Clinical Value of Predicting Individual Treatment Effects for Intensive Blood Pressure Therapy.

Journal: Circulation. Cardiovascular quality and outcomes
Published Date: Mar 1, 2019

Abstract

BACKGROUND: The absolute risk reduction (ARR) in cardiovascular events from therapy is generally assumed to be proportional to baseline risk-such that high-risk patients benefit most. Yet newer analyses have proposed using randomized trial data to develop models that estimate individual treatment effects. We tested 2 hypotheses: first, that models of individual treatment effects would reveal that benefit from intensive blood pressure therapy is proportional to baseline risk; and second, that a machine learning approach designed to predict heterogeneous treatment effects-the X-learner meta-algorithm-is equivalent to a conventional logistic regression approach.

Authors

  • Tony Duan
    Department of Computer Science, Stanford University, Stanford, California, United States of America.
  • Pranav Rajpurkar
    Harvard Medical School, Department of Biomedical Informatics, Cambridge, MA, 02115, US.
  • Dillon Laird
    Department of Computer Science (T.D., P.R., D.L., A.Y.N.), Stanford University, Stanford, CA.
  • Andrew Y Ng
  • Sanjay Basu
    Center for Primary Care and Outcomes Research, Center for Population Health Sciences, Departments of Medicine and Health Research and Policy, Stanford University, Palo Alto, CA basus@stanford.edu.

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