveness of threshold-based

criteria, such as spot count difference from control in ELISpot, can vary widely, depending on

patient population latent response and also on experimental choices that increase background

variation such as IVS testing. On the other hand, inferential statistical tests such asmDFR or the

binomial test can be impacted by varying numbers of samples per patient and also by general

differences in patient population distribution of response. Furthermore, measuring differences

between pre- and post-treatment response using either a direct statistical test, or a difference

of some kind between independently determined pre- and post-treatment response are options

when determining immune responders. The end result is that no single approach is applicable in

all cases; this, in turn, can lead to the data itself dictating the definition of immune responder, a

non-objective process that is difficult to apply broadly. In this presentation, a novel heuristic for

determining immune responders usingmultiple metrics combined in a fusion scoring approach

will be shown. Monte Carlo simulation is then used to put these fusion scores into a clear context

for the purpose of assigning responder status to individual patient samples, and hence to each



patient . A specific implementation of this approachwill be shown using data from a recent

phase 2 glioblastoma immunotherapy trial (ICT-107) in which HLA-A2 patients were treated

with six synthetic peptides. Patient samples were tested for immune response using both ELISpot

andMultimer, and it will be shown how themethodwas used for both types of data. Promising

associations between responders designated in this manner and survival endpoints suggest that

this method of designating patients as immune responders captures some of the underlying

mechanism of action of this treatment.

S4A: ADDENDUMTOSTATISTICAL PRINCIPLES FORCLINICAL TRIALS (ICH

E9): CHOOSINGAPPROPRIATE ESTIMANDSANDDEFINING SENSITIVITY

ANALYSES IN CLINICAL TRIALS

CraigMallinckrodt (Lilly) - Overview of Estimands, Estimators, and Sensitivity for

Longitudinal Clinical Trials

Recent research has fostered new guidance on preventing and treatingmissing data. Consensus

exists that clear objectives should be defined along with the causal estimands; trial design and

conduct shouldmaximize adherence to the protocol specified interventions; and, a sensible

primary analysis should be used along with plausible sensitivity analyses. Two general categories

of estimands are: effects of the drug as actually taken (de-facto, effectiveness) and effects of the

drug if taken as directed (de-jure, efficacy). Fundamental, design, and analysis considerations for

common estimands will be discussed. Examples are used to illustrate the benefit from assessing

multiple estimands in the same study. General approaches to sensitivity analyses will be introduced

and subsequent speakers in this session will elaborate on specific approaches.

Bohdana Ratitch (Quintiles) - Imputation-based Analysis Strategies in the Presence of

Treatment Non-Adherence

A definition of estimand for a clinical trial may take into account how non-adherence to randomized

treatment, e.g., early discontinuation or initiation of rescue therapy, would be accounted for in

the estimate of treatment effect, in alignment with study objectives. Ideally, this should lead to

a design of a clinical study where all data relevant for the defined estimand can be collected for

all subjects. In practice, however, some amount of missing data can be expected inmost clinical

trials andwithmost estimands, due to subjects missing planned study visits or withdrawing

from the study prematurely. In some cases, for ethical reasons, it may also be impossible to

obtain usable (non-confounded) data for a specific estimand for some subjects during the periods

of non-adherence. In case of missing data or data unusable for a given estimand, analysis methods

have to rely on some strategies of handling unusable or unavailable outcomes in amanner that

is consistent with the planned estimand. Wewill review several analysis strategies that are

based on subject-level imputation usingmultiple imputationmethodology. Wewill focus on

several variants of reference-based and delta-adjustment approaches and discuss the type of



assumptions about the outcomes of non-adhering subjects that can be implemented using these

strategies.

DevanMehrotra (Merck) - Clinical Trials with Dropouts: Proposed Estimand-Aligned

Primary and Sensitivity Analyses

In a typical randomized clinical trial comparing two treatments (test, control), the endpoint of

interest (e.g., change from baseline in HAMD-17 at 6 weeks) is not observed for dropouts. The

resultingmissing data problem is commonly tackled by invoking amissing at random (MAR)

assumption and proceeding with amixedmodel repeatedmeasures (MMRM) analysis. In some

settings, theMAR assumptionmay be reasonable for the control treatment (often placebo)

but not for the test treatment (experimental drug). In such cases, theMMRM-based estimated

between-treatment difference in endpoint means tends to be biased for the estimand of interest.

We propose a simple alternative approach in which the implicitly imputedmean for test-arm

dropouts in theMMRManalysis is explicitly replacedwith either the estimatedmean for all

control-arm patients (primary analysis) or the estimatedmean for control-arm dropouts only

(sensitivity analysis); patient-level imputation is not required. An additional sensitivity analysis

is proposed in which a common “bad” outcome is imputed for all the test and control arm dropouts

followed by a between-treatment comparison of trimmedmeans using quantile regression. All

analyses address the same estimand. A real dataset and simulations are used to support the key

messages.

