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Minimizing imbalances on patient characteristics between treatment groups in randomized trials using classification tree analysis
Author(s) -
Linden Ariel,
Yarnold Paul R.
Publication year - 2017
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.12792
Subject(s) - randomization , covariate , decision tree , tree (set theory) , computer science , treatment and control groups , clinical trial , process (computing) , randomized controlled trial , machine learning , medicine , statistics , mathematics , surgery , mathematical analysis , pathology , operating system
Rationale, aims, and objectives Randomization ensures that treatment groups do not differ systematically in their characteristics, thereby reducing threats to validity that may otherwise explain differences in outcomes. Large observed imbalances in patient characteristics may indicate that selection bias is being introduced into the treatment allocation process. We introduce classification tree analysis (CTA) as a novel algorithmic approach for identifying potential imbalances in characteristics and their interactions when provisionally assigning each new participant to one or the other treatment group. The participant is then permanently assigned to the treatment group that elicits either no or less imbalance than if assigned to the alternate group. Method Using data on participant characteristics from a clinical trial, we compare 3 different treatment allocation approaches: permuted block randomization (the original allocation method), minimization, and CTA. Treatment allocation performance is assessed by examining balance of all 17 patient characteristics between study groups for each of the allocation techniques. Results While all 3 treatment allocation techniques achieved excellent balance on main effect variables, Classification tree analysis further identified imbalances on interactions and in the distributions of some of the continuous variables. Conclusions Classification tree analysis offers an algorithmic procedure that may be used with any randomization methodology to identify and then minimize linear, nonlinear, and interactive effects that induce covariate imbalance between groups. Investigators should consider using the CTA approach as a real‐time complement to randomization for any clinical trial to safeguard the treatment allocation process against bias.