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Design and Analysis of Clinical Study 12. Randomized Clinical Trials

Design and Analysis of Clinical Study 12. Randomized Clinical Trials. Dr. Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia. Basic Design of Clinical Trials. Cured. Same. Treatment. Sample. Randomise. Blinding. Placebo. Cured. Same. Subjects. Blocking.

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Design and Analysis of Clinical Study 12. Randomized Clinical Trials

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  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 Cured Same Treatment Sample Randomise Blinding Placebo Cured Same Subjects Blocking

  3. Variations in Basic Design - 1 Run-in Design Admission On placebo Compliers randomised A S S E S S A S S E S S Cross-over Design Treatment 1 Recruitment Randomised Treatment 2

  4. Variations in Basic Design - 2 Time - Series Design No Treatment Recruitment Treatment Treatment Assess Assess Assess Low Dose Factorial Design Treatment 1 High Dose Recruitment Randomise Treatment 2 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 Treated Outcome Sample Population of Patients Control Outcome Difference in Outcome by Chance? Rigour of study Other Populations Generalisability

  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 • Low power • Multiplicity • Test for interaction • Comparability of the treatment groups maybe compromized • 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 • Early vs. late • Benefit vs. harm (blinding?) • Multiplicity • Rules. Scenarios.

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