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Semiparametric analysis of panel count data with correlated observation and follow-up times.
He X; Tong X; Sun J
Lifetime Data Anal 2009 Jun; 15(2):177-196
This paper discusses regression analysis of panel count data that often arise in longitudinal studies concerning occurrence rates of certain recurrent events. Panel count data mean that each study subject is observed only at discrete time points rather than under continuous observation. Furthermore, both observation and follow-up times can vary from subject to subject and may be correlated with the recurrent events. For inference, we propose some shared frailty models and estimating equations are developed for estimation of regression parameters. The proposed estimates are consistent and have asymptotically a normal distribution. The finite sample properties of the proposed estimates are investigated through simulation and an illustrative example from a cancer study is provided.
Analytical-methods; Analytical-models; Analytical-processes; Cancer-rates; Exposure-levels; Exposure-methods Mathematical-models; Mortality-surveys; Statistical-analysis; Therapeutic-agents; Treatment; Workplace-studies; Author Keywords: Estimating equation; Informative follow-up time; Informative observation times; Mean function model; Regression analysis
Xin He, Division of Biostatistics, College of Public Health, The Ohio State University, B-116 Starling-Loving Hall, 320 West 10th Avenue, Columbus, OH 43210
Issue of Publication
Transportation, Warehousing and Utilities
Lifetime Data Analysis
University of Maryland - College Park