S4B: CAUSAL INFERENCE IN CANCERCLINICAL RESEARCH

Donglin Zeng (UNC) – Estimating Treatment Effects with Treatment Switching via

Semi-Competing RisksModels: An Application to a Colorectal Cancer Study

Treatment switching is a frequent occurrence in clinical trials, where, during the course of the

trial, patients who fail on the control treatmentmay change to the experimental treatment.

Treatment switching creates statistical challenges for estimating the causal effect of the treatment.

Analyzing the data without accounting for switching yields highly biased and inefficient estimates

of the treatment effect. In this talk, in order to accurately assess the treatment effect, we propose

a novel class of semiparametric semi-competing risks transition survival models to accommodate

switch. Theoretical properties of the proposedmodel are examined and an efficient expectation-

maximization algorithm is derived for obtaining themaximum likelihood estimates. Simulation

studies are conducted to demonstrate the superiority of themodel compared to the intent-to-treat

analysis and other methods proposed in the literature. The proposedmethod is applied to analyze

data from the panitumumab study.

XiaofeiWang (Duke) – Bias-adjusted Kaplan-Meier Survival Curves forMarginal

Treatment Effect in Observational Studies

For time-to-event outcome of multiple treatment groups, the Kaplan-Meier estimator is often



used to estimate survival functions of treatment groups and computemarginal treatment effects,

such as difference of survival rates between treatments at a landmark time. The Kaplan-Meier

estimates and the derived estimates of marginal treatment effect are uniformly consistent

under general conditions when data are from randomized clinical trials. For data from observational

studies, these statistical quantities are often biased due to treatment-selection bias. Propensity

score basedmethods, such as the inverse probability of treatment weighting, estimate the

survival function by adjusting for the disparity of propensity scores between treatment groups.

Unfortunately, themisspecification of the regressionmodel will lead to biased estimates in

these existingmethods. Using an empirical likelihood (EL) method in which themoments of the

covariate distribution of treatment groups are constrained to equality, we obtain consistent

estimates of the survival functions and themarginal treatment effect through themodified

Kalpan-Meier estimator. Equatingmoments of the covariates distribution between treatment

simulates the covariate distribution if the patients had been randomized to these treatment

groups. We established the consistency and the asymptotic limiting distribution of the proposed

EL estimators. We demonstrated that unlike propensity scoremethods, the consistency of the

proposed estimator does not depend on a correct specification of amodel. Covariates subsets

on bias control are also discussed. Simulation was used to study the finite sample properties

of the proposed estimator and compare it with existingmethods. The proposed estimator is

illustrated with observational data from a lung cancer observational study to compare two

surgical procedures in treating early stage lung cancer patients.

Jeremy Taylor (UMICH) – Estimating the causal effect of salvage hormone therapy in

prostate cancer

Causal effects of interventions can be assessed by considering what the subject’s outcome

would have been if they had taken the intervention and compare that with what it would have

been had they not taken the intervention. After initial treatment for localized prostate cancer

patients monitor their PSA values, and a rise in PSA suggests that the cancer may be regrowing

and it would bewise to start a new therapy to prevent or delay the occurrence of clinical symptoms.

Androgen deprivation (hormone) therapy can delay the recurrence of prostate cancer, however

it has some side effects. In deciding whether to start hormone therapy some important considerations

are; what is the chance of recurrence in the next few years and how effective will hormone

therapy be at reducing that. A complication to estimating the effect of hormone therapy is that

it is given by indication, i.e. those with rising PSA levels and at greater risk of recurrence tend to

have received it. Using data from patients initially treated with radiation therapy, in this talk I

will present and compare three approaches to estimating the effect of hormone therapy. One

based on joint longitudinal and survival models, one based on sequential stratification and one

based onmarginal structural models. A second important question is to determine the optimal

treatment regime for when to start salvage hormone therapy for a population to follow. We

consider a class of regimes in which hormone therapy is first givenwhen PSA is rising and it first



crosses a threshold b. For each regime, from the observational data, wemodel the probability

of adherence to that regime, using random forests to estimate this probability and use inverse

probability weighting to estimate the expected survival curve under that regime, and define the

optimal regime as the onewith the largest mean restricted survival distribution.

S4C: NEWDEVELOPMENTS IN SURVIVALANALYSIS FORCANCERRESEARCH

Danyu Lin (UNC) –Maximum Likelihood Estimation for Semiparametric Regression

Models with Interval-Censored Data

Interval censoring arises frequently in clinical and epidemiological studies, where the event

or failure of interest is not observed at an exact time but is rather known to occur within an

interval induced by periodic monitoring. We formulate the effects of potentially time-dependent

covariates on the interval-censored failure time through semiparametric regressionmodels,

such as the Cox proportional hazardsmodel. We study nonparametric maximum likelihood

estimation with an arbitrary number of monitoring times for each subject. We devise an EM

algorithm that converges stably for arbitrary datasets. We then show that the estimators for

the regression parameters are consistent, asymptotically normal, and asymptotically efficient

with an easily estimated covariancematrix. In addition, we extend the EM algorithm and asymptotic

theory to competing risks andmultivariate failure time data. Finally, we demonstrate the desirable

performance of the proposed numerical and inferential procedures through extensive simulation

studies and applications to real medical studies.

