Semiparametric regression analysis of doubly censored recurrent event data

• Published in: Japanese journal of statistics and data science Vol. 7; no. 1; pp. 183 - 202
• Main Authors: Sankaran, P. G., Hari, S., Sreedevi, E. P.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2024; Springer Nature B.V
• Subjects: Chemistry and Earth Sciences; Computer Science; Economics; Finance; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Original Paper; Physics; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Mean function; Counting process; Double censoring; Recurrent events
• ISSN: 2520-8756; 2520-8764
• DOI: 10.1007/s42081-023-00234-x

Recurrent event data are common in survival and reliability studies, where a subject experiences the same type of event repeatedly. There are situations, in which the event of interest can be observed only if they belong to a window of observational range, leading to double censoring of recurrent event times. In this paper, we study recurrent event data subject to double censoring. We propose a proportional mean model for the analysis of doubly censored recurrent event data based on the mean function of the underlying recurrent event process. The estimators of the regression parameters and the baseline mean function are derived and their asymptotic properties are studied. A Monte Carlo simulation study is conducted to assess the finite sample behavior of the proposed estimators. Finally, the procedures are illustrated using two real-life data sets, one from a bladder cancer study and the other from a study on chronic granulomatous disease.