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Modelling variable dropout in randomised controlled trials with longitudinal outcomes: application to the MAGNETIC study

Kolamunnage-Dona, Ruwanthi, Powell, Colin and Williamson, Paula Ruth 2016. Modelling variable dropout in randomised controlled trials with longitudinal outcomes: application to the MAGNETIC study. Trials 17 (1) , pp. 222-231. 10.1186/s13063-016-1342-0

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Background Clinical trials with longitudinally measured outcomes are often plagued by missing data due to patients withdrawing or dropping out from the trial before completing the measurement schedule. The reasons for dropout are sometimes clearly known and recorded during the trial, but in many instances these reasons are unknown or unclear. Often such reasons for dropout are non-ignorable. However, the standard methods for analysing longitudinal outcome data assume that missingness is non-informative and ignore the reasons for dropout, which could result in a biased comparison between the treatment groups. Methods In this article, as a post hoc analysis, we explore the impact of informative dropout due to competing reasons on the evaluation of treatment effect in the MAGNETIC trial, the largest randomised placebo-controlled study to date comparing the addition of nebulised magnesium sulphate to standard treatment in acute severe asthma in children. We jointly model longitudinal outcome and informative dropout process to incorporate the information regarding the reasons for dropout by treatment group. Results The effect of nebulised magnesium sulphate compared with standard treatment is evaluated more accurately using a joint longitudinal-competing risk model by taking account of such complexities. The corresponding estimates indicate that the rate of dropout due to good prognosis is about twice as high in the magnesium group compared with standard treatment. Conclusions We emphasise the importance of identifying reasons for dropout and undertaking an appropriate statistical analysis accounting for such dropout. The joint modelling approach accounting for competing reasons for dropout is proposed as a general approach for evaluating the sensitivity of conclusions to assumptions regarding missing data in clinical trials with longitudinal outcomes.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: R Medicine > R Medicine (General)
Uncontrolled Keywords: Longitudinal outcome, Dropout process, Joint modelling, Competing risks
Publisher: BioMed Central
ISSN: 1745-6215
Funders: NIHR HTA
Date of First Compliant Deposit: 6 June 2017
Date of Acceptance: 13 April 2016
Last Modified: 05 May 2023 14:14

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