Butch Tsiatis (NCSU) – Inference on treatment effects from a randomized clinical trial

in the presence of premature treatment discontinuation: The SYNERGY trial

The SYNERGY trial was a randomized, open-label, multi-center clinical trial designed to compare

two anti-coagulant drugs on the basis of various time-to-event endpoints. As usual, the protocol

dictated circumstances, such as occurrence of a serious adverse event, under which it wasmandatory

for a subject to discontinue his/her assigned treatment. In addition, as in the execution of many

trials, some subjects did not complete their assigned treatment regimens but rather discontinued

study drug prematurely for other, “optional” reasons not dictated by the protocol; e.g., switching

to the other study treatment or stopping treatment altogether at their or their provider’s discretion.

In this situation, as an adjunct to the usual intent-to-treat analysis, interest may focus on inference

on the “true” treatment effect; i.e., the difference in survival distributions were all subjects in

the population to follow the assigned regimens and, if to discontinue treatment, do so only for

mandatory, but not optional, reasons. Approaches to inference on this effect used commonly

in practice are ad hoc and hence are not generally valid. We use SYNERGY as amotivating

case study to propose generally-applicable methods for estimation and testing of this “true”

treatment effect by placing the problem in the context of causal inference on dynamic treatment

regimes. Analysis of data from SYNERGY and simulation studies demonstrate the utility of the

methods. This is a joint work with DrMarie Davidian andDrMin Zhang.



Jason Fine (UNC) - Dependent Censoring and Competing Risks: Confusion and Controversy

Historically, censoring has played a dominant role in survival analysis, with the typical development

in an introductory course placing heavy emphasis on right censoring and the assumption of

independence necessary for the validity of standard analyses for the event time. Competing

risks, in which there aremultiple causes of failure, are widespread in contemporary applications

in the biological and biomedical sciences. Such issues are generally treated superficially in the

framework of right censoring, where the independence assumptionmay be violated when a

competing event occurs. This has lead to confusion regarding the appropriate analysis of competing

risks data: the conventional wisdom is that if the competing risks are independent, then standard

methods for right censored data should be employed. However, the interpretation of such

analyses may be problematic, as the quantity being estimated corresponds to a setting where

competing events do not exist, whichmay not be practically relevant. On the other hand, if

dependence is a possibility, then the standard recommendation is that alternative approaches

may be needed. Again, care is needed, as issues of interpretation are closely tied to themethod

of analyses. When the competing event is treated as a censoring event, issues of interpretation

may arise, just as under independence. This talk will survey these issues, highlighting how the

common strategy for teaching survival analysis has lead to controversy regarding the handling

of competing events. I will suggest an alternative approachwhich places censoring and competing

risks on equal footing, providing a clear and explicit understanding of key interpretive issues

and assumptions underlying the available analyses. Real data examples will be used to illustrate

themain points.

S4D: OPPORTUNITIES ANDCHALLENGES IN THEDESIGNANDANALYSIS

OF IMMUNOTHERAPIES TRIALS

Kay Tatsuoka (BMS) - Alternatives to traditional endpoints and analysis methods in

immuno-oncology trials

Traditional endpoints, design and analysis methods in Oncologymay not be appropriate for

Immuno-Oncology. Novel endpoints based on datamining will be explored as alternatives.

Their relationship to long-term survival will be discussed. Additionally, the issues related to

event-based designs owing to non-proportional hazards will be illustrated using examples.

Some possible solutions to overcome these issues will be discussed.

SusanHalabi (Duke) - Challenges in the Design of Oncology Trials with Immunotherapies

Several oncology trials with immunotherapies have shown that the treatments have impact on

overall survival but not progression-free survival. Evenwith the survival, delayed treatment

effect of at least 3months has been observed suggesting that the proportional hazards assumption.

The violation of the proportional hazardsmodel will also have an impact onmonitoring the trial.

Late stage designs will be discussed and examples will be used to illustrate howwe addressed



some of the challenges in the design.

PralayMukhopadhyay (AstraZeneca) - Statistical Considerations in the development

of novel Immune-Oncology Agents

The development of immune-oncology (IO) agents poses some unique challenges that may not

be fully addressed by the use of traditional statistical approaches. Onewell known problem

is the anticipation of a delayed treatment effect and the need for sample size considerations

assuming non-proportional hazards (PH).We discuss the impact of a delayed treatment effect

and implications on sample size and study duration during the design stage. We also explore the

appropriateness of using a weighted logrank test versus the un-weighted test for comparing

two treatment arms in this situation. Another challenge is the overall characterization of clinical

benefit of these agents, where the treatmentmay be effective in only a subset of patients but

resulting in long term remissions that are a hallmark of IO therapy. We evaluate the strengths

and limitations of available measures such as the hazard ratio (HR), medians and restricted

mean survival time (RMST) in describing overall treatment benefit. An alternative approach

using the generalized pairwise comparisonmethod is evaluated in this setting.

