Rights statement: The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 27 (1), 2018, © SAGE Publications Ltd, 2018 by SAGE Publications Ltd at the Statistical Methods in Medical Research page: http://journals.sagepub.com/home/smm on SAGE Journals Online: http://journals.sagepub.com/
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Identification of Predicted Individual Treatment Effects (PITE) in randomized clinical trials
AU - Lamont, Andrea E.
AU - Lyons, Mike
AU - Jaki, Thomas Friedrich
AU - Stuart, E. A.
AU - Feaster, Daniel
AU - Ishwaran, H.
AU - Tharmaratnam, Kukatharmini
AU - Van Horn, M. Lee
N1 - The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 27 (1), 2018, © SAGE Publications Ltd, 2018 by SAGE Publications Ltd at the Statistical Methods in Medical Research page: http://journals.sagepub.com/home/smm on SAGE Journals Online: http://journals.sagepub.com/
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 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.
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 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.
U2 - 10.1177/0962280215623981
DO - 10.1177/0962280215623981
M3 - Journal article
VL - 27
SP - 142
EP - 157
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
IS - 1
ER -