Article
Semiparametric regression analysis of longitudinal data with informative drop-outs

Authors: Lin D.Y.¹; Ying Z.²

Source: Biostatistics, Volume 4, Number 3, 1 July 2003 , pp. 385-398(14)

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Abstract:

Informative drop-out arises in longitudinal studies when the subject's follow-up time depends on the unobserved values of the response variable. We specify a semiparametric linear regression model for the repeatedly measured response variable and an accelerated failure time model for the time to informative drop-out. The error terms from the two models are assumed to have a common, but completely arbitrary joint distribution. Using a rank-based estimator for the accelerated failure time model and an artificial censoring device, we construct an asymptotically unbiased estimating function for the linear regression model. The resultant estimator is shown to be consistent and asymptotically normal. A resampling scheme is developed to estimate the limiting covariance matrix. Extensive simulation studies demonstrate that the proposed methods are suitable for practical use. Illustrations with data taken from two AIDS clinical trials are provided.

Keywords: Artificial censoring; Counting process; Dependent censoring; Linear regression; Missing data; Repeated measures

Document Type: Original article

Affiliations: 1: Department of Biostatistics, University of North Carolina, CB#7420 McGavran-Greenberg, Chapel Hill, NC 27599-7420, USA lin@bios.unc.edu 2: Department of Statistics, 618 Mathematics, Columbia University, New York, NY 10027, USA zying@stat.columbia.edu

Publication date: 2003-07-01

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Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public's health.