S5A: SAFETYANDBENEFIT-RISKANALYSIS INDRUGDEVELOPMENT

Frank Rockhold (Duke) – Benefit to risk considerations in ongoingmonitoring of clinical

trials

The overall goal of the clinical trial is to assess a primary objective and endpoint (usually a benefit)

over the background of secondary endpoints including patient safety. The objective of trial

monitoring is to integrate the information efficacy and safety information in an integratedmanner

tomake ongoing decisions about whether to continue the trial as is, have the design altered,

or prematurely discontinue based on the benefits and harms they are observing in the trial. In

other words, the IDMC is taskedwith creating a “benefit to risk” picture for the trial patients

and future patients on an ongoing basis. The science of Benefit to risk for quantitatively summarizing

completed trials (one ormany) has evolved over the past decade. The purpose of this talk to is to

continue our exploration of how onemight apply these techniques in amoremore structured

and systematic way in an IDMC setting.

Chunlei Ke (Amgen) – Benefit-risk Assessment Using Number Needed to Treat and

Number Needed to Harm

Number needed to treat (NNT) is a useful measure to translate the therapeutic effect of a drug

to clinical practice. So is the number needed to harm (NNH) for the potential risk. NNT and

NNHprovide a simple approach to assisting the benefit-risk assessment (BRA) of a drug. Time-to-event

endpoints are commonly used in oncology clinical trials. NNT has been extended to time-to-event

endpoints but these extensions have some limitations. Wewill present a hazard-based NNT

using an additive hazardsmodel. Estimation and inference procedures will be provided. Graphical



methods and formal statistical tests will be proposed to evaluate the assumption of additivity. A

multivariate additive hazardsmodel will be used to estimate NNT andNNH jointly. Inference

on the NNH andNNT ratio for BRAwill be explored as well. The proposedmethods will be

illustrated with some real clinical trial dataset.

OlgaMarchenko (Quintiles) – Safetymonitoring in oncology clinical trials

TheNIH requires data and safetymonitoring, generally, in the form of Data and SafetyMonitoring

Boards (DSMBs) for phase III clinical trials. For earlier trials (phase I and phase II), a DSMB

may be appropriate if the studies havemultiple clinical sites, are blinded (masked), or employ

particularly high-risk interventions or vulnerable populations; otherwise, safety monitoring

should be performed by a study investigator and a study safety team. A formal stopping rule for

toxicity can serve as a useful reference for a DSMB or a safetymonitoring teamwhen reviewing

the totality of toxicity data in oncology trials. Phase I trials in oncology are conducted among

cancer patients and typically with an assumption that the benefit of the cancer treatment will

increase with dose. Severity of toxicity is also expected to increase with dose, so the challenge

is to increase the dose without causing an unacceptable toxicity to patients. The goal of Phase

I trials is to identify themaximum tolerated dose (MTD). Phase II studies are conducted at the

MTD estimated from phase I and they evaluate whether a new drug has sufficient efficacy to

warrant further development and refine the knowledge of its safety profile. In Phase II trials a

toxicity rule prescribes if the trial needs to be stopped early due to levels of toxicity higher than

expected. In Phase III trial we look at the benefit-risk trade-off to decide if the trial needs to

be stopped. In this presentation, the overview of safety monitoring strategies using Frequentist

and Bayesian approaches together with case studies will be presented and discussed. The discussion

will be focusedmainly on safetymonitoring in Phase II and Phase III trials.

S5B: STATISTICAL ISSUES RELATED TOPROGRESSION-FREE SURVIVALAND

OVERALL SURVIVAL

Jim Love (Boehringer Ingelheim) –Observations on the relationship between progression-free

survival and overall survival

This talk will use published results from trials in lung cancer to support the following assertions:

The PFS-OS relation is qualitative, as well as quantitative. Qualitative factors include: the type

of comparator; extent of post-progression anti-cancer treatment; and sub-groups defined by

the targetedmechanism of drug action. Despite substantial effects on PFS, the effect onOS can

be equivocal, and can differ within sub-groups. Clear effects onOS can sometimes be demonstrated

in sub-groups defined by themechanism of action despite extensive “cross-over”. On the other

hand, significant OS can be seen despite amoremodest effect on PFS. Sub-groups driving the



significant effect onOS in the overall results can sometimes be inferred, but not clearly identified.

Richard Cook (Waterloo) – The analysis of progression-free survival, overall survival

andmarkers in cancer clinical trials

Cancer clinical trials are routinely designed on the basis of event-free survival timewhere the

event of interest may represent a complication, metastasis, relapse, or progression. This talk

is concernedwith a number of statistical issues arising with use of such endpoints including the

interpretation of composite endpoints, analyses involving dual censoring schemes for component

endpoints, and the causal interpretation of effects. Remarks will also bemade on the analysis of

longitudinal and survival data.

Terry Therneau (Mayo Clinic) – UsingMulti-stateModels to Understand Cancer Data

This talk will show howwewere able to sort out and better understand puzzling results from a

cancer trial by usingmulti-state prevalence (Aalen-Johansen) curves to track patients’ progress

through the cycle of induction, response, stem cell transplant, and relapse. Tools for this are part

of the standard R survival package and are very easy to use.

