Personalized evidence based medicine: predictive approaches to heterogeneous treatment effectsBMJ 2018; 363 doi: https://doi.org/10.1136/bmj.k4245 (Published 10 December 2018) Cite this as: BMJ 2018;363:k4245
- 1Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA
- 2Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, Netherlands
- Correspondence to: D M Kent
The use of evidence from clinical trials to support decisions for individual patients is a form of “reference class forecasting”: implicit predictions for an individual are made on the basis of outcomes in a reference class of “similar” patients treated with alternative therapies. Evidence based medicine has generally emphasized the broad reference class of patients qualifying for a trial. Yet patients in a trial (and in clinical practice) differ from one another in many ways that can affect the outcome of interest and the potential for benefit. The central goal of personalized medicine, in its various forms, is to narrow the reference class to yield more patient specific effect estimates to support more individualized clinical decision making. This article will review fundamental conceptual problems with the prediction of outcome risk and heterogeneity of treatment effect (HTE), as well as the limitations of conventional (one-variable-at-a-time) subgroup analysis. It will also discuss several regression based approaches to “predictive” heterogeneity of treatment effect analysis, including analyses based on “risk modeling” (such as stratifying trial populations by their risk of the primary outcome or their risk of serious treatment-related harms) and analysis based on “effect modeling” (which incorporates modifiers of relative effect). It will illustrate these approaches with clinical examples and discuss their respective strengths and vulnerabilities.
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Contributors: The concepts of this manuscript were discussed among all authors. DMK prepared the initial draft of the manuscript. Substantial revisions were made by all authors.
Funding: This work was partially supported through two Patient-Centered Outcomes Research Institute (PCORI) grants (the Predictive Analytics Resource Center (PARC) (SA.Tufts.PARC.OSCO.2018.01.25) and Methods Award (ME-1606-35555)), as well as by the National Institutes of Health (U01NS086294).
Competing interests: All authors have read and understood BMJ policy on declaration of interests and declare no competing interests.
Provenance and peer review: Commissioned; externally peer reviewed.
Disclosures: All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or Methodology Committee.