Control‐based imputation for sensitivity analyses in informative censoring for recurrent event data. (22nd August 2017)

• Record Type: Journal Article
• Authors: Gao, Fei; Liu, Guanghan F.; Zeng, Donglin; Xu, Lei; Lin, Bridget; Diao, Guoqing; Golm, Gregory; Heyse, Joseph F.; Ibrahim, Joseph G.
• Is Part Of: Pharmaceutical statistics. Volume 16:Number 6(2017)
• Page Start: 424
• Page End: 432
• Publication Date: 2017-08-22
• Subjects: bootstrap -- control‐based imputation -- missing data -- multiple imputation -- recurrent event data; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1821
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract : In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended. We implement the control‐based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study.