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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/319

Title: Quantile Regression Methods for Longitudinal Data with Drop-outs: Application to CD4 Cell Counts of Patients Infected with the Human Immunodeficiency Virus
Authors: Lipsitz, Stuart R.
Fitzmaurice, Garrett M.
Molenberghs, Geert
Zhao, Lue Phing
Keywords: Non and semiparametric methods
Longitudinal data
Missing data
Issue Date: 1997
Citation: Applied Statistics, 46(4). p. 463-476
Abstract: Patients infected with the human immunodeficiency virus (HIV) generally experience a decline in their CD4 cell count (a count of certain white blood cells). We describe the use of quantile regression methods to analyse longitudinal data on CD4 cell counts from 1300 patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. It is of scientific interest to determine any treatment differences in the CD4 cell counts over a short treatment period. However, the analysis of the CD4 data is complicated by drop-outs: patients with lower CD4 cell counts at the base-line appear more likely to drop out at later measurement occasions. Motivated by this example, we describe the use of `weighted' estimating equations in quantile regression models for longitudinal data with drop-outs. In particular, the conventional estimating equations for the quantile regression parameters are weighted inversely proportionally to the probability of drop-out. This approach requires the process generating the missing data to be estimable but makes no assumptions about the distribution of the responses other than those imposed by the quantile regression model. This method yields consistent estimates of the quantile regression parameters provided that the model for drop-out has been correctly specified. The methodology proposed is applied to the CD4 cell count data and the results are compared with those obtained from an `unweighted' analysis. These results demonstrate how an analysis that fails to account for drop-outs can mislead.
URI: http://hdl.handle.net/1942/319
DOI: 10.1111/1467-9876.00084
ISI #: A1997YJ64600005
ISSN: 0035-9254
Type: Journal Contribution
Appears in Collections: Research publications

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