A flexible time-varying coefficient rate model for panel count data

Dayu Sun, Yuanyuan Guo, Yang Li, Jianguo Sun and Wanzhu Tu ()
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Dayu Sun: Indiana University School of Medicine and Richard M. Fairbanks School of Public Health
Yuanyuan Guo: Eli Lilly and Company
Yang Li: Indiana University School of Medicine and Richard M. Fairbanks School of Public Health
Jianguo Sun: University of Missouri
Wanzhu Tu: Indiana University School of Medicine and Richard M. Fairbanks School of Public Health

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 4, No 2, 741 pages

Abstract: Abstract Panel count regression is often required in recurrent event studies, where the interest is to model the event rate. Existing rate models are unable to handle time-varying covariate effects due to theoretical and computational difficulties. Mean models provide a viable alternative but are subject to the constraints of the monotonicity assumption, which tends to be violated when covariates fluctuate over time. In this paper, we present a new semiparametric rate model for panel count data along with related theoretical results. For model fitting, we present an efficient EM algorithm with three different methods for variance estimation. The algorithm allows us to sidestep the challenges of numerical integration and difficulties with the iterative convex minorant algorithm. We showed that the estimators are consistent and asymptotically normally distributed. Simulation studies confirmed an excellent finite sample performance. To illustrate, we analyzed data from a real clinical study of behavioral risk factors for sexually transmitted infections.

Keywords: Panel count data; Rate model; Sieve estimation; Time-varying effects; 62N02; 62G05; 62E20 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-024-09630-1

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