Study Design

Design of Studies in STD Research

Objectives:• Discuss the following study designs:

– cross-sectional– case-control– Cohort– Clinical trial

• Discuss the components of study design:– Study Design, population, time frame, inclusion/exclusion,

sample size, study flow diagram , outcome/predictors/confounders/effect modifiers, plan of analysis, efforts to reduce threats to validity, strengths/limitations

• Discuss some complicated issues in study design

Study Designs

Descriptive Analytic Experimental

correlational

case report/case series

cross-sectional

case control

cohort

clinical trial

community trial

Criteria for Causality

• Biological Credibility

• Consistency of findings

• Dose-response

• Magnitude of the association

• Time sequence

Cross sectional

D

__D

E

__E

Case Control

E

D

__D

__E

__E

E

Cohort

E

__E

D

__D

D

__D

Phases of a Clinical Trial

• Phase I - safety (pharmacokenetics - to determine maximum tolerated dose)

• Phase II - Evidence of a response

• Phase III - Safety, efficacy

• Phase IV - Safety, Acceptability, Efficacy

Study Diagram - Classic Randomized Controlled

Eligible SubjectPool R

Int

P/SC

LTF/C

O

O

LTF/C

Study Design - Cross-over

Study Eligibles R

E

C C

E

Hypothesis Testing

• Hypothesis testing involves conducting a test of statistical significance and quantifying the degree to which sampling variability may account for the results observed in a particular study

• When designing data collection tools, keep in mind your final analysis

Statistical Tests: 2 T-test Measures of Association: Odds Ratio, Relative Risk

Objectives should be stated in terms of an hypothesis

• Null Hypothesis: There is no difference

Medication A will have not effect on disease progression

• Two tailed Hypothesis: There is some difference

Medication A will have some effect on disease progression

• One tailed Hypothesis: The difference is greater or less

Medication A will reduce deaths due to disease X

Medication A will increase deaths due to disease X

Outcome of interest

• Write the research question in advance

• outcome variable:– should be measurable in all subjects– should be capable of unbiases

assessments– should be ascertained as completely as

possible

Response or Outcome variables

• You may have outcomes other than hard endpoints

• surrogate markers

• quality of life

Follow-up Studies - Survival Analysis

• This analysis used when subjects are entered over a period of time and have various lengths of follow-up.

• Dichotomous endpoints

• Kaplan Meier or Product Limit

• Cox Proportional Hazard modeling

Intent-to-treat Analysis

• For persons who cross-over to the other arm. You classify that person into the arm they were originally assigned.

• Less biased results than “as treated” because you maintain randomization.

• Only works if there is not a lot of crossing over very early in the study

Reasons for withdrawal of Subjects

• Ineligibility (misclassification, imprisonment, moved)

• Noncompliance (adverse effects of intervention, loss of interest, changes in underlying conditions, substance usage)

Measurement

• Outcome

• Predictor

• Confounder

• Effect modifier

Validity and Reliability

xx xx

Validity Reliability

Relative Risk for a disease exposure

STD No STDDrug use 75 25 100No druguse

25 75 100

100 100 200

RR = 75/100 = 3.00 25/100

C.I. (2.10 - 4.29)

Odds Ratio Calculation

O.R. = (100*150) = 3.00 (100*50)

STD No STD TotalDrug use 100 50 150No Drug use 100 150 250

200 200 400

Confounding and/or interaction (Kleinbaum, Kupper and Morgenstern)

HIV risk perception and self-protective behaviors among high risk persons in

community settings Patricia Kissinger, Ph.D.(1)

Nomi Fuchs, MPH (2)

Catherine Schieffelin, MPH (2)

Jane Herwehe, MPH (2)

DeAnn Gruber, MSW (2)

(1) Louisiana State University, HIV Outpatient Program

(2) Children's Hospital - Family Advocacy, Care and Education Services (FACES)

Purpose

• The purpose of this study was to examine HIV risk perception and self-protective behaviors among high risk people in community settings.

Methods

• Street intercept and in-depth interviews were conducted from August 1997 to June 1998

• Inclusion:– Sexually active people– aged 15-35– living in six communities of New Orleans

with the highest gonorrhea rates.

Results• Of 1133 respondents, 97% were African

American, 37.4% were 15-18 years of age.• 46.2% reported an HIV risk behavior, 66.5%

reported condom use, and 69.9% reported ever having been tested for HIV.

• Many respondents (39%) perceived themselves to be at no risk, but reported engaging in an HIV risk behavior

• Adolescents and persons who had been HIV tested were most likely to have this discrepancy.

Results con’t

• Among the 524 persons who reported an HIV risk behavior, 19-35 year olds were less likely to use condoms and adolescent men were less likely to have been HIV tested.

• In-depth interviews revealed diverse reasons for failure to perceive oneself at risk and failure to be HIV tested including optimistic bias, risk group identity, hierarchy of risk and fear.

Table 2. Factors associated with a discrepant responsea (N=1072)

% discrepant AdjustedO.R. (95% C.I.)

