Issues in Randomization

1. Issues in Randomization Laura Lee Johnson, Ph.D. Statistician National Center for Complementary and Alternative Medicine Introduction to the Principles and Practice of Clinical Research Tuesday, October 18, 2005

2. Biostatistics • Randomization • Hypothesis Testing • Sample Size and Power • Survival Analysis

3. Objectives • Intuitive understanding of the statistics used in clinical research • Understand and perform some simple but useful analyses & sample size calculations • Learn how to collaborate effectively with statisticians

4. Objectives: Randomization Lecture • Reasons for randomization • Randomization theory and mechanisms • Types of randomized study designs • Compare randomized experimental studies to nonrandomzied observational studies • Nonrandomized experimental studies

5. Outline • Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Statistical Software, Books, Articles

6. Words I Might Use • Phase I, II, IIb, III, IV Trial • Model: y = β0 + β1x1 + β2x2 + ε • Covariate • Effect Modifier • Confounder

7. Confounding • Two or more variables • Known or unknown to the researchers • Confounded when their effects on a common response variable or outcome are mixed together

8. Confounding Example • Relationship between coffee and pancreatic cancer, BUT • Smoking is a known risk factor for pancreatic cancer • Smoking is associated with coffee drinking but it is not a result of coffee drinking.

9. What is confounding? • If an association is observed between coffee drinking and pancreatic cancer • Coffee actually causes pancreatic cancer, or • The coffee drinking and pancreatic cancer association is the result of confounding by cigarette smoking.

10. How to handle confounding • If you know something is a possible confounder, in the data analysis use • Stratification, or • Adjustment • Fear the unknown!

11. Study Design Taxonomy • Treatment vs. Observational • Prospective vs. Retrospective • Longitudinal vs. Cross-sectional • Randomized vs. Non-Randomized • Blinded/Masked or Not • Single-blind, Double blind, Unblinded

12. Outline • Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles

13. Randomization: Definition • Random Allocation • known chance receiving a treatment • cannot predict the treatment to be given • Eliminate Selection Bias • Similar Treatment Groups

14. ONE Factor is Different • Randomization tries to ensure that ONE factor is different between two or more groups. • Observe the Consequences • Attribute Causality

15. Types of Randomization • Standard ways: • Random number tables (see text) • Computer programs • NOTlegitimate • Birth date • Last digit of the medical record number • Odd/even room number

16. Types of Randomization • Simple • Blocked Randomization • Stratified Randomization

17. Simple Randomization • Randomize each patient to a treatment with a known probability • Corresponds to flipping a coin • Could have imbalance in # / group or trends in group assignment • Could have different distributions of a trait like gender in the two arms

18. Block Randomization • Insure the # of patients assigned to each treatment is not far out of balance • Variable block size • An additional layer of blindness • Different distributions of a trait like gender in the two arms possible

19. Stratified Randomization • A priori certain factors likely important (e.g. Age, Gender) • Randomize so different levels of the factor are balanced between treatment groups • Cannot evaluate the stratification variable

20. Stratified Randomization • For each subgroup or strata perform a separate block randomization • Common strata • Clinical center, Age, Gender • Stratification MUST be taken into account in the data analysis!

21. When to Randomize? • When the treatment must change! • SWOG: 1 vs. 2 years of CMFVP adjuvant chemotherapy in axillary node-positive and estrogen receptor-negative patients. • JCO, Vol 11 No. 9 (Sept), 1993

22. Randomize at the Time Trial Arms Diverge • SWOG randomized at beginning of treatment • Discontinued treatment before relapse or death • 17% on 1 year arm • 59% on 2 year arm • Main reason was patient refusal

23. Outline • Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles

24. Types of Randomized Studies • Parallel Group • Sequential Trials • Group Sequential trials • Cross-over • Factorial Designs

25. Parallel Group • Randomize patients to one of k treatments • Response • Measure at end of study • Delta or % change from baseline • Repeated measures • Function of multiple measures

26. Ideal Study - Gold Standard • Double blind • Randomized • Parallel groups

27. Sequential Trials • Not for a fixed period • Terminates when • One treatment shows a clear superiority or • It is highly unlikely any important difference will be seen • Special statistical design methods

28. Group Sequential Trials • Popular • Analyze data after certain proportions of results available • Early stopping • If one treatment clearly superior • Adverse events • Careful planning and statistical design

29. Crossover Trial • E.g. 2 treatments: 2 period crossover • Use each patient as own control • Must eliminate carryover effects • Need sufficient washout period

30. Factorial Design • Each level of a factor (treatment or condition) occurs with every level of every other factor • Selenomethionine and Celecoxib Gastroenterology 2002; 122:A71

31. Incomplete Factorial Trial • Nutritional Intervention Trial (NIT) • 4x4 incomplete factorial • A,B,C,D • Did not look at all possible interactions • Not of interest (at the time) • Sample size prohibitive

32. Outline • Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles

33. Can ONLY show Association You will never know all the possible confounders! Can show Association and Causality Well done randomization means unknown confounders should not create problems Observational Experimental

34. Observational Studies • Cohort Study • Follow a group for a while • Cardiovascular Health Study • Case-Control Study • Groups with or without outcome • Determine who was exposed to risk factor

35. Observational Studies • Cross-sectional • Collect a representative sample • Simultaneously classify by outcome and risk factor Outcome No disease Disease Y Risk Factor N

36. Observational Studies are Useful • May be only alternative • Smoking in humans • What happens in free living people (Cardiovascular Health Study) • May be cheaper and faster than a trial

37. Do not always agree • HRT • Observational trials • WHI • Publication bias?

38. Outline • Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles

39. Nonrandomized Experimental Studies • No control group • Early in investigation • Concurrent control “group” • Treatment assignment not by randomization • Historically controlled • Missing/poor data • Non-comparability of groups

40. No placebo/control = problems • Patients tend to do better by receiving some treatment, even placebo or standard of care (soc) • Comparing a patient on treatment to baseline does not take this into account

41. Additional Problems • Researchers tend to interpret findings in favor of the new treatment • Investigator bias • Impossible to distinguish the effect of time from treatment effects • Confounding

42. Human Assumptions and Concurrent Control Groups • Newer = better • Systematic allocation is unreliable and many times NOT systematic • Bias • Manipulation • No randomization  impossible to establish if comparable groups

43. Historical Control Study • Small patient pool • Pediatrics • Cancer research • Responses compared to controls from previous studies. • Only half the patients • No “placebo exposure”

44. Historical Control Problems • Serious bias for assessing treatment efficacy • Controls not a good comparison group

45. Historical Controls and Time • Treatments, technology, patient care changed over time • Patient population characteristics have changed over time

46. Non-randomized Phase II design problems • Placebo effect • Investigator bias • Unblinded treatment • Regression to the mean • Natural reduction in disease activity over time

47. Observational Studies • Why can observational studies only find a weaker degree of connection? • Subject to confounding • Can correct for what you know, but nothing to be done about the unknown • Sometimes it is unethical to do a randomized trial (e.g. smoking)

48. Causation vs. Association • Causation • Established by experimental studies and clinical trials • Association • Observation studies can merely find association between a risk factor and an response

49. Outline • Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles

50. Statistical Resources • Software • Books • Articles, etc.