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The impact of dropouts on the analysis of dose-finding studies with recurrent event data

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Akacha, Mouna and Benda, Norbert (2010) The impact of dropouts on the analysis of dose-finding studies with recurrent event data. Statistics in Medicine, Vol.29 (No.15). pp. 1635-1646. doi:10.1002/sim.3930 ISSN 0277-6715.

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Official URL: http://dx.doi.org/10.1002/sim.3930

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Abstract

This work is motivated by dose-finding studies, where the number of events per subject within a specified study period form the primary outcome. The aim of the considered studies is to identify the target dose for which the new drug can be shown to be as effective as a competitor medication. Given a pain-related outcome, we expect a considerable number of patients to drop out before the end of the study period. The impact of missingness on the analysis and models for the missingness process must be carefully considered.

The recurrent events are modeled as over-dispersed Poisson process data, with dose as the regressor. Additional covariates may be included. Constant and time-varying rate functions are examined. Based on these models, the impact of missingness on the precision of the target dose estimation is evaluated. Diverse models for the missingness process are considered, including dependence on covariates and number of events. The performances of five different analysis methods are assessed via simulations: a complete case analysis; two analyses using different single imputation techniques; a direct-likelihood analysis and an analysis using pattern-mixture models.

The target dose estimation is robust if the same missingness process holds for the target dose group and the active control group. Furthermore, we demonstrate that this robustness is lost as soon as the missingness mechanisms for the active control and the target dose differ. Of the methods explored, the direct-likelihood approach performs best, even when a missing not at random mechanism holds. Copyright (C) 2010 John Wiley & Sons, Ltd.

Item Type: Journal Article
Subjects: Q Science > QH Natural history > QH301 Biology
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
R Medicine
Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Journal or Publication Title: Statistics in Medicine
Publisher: John Wiley & Sons Ltd.
ISSN: 0277-6715
Official Date: 10 July 2010
Dates:
DateEvent
10 July 2010Published
Volume: Vol.29
Number: No.15
Number of Pages: 12
Page Range: pp. 1635-1646
DOI: 10.1002/sim.3930
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

Data sourced from Thomson Reuters' Web of Knowledge

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