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    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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Identification of Predicted Individual Treatment Effects (PITE) in randomized clinical trials

Research output: Contribution to journalJournal articlepeer-review

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Identification of Predicted Individual Treatment Effects (PITE) in randomized clinical trials. / Lamont, Andrea E.; Lyons, Mike; Jaki, Thomas Friedrich; Stuart, E. A.; Feaster, Daniel; Ishwaran, H.; Tharmaratnam, Kukatharmini; Van Horn, M. Lee.

In: Statistical Methods in Medical Research, Vol. 27, No. 1, 01.01.2018, p. 142-157.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Lamont, AE, Lyons, M, Jaki, TF, Stuart, EA, Feaster, D, Ishwaran, H, Tharmaratnam, K & Van Horn, ML 2018, 'Identification of Predicted Individual Treatment Effects (PITE) in randomized clinical trials', Statistical Methods in Medical Research, vol. 27, no. 1, pp. 142-157. https://doi.org/10.1177/0962280215623981

APA

Lamont, A. E., Lyons, M., Jaki, T. F., Stuart, E. A., Feaster, D., Ishwaran, H., Tharmaratnam, K., & Van Horn, M. L. (2018). Identification of Predicted Individual Treatment Effects (PITE) in randomized clinical trials. Statistical Methods in Medical Research, 27(1), 142-157. https://doi.org/10.1177/0962280215623981

Vancouver

Lamont AE, Lyons M, Jaki TF, Stuart EA, Feaster D, Ishwaran H et al. Identification of Predicted Individual Treatment Effects (PITE) in randomized clinical trials. Statistical Methods in Medical Research. 2018 Jan 1;27(1):142-157. https://doi.org/10.1177/0962280215623981

Author

Lamont, Andrea E. ; Lyons, Mike ; Jaki, Thomas Friedrich ; Stuart, E. A. ; Feaster, Daniel ; Ishwaran, H. ; Tharmaratnam, Kukatharmini ; Van Horn, M. Lee. / Identification of Predicted Individual Treatment Effects (PITE) in randomized clinical trials. In: Statistical Methods in Medical Research. 2018 ; Vol. 27, No. 1. pp. 142-157.

Bibtex

@article{03add89ce3ee4e94a038db0f5e9e2a7d,
title = "Identification of Predicted Individual Treatment Effects (PITE) 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.",
author = "Lamont, {Andrea E.} and Mike Lyons and Jaki, {Thomas Friedrich} and Stuart, {E. A.} and Daniel Feaster and H. Ishwaran and Kukatharmini Tharmaratnam and {Van Horn}, {M. Lee}",
note = "The final, definitive version of this article has been published in the Journal, Statistical Methods in Medical Research, 27 (1), 2018, {\textcopyright} 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/ ",
year = "2018",
month = jan,
day = "1",
doi = "10.1177/0962280215623981",
language = "English",
volume = "27",
pages = "142--157",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",
number = "1",

}

RIS

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 -