Algorithms for imputing partially observed recurrent events with applications to multiple imputation in pattern mixture models. Issue 3 (4th May 2018)

• Record Type: Journal Article
• Authors: Tang, Yongqiang
• Is Part Of: Journal of biopharmaceutical statistics. Volume 28:Issue 3(2018)
• Page Start: 518
• Page End: 533
• Publication Date: 2018-05-04
• Subjects: Mixed poisson process -- negative binomial process -- overdispersion -- pattern mixture models -- rejection sampling; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2017.1333999
• Languages: English
• ISSNs: 1054-3406

Abstract

ABSTRACT: Five algorithms are described for imputing partially observed recurrent events modeled by a negative binomial process, or more generally by a mixed Poisson process when the mean function for the recurrent events is continuous over time. We also discuss how to perform the imputation when the mean function of the event process has jump discontinuities. The validity of these algorithms is assessed by simulations. These imputation algorithms are potentially very useful in the implementation of pattern mixture models, which have been popularly used as sensitivity analysis under the non-ignorability assumption in clinical trials. A chronic granulomatous disease trial is analyzed for illustrative purposes.