1. Randomized ControlledClinical Trials Marin Kollef, M.D. Associate Professor of Medicine Washington University Director, Medical Critical Care Barnes-Jewish Hospital

2. CHAOS THEORYProcess without Structure or Structure without Process equals CHAOS

3. Clinical Trial:DefinitionA planned experiment designed toassess the efficacy of a treatment bycomparing the outcomes in a groupexposed to the intervention with those observed in a comparable group receiving a control orplacebo treatment.

4. East India Shipping Company 1600: General Lancaster’s Ship“And the reason why the General’s men stood better in health then the men of other ships, was this: he brought to the sea with him certain Bottles of the Juice of Limmons, which he gave to each one . . . (Drummond and Wilbraham, 1940)

5. Lind 1747 on board the Salisbury • 12 patients with scurvy taken on board • 2 daily quart of cider • 2 elixir vitriol • 2 vinegar • 2 received one half pint of seawater • 2 nutmeg • 2 orange and lemon juice* • (*These had sudden and visible effects • with one returning to duty after 6 days)

6. RCTs: Timing “On being asked to talk on principles of research, my first thought was to arise after the chairman’s introduction, to say “Be careful,” and to sit down. (Jerome Cornfield, 1959)

7. Avoid preconceived notions Adequate preparation Wait Rush In

8. Other Considerations Test and control groups Outcome measure to evaluate study treatment A bias-free method for assigning patients to groups

9. Requirements: Test and Control Groups Distinguishable. Medically justifiable Ethical base for each treatment Compatible with health care needs Either treatment acceptable Reasonable doubt about efficacy Benefits outweigh risks Similar to real-world use

10. Allocation Scheme Masked to patients, MDs, others Cannot predict future assignments Allocation order is reproducible Allocation methods documented Allocation method has known mathematical properties Process provides a clear audit trail Departures from allocation can be detected

11. Treatment Population Placebo Outcomes Sample Randomize General Schema for RCTs

12. Review of Steps for RCT Design 1. Select sample from population 2. Measure baseline variables 3. Randomize 4. Apply interventions & placebo 5. Follow-up cohorts 6. Measure outcomes

13. Inclusion Criteria Exclusion Criteria

14. Measure Baseline Variables Define potential important variables Compare between study groups Account for differences in study design or analysis of results

15. Severity of Illness Difficult to measure Disease specific v. general Use validated instruments Applicability to study population Acknowledge limitations

16. Randomization The allocation to study groups serves as the main predictor variable of the study. In the simplest design, one group receives the active treatment and the other remains as an untreated control.

17. The effects of any maldistributions that do occur as a result of chance (1 in every 20 baseline variables will differ at P < 0.05) are automatically included in the statistical tests of the likelihood that chance accounts for outcome differences between study groups.

19. Pre-randomization confounding variables Randomization Effect- Effect Key strategies to rule out rival explanation Post-randomization confounding variables Blinding (unintended interventions)

20. Blinding in the ICU Difficult for non-medications • Low vs. high tidal volume • Weaning & sedation protocol • HME v. heated water humidity • Nutritional support

21. Protocols & Unblinded Studies • ECCO2R at LDS Hospital • ARDS population • Protocol direction of all adjustments • in therapy/management • Powerful aid in performance of unblinded research • Limited availability/Expensive

22. Special RCT Designs • Run-in: Maximize power by • increasing responsive proportion • in intervention group • Factorial: Answers 2 questions • Matched pairs: gender, eyes • Group randomization: Natural • pairings, complicated sample size • estimates and analyses

23. Treatment Groups Population Sample Placebo Outcomes Randomize Run in Design for RCTs

24. p = placebo drug Outcomes Drug A & B Population Drug A & Bp Drug Ap& B Sample Drug Ap& Bp Factorial Design for RCTs

25. Special RCT Designs: Nonblinded • Single blind c/w double blind • Partial blinding (outcomes assessment, • data collection) • Time series: confounders gone, 2X • sample size, limited utility • Crossover: accounts for time-dependent • covariables, carryover effects

26. Treatment Population Treatment Sample Control Time Series Design for RCTs

27. 1st time period 2nd time period Population Sample Crossover Design for RCTs

28. Intention-to-Treat • The full potential of a RCT to eliminate • the influence of baseline confounding • variables is only realized when the results • are analyzed according to random group • assignments.

29. Blocked Randomization • Enrollment over extended time • Changing demographics • Statistical reasons to block • Variable blocks preferable to • fixed in unmasked trials

30. Adaptive Randomization “Biased coin” randomization procedure • Number adaptive: assignment • modified by departures from desired • allocation ratios • Baseline adaptive: • Outcome adaptive: Balances the treatment groups

31. APACHE II <15 Population APACHE II > 15 Sample Stratify Randomize Stratified Design for RCTS

32. Outcome Measures • Easy to diagnose and observe • Free of measurement or ascertainmenterrors • Can be observed independent of • treatment assignment • Clinically relevant • Chosen before the start of datacollection