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Identification of the optimal treatment regimen in the presence of missing covariates.
- Source :
- Statistics in Medicine; 2/20/2020, Vol. 39 Issue 4, p353-368, 16p
- Publication Year :
- 2020
-
Abstract
- Covariates associated with treatment-effect heterogeneity can potentially be used to make personalized treatment recommendations towards best clinical outcomes. Methods for treatment-selection rule development that directly maximize treatment-selection benefits have attracted much interest in recent years, due to the robustness of these methods to outcome modeling. In practice, the task of treatment-selection rule development can be further complicated by missingness in data. Here, we consider the identification of optimal treatment-selection rules for a binary disease outcome when measurements of an important covariate from study participants are partly missing. Under the missing at random assumption, we develop a robust estimator of treatment-selection rules under the direct-optimization paradigm. This estimator targets the maximum selection benefits to the population under correct specification of at least one mechanism from each of the two sets-missing data or conditional covariate distribution, and treatment assignment or disease outcome model. We evaluate and compare performance of the proposed estimator with alternative direct-optimization estimators through extensive simulation studies. We demonstrate the application of the proposed method through a real data example from an Alzheimer's disease study for developing covariate combinations to guide the treatment of Alzheimer's disease. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02776715
- Volume :
- 39
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Statistics in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 141152904
- Full Text :
- https://doi.org/10.1002/sim.8407