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Change-plane analysis for subgroup detection with a continuous treatment.

Peng Jin1, Wenbin Lu2, Yu Chen3,4

  • 1Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA.

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|September 22, 2022
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Summary
This summary is machine-generated.

This study introduces a new statistical model to find subgroups benefiting differently from continuous treatments. The method helps identify personalized treatment effects for better health outcomes.

Keywords:
double robustnessenvironmental exposureheterogeneitysemiparametric modelspline

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Research Methods

Background:

  • Identifying patient subgroups with differential treatment effects is crucial for personalized medicine.
  • Research on differential treatment effects primarily focuses on binary treatments, leaving continuous treatments understudied.
  • Continuous treatments, like environmental exposures, require specific methods to analyze subgroup effects.

Purpose of the Study:

  • To propose a semiparametric change-plane model for detecting subgroups with differential effects under continuous treatment.
  • To develop a doubly robust test statistic for assessing the existence of such subgroups.
  • To provide a unified framework applicable to various outcome types and offer nonparametric extensions.

Main Methods:

  • Developed a semiparametric change-plane model for continuous treatments.
  • Introduced a doubly robust test statistic valid under partial model misspecification.
  • Established asymptotic distributions for the test statistic under null and local alternative hypotheses.
  • Proposed methods for estimating subgroup-defining parameters when differential effects exist.

Main Results:

  • The proposed testing procedure is valid when either the covariate effect function or the generalized propensity score function is correctly specified.
  • The change-plane parameters can be estimated upon rejection of the null hypothesis of no subgroup.
  • The framework accommodates diverse outcome distributions (e.g., exponential family, time-to-event).
  • Extensive simulations demonstrate the method's performance across various scenarios.

Conclusions:

  • The study presents a robust statistical framework for analyzing differential treatment effects in continuous treatment settings.
  • The proposed methods enhance the ability to identify personalized treatment strategies.
  • The approach is versatile, applicable to various data types and offers extensions for complex scenarios.
  • Demonstrated practical utility through an application to arsenic exposure data.