Biostatistics Advance Access originally published online on July 28, 2006
Biostatistics 2007 8(2):368-382; doi:10.1093/biostatistics/kxl016
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Regression analysis of mean quality-adjusted lifetime with censored data
Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia 22908, USA hkwang{at}virginia.edu
Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA
* To whom correspondence should be addressed.
In clinical trials of chronic diseases such as acquired immunodeficiency syndrome, cancer, or cardiovascular diseases, the concept of quality-adjusted lifetime (QAL) has received more and more attention. In this paper, we consider the problem of how the covariates affect the mean QAL when the data are subject to right censoring. We allow a very general form for the mean model as a function of covariates. Using the idea of inverse probability weighting, we first construct a simple weighted estimating equation for the parameters in our mean model. We then find the form of the most efficient estimating equation, which yields the most efficient estimator for the regression parameters. Since the most efficient estimator depends on the distribution of the health history processes, and thus cannot be estimated nonparametrically, we consider different approaches for improving the efficiency of the simple weighted estimating equation using observed data. The applicability of these methods is demonstrated by both simulation experiments and a data example from a breast cancer clinical trial study.
Keywords: Counting process; Estimating equation; Martingale process; Quality of life; Survival analysis
Received February 2, 2006; revised June 22, 2006; accepted for publication December 21, 2006.