Data-Driven Strategies for Improved Site Activation and

Patient Enrollment Forecasting

Introduction

Cindy Venendaal, CCRA, MPH, PhD Senior Director, Project Management Clinipace Worldwide

Mark Shapiro, MA, MBA Vice President, Clinical Development Clinipace Worldwide

PRESENTERS

Step 1: Site Selection

Understand protocol and SOC for target patient population

Build robust site profile

Accelerate selection of sites that provide greatest chance of success

Use data used to develop start-up timeline, plan resources, and proactively identify potential budget variances

In Action: Data-Driven Site Selection

Step 2: Regulatory Docs & PSV

Precisely target sites with forecasting and feasibility data

Expedite completion of regulatory documents by electronic submissions

Save time with parallel pre-study site visits for select sites

In Action: Data-Driven Regulatory/Pre-Study

Step 3: Contracting and Budgeting

Time savings can be realized by using agreed upon standardized language from contract and budget templates

If EC/IRB approval is required before contract can be approved, site can review and provide feedback for contract while regulatory documents are with the EC/IRB

Contract can then be agreed upon, pending EC/IRB approval, modifying a serial process to a parallel process

In Action: Data-Driven Contracting/Budgeting

Step 4: Site Initiation Visit

Key activities include: – Collaborating to define a site-specific enrollment plan and target

– Build relationships among the principal investigator (PI), CRO and sponsor

Set date of site initiation visit determined based on expected date of EC/IRB approval

Invest time explaining the project and protocol to get full buy-in from site personnel – Improves enrollment

– Reduces time required for trial execution

Ongoing Activities

Update site activation and enrollment forecast models based on incoming real-time data

Strategic recommendations are possible if either enrollment or activations are not proceeding as planned – These can include:

• Change site profile

• Adjust the number or location of sites

• Adjust the protocol based on IRB/EC/MOH/Site feedback

Benefits of TEMPO

Polling Question

Forecasting Study Start-Up Timelines

Forecasting challenges – Forecasts should begin with historical data

– Past performance is no guarantee

– Uncertainty is inherent and asymmetric • Delays are power-law distributed

Considerations when forecasting study start-up timelines – Published forecasting models for clinical trials don’t take into account

nuances of sites operation

– Sites follow SOPs and complex processes are sometimes the result of outside accreditation (e.g., NCI CCC)

– Larger, more research-oriented sites tend to have more formal processes; smaller, less research-focused sites tend to have less formalized and more flexible processes for start-up

– Enrollment forecasting models developed to determine drug supply requirements (Poisson-Gamma)

Weekly Site Activation

For local IRBs, site activations are roughly Poisson distributed

For a 50-site study, the number of sites activated per week during start-up might look like this:

0%

5%

10%

15%

20%

25%

30%

0 1 2 3 4 5 6 7 8 9

Od

ds

Sites activated per week

92%

In Action: Effect of Site Structure on Timeline

Polling Question

Forecasting Enrollment

Enrollment is not linear – Enrollment is slow during start-up when few sites are activated

– Enrollment peaks only after most or all sites are active

Historic information should be the starting point

Feasibility data should be used cautiously for forecasting – Optimism bias

– Initial forecast ignores information about specific sites

Not all sites are the same – Site performance follows a Gamma or Power Law distribution

– Number of planned sites matters

Forecasts should be revised after data starts coming in

In Action: Forecasting Enrollment Rate

y = 0.862x-0.707 R² = 0.6638

0.00

0.50

1.00

1.50

2.00

2.50

1 10 100

Stu

dy

Enro

llmen

t R

ate

(Su

bje

cts

per

Sit

e p

er M

on

th)

Number of Study Sites

Phase 2, AML Treatment Study Enrollment Rates versus Number of Study Sites with 95% CI

Forecasting First Patient In

In the absence of recruitment advertising campaign before site activation, eligible subjects won’t be lined up waiting to enter trial

Expectations regarding duration between site activation and first subject enrolled should be tempered – If average sites are expected to enroll 1 subject per month, then the

average time from activation to FPI would be 15 days

Sites with fast activation may be highly motivated, but large sites tend to have more complex internal processes, and thus slower start-up

Questions?