S5C: CURRENT ISSUES IN BIOSIMILAR STUDIES

XiaoyuDong (FDA) - Exact Test Based Approach for Equivalence Test with Parameter

Margin

Equivalence test has a wide range of application in pharmaceutical statistics in which we need to

compare the similarity between two groups. In recent years, equivalence test has been used in

assessing analytical similarity between a proposed biosimilar product and a reference product.

More specifically, themean values of the two products for a given quality attribute are compared

against an equivalencemargin of±f × σR, which is a function of the reference variability.

In practice, this margin is unknown and is estimated from the sample as±f × SR. If we use

this estimatedmargin with the classic t-test statistics on the equivalence test on themeans,

both Type I and Type II errors may inflate. To resolve this issue, we develop an exact-based test

method and compare this methodwith other proposedmethods, such asWald test, constrained

Wald test and Generalized Pivotal Quantity in terms of Type I error and power. Application of

thosemethods on data analysis is also provided in this paper. This work focuses on the development

and discussion on the general statistical methodology and is not limited to the application of

analytical similarity.

Aili Cheng (Pifzer) – A Further Look at the Current Equivalence Test for Analytical

Similarity Assessment

Establishing analytical similarity is the foundation of the biosimilar product development. Although

there is no guidance on how to evaluate analytical data for similarity, the FDA recently suggested

the equivalence test for themean difference between innovator and the biosimilar product



as the statistical similarity assessment for Tier 1 quality attributes (QAs), which are defined

as theQAs that are directly related to themechanism of action. However, our mathematical

derivation and simulation work has shown that type I error is typically increased inmost realistic

settings when an estimate of sigma is used for the equivalencemargin, which cannot be corrected

by increasing sample size. The impact of the constant c on type I error and sample size adjustment

in the imbalanced situation will be discussed in this presentation as well. This is a joint work

with Dr Neal Thomas from Pifzer.

Meiyu Shen (FDA) – Statistical considerations regarding to correlated lots in analytical

biosimilar equivalence test

In the evaluation of the analytical similarity data, an equivalence testing approach for most

critical and quantitative quality attributes, that are assigned to Tier 1 in their proposed three-tier

approach, was proposed (Tsong, Dong, and Shen, 2016). Food andDrug Administration (FDA)

has recommended the proposed equivalence testing approach to sponsors throughmeeting

comments for Pre-Investigational NewDrug Applications (PINDs) and Investigational New

Drug Applications (INDs) since 2014. FDA has received some feedbacks on statistical issues of

potentially correlated reference lot values subjected to the equivalence testing since independent

and identical observations (lot values) from the proposed biosimilar product and the reference

product are assumed. In this article, we describe onemethod proposed by Yang et al (2016) in

biosimilar submissions for correcting the estimation bias of the reference variability so as to

increase the equivalencemargin and its modified versions for increasing the equivalencemargin

and correcting standard errors in the confidence intervals assuming that the lot values are

correlated under a few known correlationmatrices. Our comparisons between these correcting

methods and no correction for bias in the reference variability under several assumed correlation

structures indicate that all correctingmethods would increase type I error rate dramatically

but only improve the power slightly for most of the simulated scenarios. For some particular

simulated cases, type I error rate can be extremely large (e.g., 59%) if the guessed correlation is

larger than the assumed correlation. Since the source of a reference drug product lot is unknown

in nature, correlation between lots is a design issue. Hence, to obtain independent reference lot

values by purchasing reference lots at a wide timewindow often is a design remedy for correlated

reference lot values. This is a joint work with Dr TianhuaWang andDr Yi Tsong from FDA/CDER.

S5D: EXPOSURE RESPONSEMODELING IN THE PHARMACEUTICAL INDUSTRY

YamingHang (Biogen Idec) – Important Issues Relevant to Statisticians in Exposure-Response

Modeling – Illustrated by Case Studies

Over the years, pharmacometrics work including exposure-responsemodeling and simulation

have been appliedmore broadly to aid drug development as well as to influence the regulatory

decisionmaking. Depending on the purpose of themodeling work and nature of the data, same

data set can be analyzed in very different approaches. In turn, the requirement for statistical



rigor and robustness of themodel varies. For example, a proportional hazardmodel might be

sufficient to describe the relationship between exposure and risk of certain event, however,

it is most likely insufficient to predict the incidence rate under different complicated dosing

regimens. Through a few case studies, the presenter will discuss the important issues related

to exposure-response analysis that are fit-for-purpose in nature. These topics include but are

not limited to: underlying physiology/mechanism, experimental design, selection of exposure

metric, proper statistical methodology andmodel qualification. The audience will also be given

a taste of how exposure-responsemodeling can be used to answer questions during the drug

development, or impact the labeling language.

BretMusser (Merck) – Dose-Response and Exposure-Response Analyses in Dose

Selection

Dose selection is themost critical decision in a clinical development program; that is, choosing

the right dose in a given patient population for a certain indication. Traditionally, dose selection

decisions have been driven by dose-response analyses, but more recently exposure-response

analyses have grown in prominence to both supplement and replace dose-response analyses.

