TY - JOUR
T1 - Identification of predicted individual treatment effects in randomized clinical trials
AU - Lamont, Andrea
AU - Lyons, Michael D.
AU - Jaki, Thomas
AU - Stuart, Elizabeth
AU - Feaster, Daniel J.
AU - Tharmaratnam, Kukatharmini
AU - Oberski, Daniel
AU - Ishwaran, Hemant
AU - Wilson, Dawn K.
AU - Van Horn, M. Lee
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grant MR/L010658/1 from the Medical Research Council of the United Kingdom, awarded to Principal Investigator, Thomas Jaki, PhD.
Publisher Copyright:
© The Author(s) 2016.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Heterogeneity in treatment effects
KW - Individual predictions
KW - Individualized medicine
KW - Multiple imputation
KW - Predicted individual treatment effects
KW - Random decision trees
KW - Random forests
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U2 - 10.1177/0962280215623981
DO - 10.1177/0962280215623981
M3 - Article
C2 - 26988928
AN - SCOPUS:85041407704
SN - 0962-2802
VL - 27
SP - 142
EP - 157
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 1
ER -