Properties of the Estimators of the Cox Regression Model with Imputed Data

Luciana Carla Chiapella, Marta Beatriz Quaglino () and María Eugenia Mamprin
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Luciana Carla Chiapella: Universidad Nacional de Rosario, CONICET
Marta Beatriz Quaglino: Instituto de Investigaciones Teóricas y Aplicadas (Escuela de Estadística), Universidad Nacional de Rosario
María Eugenia Mamprin: Universidad Nacional de Rosario, CONICET

Statistics in Biosciences, 2023, vol. 15, issue 2, No 2, 330-352

Abstract: Abstract Cox regression is one of the most commonly used methods in biomedical research when studying the relationship between a set of covariates and the time up to the occurrence of an event of interest. In research studies, it is not surprising to find missing data, which may compromise the well-known asymptotic properties of the estimators and lead to wrong inferences. In this paper, we present the results of an extensive simulation study on the impact of different methods for the treatment of missing data in estimating the parameters of a Cox model with mixed covariates. The study considers different mechanisms and proportions of missing data and different sample sizes. A variety of five methods are applied for the treatment of missing data and the distributional properties of the estimators of the model parameters; their predictive capacity and the precision of the imputations are compared. In general, the publications that compare imputation techniques in the context of Cox models do so using complete case analysis or multiple imputation. In this paper, the consideration of some flexible imputation methods is proposed. These methods have been shown to provide acceptable results, so their consideration is recommended in cases similar to those raised in this study. Finally, a real motivating case is introduced and the results of the analysis of its information are presented, following the guidelines that arise from the recommendations derived from the simulation study.

Keywords: Missing data; Cox regression; Imputation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-022-09361-7

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