Exposure-response analyses have beenwidely used to support new target populations (such as

pediatric or geriatric populations), dosing regimens, dosing forms and routes of administration,

and are increasingly used to support even the primary dose selection decisions. In this talk,

dose-response and exposure-response analyses will be compared and contrasted, and the strengths

and limitations of each will be discussed. Examples will be provided to illustrate themajor points.

Joint work with Ronda K Rippley (Merck)

Dalong Patrick Huang (FDA/CDER) – Some Statistical Issues in Exposure-Response

Modeling using Concentration-QTcData

The ICH E14 clinical guidance has been revised (December 2015) and now enables the use

of exposure response (ER) analysis applied to early phase clinical data to provide definitive

evidence of the lack of a QT effect of a drug in development. The overview of the revised guidance

will be provided. Wewill present statistical reviewers’ perspectives in concentration-QTcmodeling

based on FDA’s QT-interdisciplinary reviews and simulations.



POSTER SESSION - ABSTRACTS

Design and statistical analysis of method transfer studies for biotechnology products

Meiyu Shen (FDA), Lixin Xu (FDA), Yi Tsong (FDA), Juhong Liu (FDA)

During the biotechnology product development, a new analytical procedure regarding the

choice of analytical instrumentation andmethodology is often carefully selected based on the

intended purpose and scope of the analytical method. After an analytical method is successfully

validated and implemented, this method including the standard operating procedure (SOP)

will be followed during the life cycle of the product. The life cycle management of analytical

methods also includes but not limited to trend analyses onmethod performance at regular

intervals to evaluate the need to optimize the analytical procedure or to qualify even revalidate

all or a part of the analytical procedure, development and validation of a new or alternative

analytical method for a new impurity, and transferring a well-developed analytical method from

an original laboratory to a new contract laboratory or a new proposed production site. Method

transfer is a common practice during the life cycle management of analytical methods. Since the

analytical method to be transferred has been already thoroughly evaluated and fully validated

for its intended purpose at the original laboratory, themain purpose of method transfer studies

is usually through a qualification process to determine if the two laboratories providing comparable

results across the range of interest, and to assure themethod after transfer is still suitable

for its intended use. Inconsistent advice is often seen regarding testingmaterials, statistical

methods, and acceptance criteria in the literature. Furthermore, there is no detail on advising

the design and analyses for method transfer studies in the regulatory guidance fromUS Food

andDrug Administration (FDA) and other Agencies or authorities. Wewill propose a design and

a statistical equivalence analysis with a sample size dependent margin for themean comparison

of the proposed two laboratories (the original laboratory and the receiving one) in method

transfer studies in this poster based on our review experience.

Assessment of prescribability based on two one-sided probabilities

HuzhangMao (UTexas), Xiaoyu (Cassie) Dong (FDA), Yi Tsong (FDA)

Drug prescribability is defined as the choice for prescribing an appropriate drug product for a

new patient between a reference drug product and a test drug product. Biological drug products

aremade via living systems and are complex and variable in nature. Unlike generic versions

of small molecule drug products that contain the exact same structure as the innovative drug,

biological products can only be similar to reference product. Hence, average bioequivalence

between test and reference drug products need to be confirmed before prescribability can

be claimed. Prescribability is usually assessed through comparison of themarginal response

distributions between test and reference products by including the total variability. Using the

two one-sided tests approach developed byDong et al (2014), parallel arm design and 2 × 2

crossover design were investigated to assess prescribability in terms of type I error rate, statistical



power and sample size requirement. Our results indicated that both parallel arm design and 2 ×

2 crossover design can be used to assess prescribability, In addition, compared to parallel arm

design, crossover design is more efficient and thus requires smaller sample size by removing

the variance due to subject-by-sequence interaction, which is attributed to the fact that each

subject serves as his/her control in either sequence.

Dilemma on Conditional Power Based Sample Size Re-estimation

Xiaoyu Cai (GWU), Yi Tsong (FDA),Meiyu Shen (FDA), Yu-TingWeng (FDA)

Conditional powermethodwas widely discussed in the literature on adaptive un-blinded sample

size re-estimation (SSR) designs. The common application of conditional powermethod can be

divided into three categories: (a) Futility assessment (b) Decision on sample size adjustment and

(c) Decision on sample size increment. The conditional power basedmethods have advantages

of controlling type I error rate andmaintaining conditional power of the final test at a desired

level. However, likemost of other designs, it is not a universal optimal design, even among different

approaches of adaptive designs. Moreover, the performance of conditional powermethod

highly relies on some operational factors and sample distribution parameters such as the information

fraction, the true treatment and so on. The problem is, except the basic statistical operating

characteristics (type I error rate, power and so on), we have plenty of different criteria to compare

different designs but hardly identify amost important criteria. For example, wemay get better

estimation of conditional power but lost efficiency under the same information fraction. Several

problemsmaymake it a dilemmawhether or not it is worthwhile to use conditional power based

adaptive design in the clinical trials. In this study, wewill point out some of these potential

problems.

