title: Identification of predicted individual treatment effects in randomized clinical trials.
abstract: 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 non-parametric 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.
authors.affiliation: 1 Department of Psychology, Barnwell College, University of South Carolina, Columbia, USA.
authors.forename: Andrea
authors.initials: A
authors.lastname: Lamont
authors.affiliation: 2 Department of Psychology, University of Houston, Houston, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Lyons
authors.affiliation: 3 Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: Jaki
authors.affiliation: 4 Department of Mental Health, Department of Biostatistics, and Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins, Baltimore, MD, USA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Stuart
authors.affiliation: 5 Department of Public Health Sciences, Division of Biostatistics, University of Miami, Miami, FL, USA.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Feaster
authors.affiliation: 6 Lancaster University Fylde College, Lancaster University, Lancaster, UK.
authors.forename: Kukatharmini
authors.initials: K
authors.lastname: Tharmaratnam
authors.affiliation: 7 Department of Methodology and Statistics, Tilburg University, Tilburg, the Netherlands.
authors.forename: Daniel
authors.initials: D
authors.lastname: Oberski
authors.affiliation: 5 Department of Public Health Sciences, Division of Biostatistics, University of Miami, Miami, FL, USA.
authors.forename: Hemant
authors.initials: H
authors.lastname: Ishwaran
authors.affiliation: 1 Department of Psychology, Barnwell College, University of South Carolina, Columbia, USA.
authors.forename: Dawn K
authors.initials: DK
authors.lastname: Wilson
authors.affiliation: 8 Department of Individual, Family and Community Education, University of New Mexico, Albuquerque, NM, USA.
authors.forename: M Lee
authors.initials: ML
authors.lastname: Van Horn
mesh_terms: D003663:Decision Trees; D005544:Forecasting; D006801:Humans; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D016896:Treatment Outcome
keywords: Predicted individual treatment effects; heterogeneity in treatment effects; individual predictions; individualized medicine; multiple imputation; random decision trees; random forests