Subgroups from regression trees with adjustment for prognostic effects and postselection inference

Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounde...

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Vydané v:Statistics in medicine Ročník 38; číslo 4; s. 545 - 557
Hlavní autori: Loh, Wei‐Yin, Man, Michael, Wang, Shuaicheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Wiley Subscription Services, Inc 20.02.2019
Predmet:
Algorithms
bootstrap
Bootstrap method
Breast cancer
Clinical trials
differential treatment effect
Humans
Kaplan-Meier Estimate
Medical prognosis
Medical statistics
Medical treatment
missing value
Models, Statistical
Precision medicine
Prognosis
Randomized Controlled Trials as Topic - methods
Regression Analysis
Treatment Outcome
precision medicine
missing value
differential treatment effect
bootstrap
ISSN:0277-6715, 1097-0258, 1097-0258
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Shrnutí:Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal‐theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored.
Bibliografia:ObjectType-Article-1
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.7677