Percentile Estimation and Applications

Qi Xia (Temple), Yi Tsong (FDA), Yu-TingWeng (FDA)

Percentile is ubiquitous in statistics and plays a significant role in the day-to-day statistical

application. Not only it can be applied to screening and confirmatory cut-point determination

in immunogenicity assays but also the general percentile formulation enriches the statistical

literature for mean comparison between reference group and test group in bioequivalence or

biosimilarity studies, with the analytical biosimilarity evaluation and scaled average bioequivalence

as special cases. Shen et al. (2015) proposed and compared exact based approachwith some

approximated approaches in one sample scenario for cut-point determination. However, exact

based approach has the issue of computational time complexity. In this poster, we explored

more approximated approaches for percentile estimation such asMethod of Variance Estimates

Recovery (MOVER) based approaches andModified Large Sample (MLS) approaches. All these

approximated approaches are comparedwith exact based approach in one or two sample scenarios.

The applications and performance comparison for each approach are displayedwith numerical

results.



Statistical considerations regarding to correlated lots in analytical biosimilar equivalence

test

TiamhuaWang (FDA),Meiyu Shen (FDA), Qi Xia (Temple), Yi Tsong (FDA)

In the evaluation of the analytical similarity data, FDA has received some feedbacks on statistical

issues of potentially correlated reference lot values subjected to the equivalence testing since

independent and identical observations (lot values) from the proposed biosimilar product and

the reference product are assumed. In this work, we describe onemethod proposed by Yang et

al (2016) in biosimilar submissions for correcting the estimation bias of the reference variability

so as to increase the equivalencemargin and its modified versions for increasing the equivalence

margin and correcting standard errors in the confidence intervals assuming that the lot values

are correlated under a few known correlationmatrices. Our comparisons between these correcting

methods and no correction for bias in the reference variability under several assumed correlation

structures indicate that all correctingmethods would increase type I error rate dramatically

but only improve the power slightly for most of the simulated scenarios. Since the source of

a reference drug product lot is unknown in nature, correlation between lots is a design issue.

Hence, to obtain independent reference lot values by purchasing reference lots at a wide time

window often is a design remedy for correlated reference lot values.

An application ofmodel-fitting formarginal structural modeling in the context of an

observational cohort studywithmissing exposures

Hyang Kim (PAREXEL)

Assessing treatment effectiveness in longitudinal, observational data can be complex, because

treatments are not randomly assigned and patients can change treatment at any time at the

discretion of the investigator depending on changes in confounder. Hence, there are some

confounding of the effect of treatment by a time-varying variable which is affected by previous

exposure and can also subsequently influence treatment changes. Marginal structural models

(MSM) estimation employed in which the goal is to obtain coefficients to create weights so that

treatment exposure is not temporally confounded. However, missing in covariates/confounders

measurements are unavoidable with longitudinal, observational data and it is directly violated

theMSMmodeling assumptions. MSMpermits us to create a pseudo-population in which treatment

exposure is no longer temporally confounded. The pseudo-population can then be used in a

straightforwardmanner with an appropriate regression estimator to derive treatment effects.

A simulation study is conducted to examine bias in estimation of the effect of treatment on

recurrent event rates when ignoringmissing in covariates and further themodel assumptions

ofMSM.We demonstrate implementation of themethod in a longitudinal cohort study setting

where the effect of treatment is estimated on binary outcomes with both time-fixed and time-varying

potential confounders. Puttingmodel misspecification problem aside, simulation study shows

that strength of confoundingmay lead to bias in estimation regardless missing in covariates.

Modeling for assessing treatment effective in presence of time varying confounders andmissing



exposures on the confounders is a real challenge. A customized approach is required to estimate

causal treatment effects where time-varying confounders are affected by previous exposure

andmissing.



