Regression analysis of panel count data with covariate-dependent observation and censoring times

• Published in: Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 62; no. 2; pp. 293 - 302
• Main Authors: Sun, J., Wei, L. J.
• Format: Journal Article
• Language: English
• Published: Oxford, UK and Boston, USA Blackwell Publishers Ltd 2000; Blackwell Publishers; Blackwell; Royal Statistical Society
• Series: Journal of the Royal Statistical Society Series B
• Subjects: Biometrics; Cancer; Counting process; Covariance matrices; Cox model; Data analysis; Estimating function; Estimation; Exact sciences and technology; Health economics; Linear inference, regression; Mathematical economics; Mathematics; Neoplasia; Placebos; Probability and statistics; Recurrent events; Regression analysis; Sciences and techniques of general use; Standard error; Statistics; Time dependence; Unbiased estimators; Urinary bladder; Mean function; Recurrent event; Independence; Gaussian distribution; Non parametric estimation; Regression analysis; Covariance matrix; Covariate; Poisson process; Observation time; Counting process; Estimating function; Cox model
• ISSN: 1369-7412; 1467-9868
• DOI: 10.1111/1467-9868.00232

Panel count data often occur in a long-term study where the primary end point is the time to a specific event and each subject may experience multiple recurrences of this event. Furthermore, suppose that it is not feasible to keep subjects under observation continuously and the numbers of recurrences for each subject are only recorded at several distinct time points over the study period. Moreover, the set of observation times may vary from subject to subject. In this paper, regression methods, which are derived under simple semiparametric models, are proposed for the analysis of such longitudinal count data. Especially, we consider the situation when both observation and censoring times may depend on covariates. The new procedures are illustrated with data from a well-known cancer study.