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Identification of predicted individual treatment effects in randomized clinical trials

TitleIdentification of predicted individual treatment effects in randomized clinical trials
Publication TypeJournal Article
Year of Publication2018
AuthorsLamont, A, Lyons, MD, Jaki, T, Stuart, E, Feaster, DJ, Tharmaratnam, K, Oberski, D, Ishwaran, H, Wilson, DK, Van Horn, ML
JournalStatistical Methods in Medical Research
Type of ArticleArticle
ISBN Number09622802 (ISSN)
KeywordsAdult, article, conceptual framework, controlled study, decision tree, Female, heterogeneity in treatment effects, human, Individual predictions, Individualized Medicine, Male, Monte Carlo method, Multiple imputation, personalized medicine, Predicted individual treatment effects, prediction, Random decision trees, random forest, Random forests, randomized controlled trial, treatment response

In most medical research, treatment effectiveness is assessed using the average treatment effect or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as predicted individual treatment effects. We first apply the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the predicted individual treatment effect approach. The predicted individual treatment effects can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of predicted individual treatment effects. We compare the performance of two methods used to obtain predictions: multiple imputation and nonparametric random decision trees. Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the random decision trees tended to underestimate the predicted individual treatment effect for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed. © The Author(s) 2016.