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## Issues in Randomization

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**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**Biostatistics**• Randomization • Hypothesis Testing • Sample Size and Power • Survival Analysis**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**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**Outline**• Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Statistical Software, Books, Articles**Words I Might Use**• Phase I, II, IIb, III, IV Trial • Model: y = β0 + β1x1 + β2x2 + ε • Covariate • Effect Modifier • Confounder**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**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.**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.**How to handle confounding**• If you know something is a possible confounder, in the data analysis use • Stratification, or • Adjustment • Fear the unknown!**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**Outline**• Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles**Randomization: Definition**• Random Allocation • known chance receiving a treatment • cannot predict the treatment to be given • Eliminate Selection Bias • Similar Treatment Groups**ONE Factor is Different**• Randomization tries to ensure that ONE factor is different between two or more groups. • Observe the Consequences • Attribute Causality**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**Types of Randomization**• Simple • Blocked Randomization • Stratified Randomization**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**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**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**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!**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**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**Outline**• Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles**Types of Randomized Studies**• Parallel Group • Sequential Trials • Group Sequential trials • Cross-over • Factorial Designs**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**Ideal Study - Gold Standard**• Double blind • Randomized • Parallel groups**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**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**Crossover Trial**• E.g. 2 treatments: 2 period crossover • Use each patient as own control • Must eliminate carryover effects • Need sufficient washout period**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**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**Outline**• Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles**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**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**Observational Studies**• Cross-sectional • Collect a representative sample • Simultaneously classify by outcome and risk factor Outcome No disease Disease Y Risk Factor N**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**Do not always agree**• HRT • Observational trials • WHI • Publication bias?**Outline**• Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles**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**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**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**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**Historical Control Study**• Small patient pool • Pediatrics • Cancer research • Responses compared to controls from previous studies. • Only half the patients • No “placebo exposure”**Historical Control Problems**• Serious bias for assessing treatment efficacy • Controls not a good comparison group**Historical Controls and Time**• Treatments, technology, patient care changed over time • Patient population characteristics have changed over time**Non-randomized Phase II design problems**• Placebo effect • Investigator bias • Unblinded treatment • Regression to the mean • Natural reduction in disease activity over time**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)**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**Outline**• Introductory Statistical Definitions • What is Randomization? • Randomized Study Design • Experimental vs. Observational • Non-Randomized Study Design • Stat Software, Books, Articles**Statistical Resources**• Software • Books • Articles, etc.