1. Design and Analysis of Clinical Study 12. Randomized Clinical Trials Dr. Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia

2. Basic Design of Clinical Trials Sample Subjects Randomise Treatment Placebo CuredSame CuredSame Blinding Blocking

3. Variations in Basic Design - 1 Run-in Design Admission On placeboCompliers randomised Cross-over Design Recruitment Randomised Treatment 1 Treatment 2 ASSESSASSESS ASSESSASSESS

4. Variations in Basic Design - 2 Time - Series Design RecruitmentTreatment Assess No Treatment Assess Treatment Assess Factorial Design RecruitmentRandomise Treatment 1 Treatment 2 Low Dose High Dose Low Dose High Dose

5. Issues of Methodology - I Entry criteria –Strict for explanatory trials –Less strict for pragmatic trials Diagnosis –How accurate? Intervention –Compliance –Drop-out –Competing intervention

6. Issues of Methodology - II Subject allocation –Different rates of drop-out between groups cause under or over estimate of outcome Treatment allocation –Randomisation and blinding help to remove bias. Blocking needed if outcomes vary because of age, sex or other attributes.

7. Challenges in Designing Clinical Trials Control of bias –In allocation of subjects to treatment –In assessment of outcome Sample size –How small a difference is clinically important? –What tests of significance will be used? –What outcome is expected in the control group? Drop-outs and withdrawals –How to handle them during analysis?

8. Generalizability of Results Population of Patients Sample Treated Control Outcome Difference in Outcome by Chance? Generalisability Other Populations Rigour of study

9. Randomization works! Volunteerism Eligibility Placebo effect Hawthorne effect Regression towards the mean

10. Volunteerism, Eligibility, Placebo Effect Volunteerism –People who agree to participate in clinical trials are an “elite” group of patients with extremely good prognosis. Eligibility: –Patients have to meet stringent eligibility criteria before randomization, or they would be excluded Placebo can do just about anything (prolong life, cure cancer). Placebo can also cause side effects. Placebo effect is very useful in medicine but in epidemiology it causes problems, so we try to equalize it between the 2 groups.

11. Regression Towards the Mean Weather game Individuals with initially abnormal results tend on average to have more normal (closer to the mean) results later. Lab tests, BP etc. Recheck before randomization. Run-in period. Sophomore slump, medical school, Airforce landing feedback

12. Objectives of Subgroup Analysis Support the main finding Check the consistency of main finding Address specific concerns re efficacy or safety in specific subgroup Generate hypotheses for future studies

13. Inappropriate Uses of Subgroup Analysis Rescue a negative trial Rescue a harmful trial Data dredging: find interesting results without a prespecified plan or hypothesis

14. To Avoid Inappropriate Uses of Subgroup Analysis Prespecify analysis plan Prespecify hypotheses to be tested based on prior evidence Plan adequate power in the subgroups Avoid the previous pitfalls

15. Problems with Subgroup Analysis 1.Low power 2.Multiplicity 3.Test for interaction 4.Comparability of the treatment groups maybe compromized 5.Over interpretation

16. ITT Intention to treat analysis Once randomized always analyzed Why ? 1. Change in therapy may be related to outcome or eligibility 2. To get the full benefit of randomization 3. Effectiveness versus efficacy

17. Five-Year Mortality in Coronary Drug Project

18. Screening Mammography

19. Descriptions of “Trials” 34% relative decrease in the incidence of MI. The decrease is statistically significant. The 95% CI ranges from 55% relative decrease to a 9% relative decrease. 1.4% decrease in …. (2.5% versus 3.9%). The decrease is statistically significant. The 95% confidence interval ranges from a 2.5% decrease to a.. 77 persons must be treated for an average of just over 5 years to prevent 1 MI.

20. Ethical Issues When is it unethical to randomize ? When Do you stop a trial? Data Safety Monitoring Board Early Termination rules O’Brien Fleming 1.Early vs. late 2.Benefit vs. harm (blinding?) 3.Multiplicity 4.Rules. Scenarios.