Age 15-18 19-35

44.936.2

1.58 (1.20-2.09)**1.00

Gender Women Men

39.238.7

1.02 ( .79-1.33)1.00

Used a condomlast sexual act Yes No

42.536.1

1.16 ( .88-1.52)1.00

Been HIV tested Yes No

39.338.3

1.37 (1.01-1.85)*1.00

Table 3. Factors associated with self protective behaviors among persons reporting an HIV risk behavior (N=524)

Condom useAdjusted O.R.(95% C.I.)

Ever been HIVtestedAdjusted O.R.(95% C.I.)

Age 15-18 19-35

1.00 .48 ( .32 - .72)**

.16 ( .10- .25)**1.00

Gender Women Men

.68 ( .46- 1.01)1.00

1.00 .34 ( .22 - .52)**

Self-assessed HIV risk Yes No

1.26 ( .65-2.30)1.00

2.01 ( .94-4.29)1.00

Assessed partner's risk Yes No

.58 ( .31 - 1.10)1.00

.58 (.27-1.24)1.00

**p < .01

Table 4 Association between reported risk behavior and

self-assessed riskAmong thosereporting highrisk behavior

Agreementbetween selfreported risk andassessed riskK (95% C.I.)

Self-perceivedat risk

15.6% .095 (.057-.135)

Perceivedpartner(s)' atrisk

19.1% .130 (.099-.171)

Kappa .10 (95% C.I. 06-.14) indicating poor reliability

Non-experimental (analytic) study designs

• Conducted because of ethics, cost or convenience

• Two primary types:– Cohort– Case-control

Experimental Designs• Experiment – a set of observations, conducted under

controlled circumstances, in which the scientist manipulates the conditions to ascertain what effect such manipulation has on the observations.

• Ideally only one factor is examined (however, biological variation exists)– Clinical Trials – (individual in a special environment are

randomized)

– Field Trials – (individuals in the community are randomized)

– Community Interventions – (whole communities are randomized)

Field Trials

• Differ from clinical trials in that subjects have not yet gotten disease– (1955) Salk vaccine for Polio– (1975) Vitamin C in preventing the

common cold)– (1982) MRFIT – a field trial of several

primary preventives of MI (N=12,866 and cost $115 million)

Community Intervention and Cluster Randomized Trials

• Community intervention is an extension of a field trial that involves intervention on a community-wide basis– (eg. Mass media campaigns)– (eg. Fluoridated water)

• Cluster randomization - groups of participants are randomized. The larger the cluster, the less that is accomplished by randomizing.

Study Protocol• Rationale and background• Objectives• Study Design• Inclusion/Exclusion• Definitions (intervention, measurements, adherence)• Study Flow chart• Sample Size calculation• Plan of analysis (interim analysis)• Appendices

– Questionnaires– Consent forms– Instructions to interviewers

Example of a flow chart for randomization

Example of a comparison table to demonstrate that randomization was successful

Incidence vs. Prevalence

• In infectious diseases of short duration, incidence may be close to prevalence

• In chronic diseases, prevalence will be far greater than incidence

• Monitor disease burden by prevalence

• Monitor efficacy of programs by incidence

Calculate an Incident Rate

Jan July Jan July Jan July Jan July Jan July Jan time at

1976 1976 1977 1977 1978 1978 1979 1979 1980 1980 1981 riskSub A *---------------------- 2.0 Sub B *---------------------------------x 3.0Sub C *--------------------------------------------------------- 5.0Sub D *--------------------------------------- 4.0Sub E *---------------------------x 2.5Total Years at risk 16.5

* = initiation of study ID=___cases/___person-years-- =Time followedx = development of disease

Measures of Associaton

• Since clinical trials are prospective and the intervention precedes the outcome, a relative risk is calculated.

• Covariates and confounders can be either controlled for in the design or adjusted for in the analysis

Is PID more common among HIV-infected women

• Research Question• Population• Inclusion/exclusion• Study Design• Type of analysis and Unit of analysis• What are the predictors, confounders, and

outcomes of interest• Findings• Limitations/Strengths

Difficulties with this study

• Definition of a case

• Choice a proper control

• Detection bias

A microbicide to prevent HIV among women

• Research Question• Population• Inclusion/exclusion• Study Design• Type of analysis and Unit of analysis• What are the predictors, confounders, and

outcomes of interest• Findings• Limitations/Strengths

Difficulties with this study

• Ethical dilemma

• Exposure is altered by study itself

• Choice of cases and controls

• Sample size considerations

An HPV vaccine to prevent HPV among women

• Research Question• Population• Inclusion/exclusion• Study Design• Type of analysis and Unit of analysis• What are the predictors, confounders, and

outcomes of interest• Findings• Limitations/Strengths

Difficulties with this study

• Misclassification bias possible

• Population to study difficult to find

• Sample size

• Generalizability

Study Design• Statement of hypothesis• Population

– Sampling– Inclusion/Exclusion

• Time frame• Design• Measurement

– Predictors– Confounders– outcome

• Analysis plan– Sample size– Dummy Tables– Analyses to be done

• Efforts to minimize threats to validity• Strengths and limitations

Study Designs

Descriptive Analytic Experimental

correlational

case report/case series

cross-sectional

case control

cohort

clinical trial

community trial

Confounding

E D

C

E=exposure

C=confounder

D=disease

Strategies for Partner Treatment for STD control

By

Patty Kissinger, Ph.D.

