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LECTURE 05 2011

# LECTURE 05 2011 - Stratification Are you a lumper or a...

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Stratification: Are you a lumper or a splitter?

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…and if you are a splitter, how should you split the data and when?
Outline of Stratification Lectures Definitions, examples and rationale (credibility) Implementation Fixed allocation (permuted blocks) Adaptive (minimization) Rationale - variance reduction Pre- and post-stratification

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Stratification in randomized trials is different from stratified random sampling where the population might be divided up into strata, e.g., census tracts, and each stratum is sampled randomly for some pre-specified sample size.
Stratification A procedure in which factors known to be associated with the response (prognostic factors) are taken into account in the design (e.g., randomization) Another type of restriction on the randomization. Goal of permuted block randomization is to achieve balance on the number in each treatment arm over time. Goal of stratification is to achieve balance between groups with respect to important prognostic factors. Pre-stratification refers to a stratified design; post- stratification refers to the analysis

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Post-stratification (def.) Classification of experimental units into strata after they have been randomized for the purpose of data analysis e.g., stratified analysis of variance (normally distributed response), Mantel-Haenszel (binary response). Often adjustment for baseline covariates is carried out using regression methods, e.g., linear regression or analysis of covariance (continuous), logistic regression (binary), or Cox regression (time to event) This can be done irrespective of whether you employed pre-stratification. Note: The term post-stratification is sometimes used to describe stratification on data collected post-randomization. Such analyses can be very difficult to interpret. More later on that issue.
General Problems/Issues with Post- Stratification Model dependence / data dredging How were covariates (stratifying variables) selected? How were cut-points (metric) chosen? Frequently covariates are not pre-specified Partial solution: Analysis plan in the protocol that includes all covariates considered important (pre- stratification variables + others); updated analysis plan prior to unblinding the results of the study to investigators.

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Possible Stratification Scenarios Pre- plus post-stratification Pre-stratification only Post-stratification only Neither pre- nor post- stratification Regression adjustment with or without stratification
Advantages Prevents “accidental bias” resulting from mal-distribution of important prognostic variables Increases precision (if stratifying variables are related to outcome) Facilitates subgroup analysis Results less subject to criticism

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International Conference on Harmonization (ICH) Guideline (E-9 Document) “Stratification by important prognostic factors measured at baseline (e.g., severity of disease, age, sex, etc.) may sometimes be valuable in order to promote balanced allocation within strata; this has greater potential benefit in small trials.”
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