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Semiparametric estimation exploiting covariate independence in two-phase randomized trials.

Dai, James Y and LeBlanc, Michael and Kooperberg, Charles (2009) Semiparametric estimation exploiting covariate independence in two-phase randomized trials. Biometrics, 65 (1). pp. 178-187. ISSN 1541-0420

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Article URL: http://www3.interscience.wiley.com/cgi-bin/fulltex...

Abstract

Recent results for case-control sampling suggest when the covariate distribution is constrained by gene-environment independence, semiparametric estimation exploiting such independence yields a great deal of efficiency gain. We consider the efficient estimation of the treatment-biomarker interaction in two-phase sampling nested within randomized clinical trials, incorporating the independence between a randomized treatment and the baseline markers. We develop a Newton-Raphson algorithm based on the profile likelihood to compute the semiparametric maximum likelihood estimate (SPMLE). Our algorithm accommodates both continuous phase-one outcomes and continuous phase-two biomarkers. The profile information matrix is computed explicitly via numerical differentiation. In certain situations where computing the SPMLE is slow, we propose a maximum estimated likelihood estimator (MELE), which is also capable of incorporating the covariate independence. This estimated likelihood approach uses a one-step empirical covariate distribution, thus is straightforward to maximize. It offers a closed-form variance estimate with limited increase in variance relative to the fully efficient SPMLE. Our results suggest exploiting the covariate independence in two-phase sampling increases the efficiency substantially, particularly for estimating treatment-biomarker interactions.

Item Type: Article
Additional Information: This article is available to subscribers only via the URL above.
DOI: 10.1111/j.1541-0420.2008.01046.x
PubMed ID: 18479485
NIHMSID: NIHMS107483
PMCID: PMC2892338
Grant Numbers: R01 CA074841-09, U01 CA125489-03, P01 CA053996-31
Keywords or MeSH Headings: * Algorithms * Biological Markers * Biometry/methods* * Data Interpretation, Statistical * Humans * Models, Theoretical * Randomized Controlled Trials as Topic/statistics & numerical data* * Treatment Outcome
Subjects: Research Methodologies > Clinical Trials
Research Methodologies > Mathematics and statistics
Depositing User: Library Staff
Date Deposited: 02 Apr 2009 22:01
Last Modified: 14 Feb 2012 14:42
URI: http://authors.fhcrc.org/id/eprint/272

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