Objectives

• Background

• Prior Studies

• Present Studies

• Policy implications

Why treat partners?

• Primary prevention - to break the chain of transmission – Healthy men don’t access health care– Many STDs are asymptomatic

• Secondary prevention - to prevent complications of the disease– STD infections increase the risk of HIV– Recurrence can cause serious health consequences

Recurrent chlamydia

• Causes PID, ectopic pregnancy, infertility and chronic pelvic pain

• Many women are re-infected by an untreated partner

• Strategies for partner treatment are necessary

Basic Reproductive Rate of Infection

(Anderson and May)

Ro= D c Ro is the basic reproductive rate of infection

is the transmission coefficient

D is the infectious period

c is the mean rate of sexual partner change

Sexual Networks

X

X

X

XX

X

X

Methods of Partner Treatment

• Partner referral

• Partner notification

• Patient delivered partner treatment

Problems with Partner Referral

• Studies of chlamydia demonstrate that only 25-40% of named male partners were treated.

• Partners – not told– refuse to come for testing/Rx

Problems with Partner Notification

• Confidentiality

• Expensive and time consuming– Almost 800,000 cases of chlamydia and

400,000 cases of gonorrhea were reported in the US in 2001

• Not all partners are named

• Hard to find partners

Problems with Partner Treatment

• Safety– Allergies– Pregnant women

• Liability– Physician– Nurses– Institutions

• Fear of uncontrolled antibiotic use– Fear of selling medication– Fear of stocking up on medicine

Empirical data in favor of PDPM

• Retrospective cohort in New Orleans (Kissinger et al., Sex Trans Inf 1998; 74:331-333)

• Correlational in Sweden (Ramsted et al 1991; 2:116-118)

• Cross-sectional in San Francisco (Hammer et al. National STD Conf 2000; Wisconsin)

• Cross-sectional in Washington (Golden et al STD 1999; 26:543-547).

• Randomized trial in Uganda (Nuwaha et al. STD 2001; 105-110

Multi-centered Trial – Infertility Prevention Program

• 1787 women aged 14-34

• Eight cities

• Randomized to PDPM versus PR

• Tested at 1 and 4 months using LCR or PCR

• Given 1 gm azithromycin

• Outcome was recurrence

Second Study - Prospective Study

(n/N) % RR 95%CI p value

(108/726) 15 -- --

(87/728) 12 0.8 (0.62-1.05) 0.102

Strategy

1Partnerreferral

Patientdelivered

Issues with the study

• Loss-to-follow-up

• Low power

• Persistence versus recurrence

• Powder form of medication

PDPM seems reasonable

• At the time of treatment for their own chlamydial infection, a majority of women have a partner who remains untreated (Golden, 2001)

• Most patients with STDS prefer to notify the partner themselves (Golden, 2001)

• Men generally perceive practical obstacles to obtaining treatment (Fortenberry, 1997)

Present Studies PA0008 – Female trichomonas trial and male

urethritis study

• Testing three methods: partner referral, booklet referral, PDM

• Male urethritis – quasi-experimental – Delgado

• Female trichomonas – randomized trial – 01 Family Planning

• Baseline and follow-up visit– ACASI interviews– STD testing

Booklet referral

Patient Delivered Partner Medicine

• For Trichomonas (1 gram of metronidazole)

• For Male urethritis (1 sachet of azithromycin 1gram sachet and 1 dose of cefixime 400 mg orally)

• Directly observed medication for index

Outcomes measures

• How many partners are treated (index patient-report)

• How many partners show up to clinic saying that they have been referred by an index partner

• Recurrence rates– InPouch– BD urines

TrichomonasReferral Booklet PDM

Patients 64 61 61

Partners 68 68 73

Ratio 1.06 1.11 1.20

Follow-up rate

66.2 85.3 75.3

Desired 113 113 113

% of desired enrolled

56.6 54.0 54.0

Male urethritisReferral Booklet PDM

Partners 282 237 207

Index 141 121 111

Ratio 2.0 1.96 1.86

Follow-up rate*

66.9 81.0 64.0

Desired 182 182 182

% of desired enrolled

77.5 66.5 61.0

Interim Analysis

Partner took the medicine

ARM 1 ARM 2 ARM 3

Male Urethritis 36.8 45.1* 77.0**

Trichomonas 73.9 60.3 90.1*

*P<0.05, **P<0.01

Policy implementation issues

• More evidence?

• Practice protection

• Need to educate

• Financial support