Sensitivity analyses for partially observed recurrent event data

Akacha, Mouna and Ogundimu, Emmanuel (2015) Sensitivity analyses for partially observed recurrent event data. Pharmaceutical Statistics, 15 (1). pp. 4-14. ISSN 1539-1604

Full text not available from this repository. (Request a copy)
Official URL: http://dx.doi.org/10.1002/pst.1720

Abstract

Recurrent events involve the occurrences of the same type of event repeatedly over time and are commonly encountered in longitudinal studies. Examples include seizures in epileptic studies or occurrence of cancer tumors. In such studies, interest lies in the number of events that occur over a fixed period of time. One considerable challenge in analyzing such data arises when a large proportion of patients discontinues before the end of the study, for example, because of adverse events, leading to partially observed data. In this situation, data are often modeled using a negative binomial distribution with time‐in‐study as offset. Such an analysis assumes that data are missing at random (MAR). As we cannot test the adequacy of MAR, sensitivity analyses that assess the robustness of conclusions across a range of different assumptions need to be performed.

Sophisticated sensitivity analyses for continuous data are being frequently performed. However, this is less the case for recurrent event or count data. We will present a flexible approach to perform clinically interpretable sensitivity analyses for recurrent event data. Our approach fits into the framework of reference‐based imputations, where information from reference arms can be borrowed to impute post‐discontinuation data. Different assumptions about the future behavior of dropouts dependent on reasons for dropout and received treatment can be made. The imputation model is based on a flexible model that allows for time‐varying baseline intensities. We assess the performance in a simulation study and provide an illustration with a clinical trial in patients who suffer from bladder cancer.

Item Type: Article
Uncontrolled Keywords: count data, missing data, pattern‐mixture models, recurrent event data, sensitivity analyses
Subjects: G300 Statistics
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Becky Skoyles
Date Deposited: 26 Jun 2018 12:06
Last Modified: 26 Jun 2018 12:06
URI: http://nrl.northumbria.ac.uk/id/eprint/34711

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence