Increasing Power in Randomized Trials with Right Censored Outcomes Through Covariate Adjustment

Targeted maximum likelihood methodology is applied to provide a test that makes use of the covariate data that are commonly collected in randomized trials, and does not require assumptions beyond those of the logrank test when censoring is uninformative. Under informative censoring, the logrank test...

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Bibliographic Details
Published inJournal of biopharmaceutical statistics Vol. 19; no. 6; pp. 1099 - 1131
Main Authors Moore, K. L., van der Laan, M. J.
Format Journal Article
LanguageEnglish
Published England Taylor & Francis Group 01.11.2009
Subjects
Algorithms
Clinical trials
Computer Simulation
Covariate adjustment
Data Interpretation, Statistical
Humans
Likelihood Functions
Logrank test
Randomized Controlled Trials as Topic - statistics & numerical data
Targeted maximum likelihood estimation
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Summary:Targeted maximum likelihood methodology is applied to provide a test that makes use of the covariate data that are commonly collected in randomized trials, and does not require assumptions beyond those of the logrank test when censoring is uninformative. Under informative censoring, the logrank test is biased, whereas the test provided in this article is consistent under consistent estimation of the censoring mechanism or the conditional hazard for survival. Two approaches based on this methodology are provided: (1) a substitution-based approach that targets treatment and time-specific survival from which the logrank parameter is estimated, and (2) directly targeting the logrank parameter.
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ISSN:1054-3406
1520-5711
DOI:10.1080/10543400903243017