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Relative risk regression for current status data in case‐cohort studies

Title: Relative risk regression for current status data in case‐cohort studies
Authors: Li, Zhiguo; Nan, Bin
Contributors: Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA; Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.
Publisher Information: John Wiley & Sons, Inc.
Publication Year: 2011
Collection: University of Michigan: Deep Blue
Subject Terms: Estimated Weights; Interval Censoring; Missing Covariates; Two‐Phase Design; Weighted Bootstrap; Weighted Likelihood; Primary 62E20; 62N02; Secondary 62D05; Statistics and Numeric Data; Science
Description: We propose using the weighted likelihood method to fit a general relative risk regression model for the current status data with missing data as arise, for example, in case‐cohort studies. The missingness probability is either known or can be reasonably estimated. Asymptotic properties of the weighted likelihood estimators are established. For the case of using estimated weights, we construct a general theorem that guarantees the asymptotic normality of the M‐estimator of a finite dimensional parameter in a class of semiparametric models, where the infinite dimensional parameter is allowed to converge at a slower than parametric rate, and some other parameters in the objective function are estimated a priori. The weighted bootstrap method is employed to estimate the variances. Simulations show that the proposed method works well for finite sample sizes. A motivating example of the case‐cohort study from an HIV vaccine trial is used to demonstrate the proposed method. The Canadian Journal of Statistics 39: 557–577; 2011. © 2011 Statistical Society of Canada Nous proposons d'utiliser la méthode de vraisemblance pondérée pour ajuster un modèle de régression général pour le risque relatif sur des données de statut présent avec données man‐quantes. Une telle situation se produit dans les études cas‐cohorte. La probabilité d'être manquante est connue ou bien elle peut être estimée de façon raisonnable. Les propriétés asymptotiques des estimateurs de vraisemblance pondérée sont obtenues. Lorsque des poids estimés sont utilisés, nous obtenons un théorème général garantissant la normalité asymptotique du M‐estimateur d'un pa‐ramètre de dimension fini appartenant à une classe de modèles semi‐paramétriques, pour laquelle le paramètre de dimension infinie peut converger à un taux plus lent que le taux paramétrique, et que d'autres paramètres de la fonction objective sont estimés a priori La méthode d'auto‐amorçage pondérée est utilisée pour estimer les variances. Des simulations montrent que la méthode proposée fonctionne ...
Document Type: article in journal/newspaper
File Description: application/pdf
Language: unknown
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DOI: 10.1002/cjs.10111
Availability: https://hdl.handle.net/2027.42/88021; https://doi.org/10.1002/cjs.10111
Rights: IndexNoFollow
Accession Number: edsbas.6DBAC8EA
Database: BASE