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Issues in Randomization

Issues in Randomization

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Issues in Randomization

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  1. Issues in Randomization Laura Lee Johnson, Ph.D. Biostatistician National Cancer Institute Introduction to the Principles and Practice of Clinical Research Monday, October 25, 2004

  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.