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Estimation and comparison of rates of change in longitudinal studies with informative drop‐outs
Author(s) -
Touloumi G.,
Pocock S. J.,
Babiker A. G.,
Darbyshire J. H.
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19990530)18:10<1215::aid-sim118>3.0.co;2-6
Subject(s) - missing data , attrition , covariance , statistics , computer science , drop out , econometrics , mathematics , medicine , dentistry , economics , demographic economics
Many cohort studies and clinical trials have designs which involve repeated measurements of disease markers. One problem in such longitudinal studies, when the primary interest is to estimate and to compare the evolution of a disease marker, is that planned data are not collected because of missing data due to missing visits and/or withdrawal or attrition (for example, death). Several methods to analyse such data are available, provided that the data are missing at random. However, serious biases can occur when missing ness is informative. In such cases, one needs to apply methods that simultaneously model the observed data and the missingness process. In this paper we consider the problem of estimation of the rate of change of a disease marker in longitudinal studies, in which some subjects drop out prematurely (informatively) due to attrition, while others experience a non‐informative drop‐out process (end of study, withdrawal). We propose a method which combines a linear random effects model for the underlying pattern of the marker with a log‐normal survival model for the informative drop‐out process. Joint estimates are obtained through the restricted iterative generalized least squares method which are equivalent to restricted maximum likelihood estimates. A nested EM algorithm is applied to deal with censored survival data. The advantages of this method are: it provides a unified approach to estimate all the model parameters; it can effectively deal with irregular data (that is, measured at irregular time points), a complicated covariance structure and a complex underlying profile of the response variable; it does not entail such complex computation as would be required to maximize the joint likelihood. The method is illustrated by modelling CD4 count data in a clinical trial in patients with advanced HIV infection while its performance is tested by simulation studies. Copyright © 1999 John Wiley & Sons, Ltd.