ORGANIZERS, SPEAKERS, INSTRUCTORS, POSTER PRESENTERS

Name Affiliation Name Affiliation

Amanda Redig DFCI Annie Lin FDA

BenGoldstein Duke Bohdana Ratitch Quintiles

BretMusser Merck Bruce Binkowitz Merck

Butch Tsiatis NCSU Chunlei Ke Amgen

Cliburn Chan Duke CraigMallinckrodt Lilly

Dalong Huang FDA Dan Sargent Mayo

Danyu Lin UNC DavanMehrotra Merck

DebbieMedlin Duke Donglin Zeng UNC

Elizabeth DeLong Duke Eric Groves Quintiles

Eric Perterson Duke Federico Innocenti UNC

Frank Rockhold Duke Fred Snikeris PAREXEL

Gary Koch UNC Gordon Lan JnJ

Ilya Lipkovich Quintiles Inna Perevozskaya Pfizer

James Love Boehringer-Ingelheim Jason Fine UNC

Jeremy Taylor Michigan Jichun Xie Duke

JingWang Glead John Bauman Quintiles

Joseph Cappelleri Pfizer Joseph Lucas Duke

Kay Tatsuoka BMS Ke Song Duke

KentWeinhold Duke Kerry Lee Duke

Kevin Liu JnJ Kouros Owzar Duke

Lisa LaVange FDA Lynn Lin PSU

Mark Chang Veristat Marlina Nasution Paraxel

Maura Stokes SAS Meiyu Shen FDA

NancyQi Duke OlgaMarchenko Quintiles

PralayMukhopadhyay Astrazeneca Qi Jiang Amgen

Radleigh Santos TPIMS Rajeshwari Sridhara FDA

Raluca Gordan Duke Renè Kubiak Boehringer-Ingelheim

Richard Cook Walterloo Richard Simon NIH

Robin Bliss Veristat SandeepMenon Pfizer

SharonMurray Paraxel Sharon Updike Duke

Shein-Chung Chow Duke Shiowjen Lee FDA

Sin-Ho Jung Duke Stephen George Duke

Steve Snapinn Amgen SumithraMandrekar Mayo

Susan Halabi Duke Suzanne Dahlberg DFCI



Terry Hyslop Duke Terry Sosa Quintiles

Terry Therneau Mayo Walter Boyle Sutherland Healthcare

XiaofeiWang Duke XiaoyuDong FDA

Yaming Hang Biogen Yeh-Fong Chen FDA

Yi Tsong FDA Yijing Shen Genentech

YuanWu Duke



ATTENDEE LIST

Name Affiliation Name Affiliation

John Bauman Quintiles Anna Bellach Copenhagen

Gerald Belton NCSU Parul Bhargava Shire

Bruce Binkowitz Merck Robin Bliss Veristat

Walter Boyle Sutherland AILI CHENG Pfizer

Joseph Cappelleri Pfizer Cliburn Chan Duke

Stephen Chang Pharmacycl Ling Chen FDA

Mei Chen Medpace Yeh-Fong Chen FDA

Shein-Chun Chow Duke Richard Cook Waterloo

Mary Cooter Duke Doug Criger PPD

SuzanneDahlberg DFCI QianyuDang FDA

Sargent Daniel Mayo XiaoyuDong FDA

Dexiang Gao Colorado ConwayGee Chiltern

Stephen George Duke BenGoldstein Duke

Raluca Gordan Duke Lin Gu Duke

SusanHalabi Duke Yaming Hang Biogen

JimHerndon Duke ShuyenHo PAREXEL

ChuyunHuang PAREXEL Dalong Huang FDA

WayneHuggins RTI FEDERICO INNOCENTI UNC

James Imus Quintiles Qi Jiang Amgen

Teri Jimenez PAREXEL Sin-Ho Jung Duke

Natalia Kan-Dobrosky PPD Chunlei Ke Amgen

Wang Kehui PPD Brian Kilgallen UCB

Shiowjen Lee FDA Lynn Lin PSU

Min Lin FDA Ilya Lipkovich Quintiles

Kevin Liu Janssen Lan Liu INC Resear

Shubin Liu PPD James Love Boehringer

Joseph Lucas Duke CraigMallinckrodt Eli Lilly

SumithraMandrekar Mayo OlgaMarchenko Quintiles

MalickMbodj FDA CynthiaMcShea UCB

DebbieMedlin Duke DevanMehrotra Merck

SandeepMenon Pfizer ZhuangMiao FDA

PralayMukhopadhyay AstraZenec SharonMurray PAREXEL

BretMusser Merck Marlina Nasution PAREXEL

Tatiana Nevmyrych PPD Wei Pan Duke



Inna Perevozskaya Pfizer Matthew Phelan Duke

XINYUEQI Duke Bohdana Ratitch Quintiles

Amanda Redig DFCI Frank Rockhold Duke

Kingshuk Roy Choudhur Duke Christel Rushing Duke

Radleigh Santos TPIMS Justin (Zo Shang PAREXEL

Lynn Shemanski CRAB Meiyu Shen FDA

Yijing Shen Genentech Qing Shi PAREXEL

Richard Simon NIH Steven Snapinn Amgen

Fred Snikeris PAREXEL Terry Sosa Quintiles

Rajeshwari Sridhara FDA Patricia Stephenson Rho

Thais Talarico Duke Jiali Tang PPD

Jeremy Taylor Umichigan Therneau Terry Mayo

Samantha Thomas Duke Tracy Truong Duke

Anastasios Tsiatis NCSU Yi Tsong FDA

Fabiana Vazquez Duke JINGWANG Gilead

YU-TINGWENG FDA GuanfangWang Yahoo

LiweiWang PPD XiaofeiWang Duke

KentWeinhold Duke YuanWu Duke

Jichun Xie Duke Qing Yang Duke

Lisa Ying BMS JIAYIN ZHENG Duke

Donglin Zeng UNC eric groves Quintiles

frances wang Duke tianhuawang FDA



FLOORPLAN

The symposiumwill be held atMillenniumHotel Durham, 2880 CampusWalk Avenue, Durham,

NC., 27705. MillenniumHotel Durham is minutes away fromDukeUniversity Hospital and

Duke University and an easy drive to downtownDurham.

OPENINGREMARKS, KEYNOTE SPEECH, PARALLEL SESSIONS,

POSTER SESSION, SOCIALMIXER

SHORTCOURSES, PARALLEL SESSIONS



Department of Biostatistics and Bioinformatics

Duke University School ofMedicine

2424 Erwin Road, Suite 1102

Durham, NC 27710

U.S.A.


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