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



Kolamunnage-Dona, R ORCID: 0000-0003-3886-6208, Powell, C and Williamson, PR ORCID: 0000-0001-9802-6636
(2016) Modelling variable dropout in randomised controlled trials with longitudinal outcomes: Application to the MAGNETIC study. Trials, 17.

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Abstract

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
Uncontrolled Keywords: Longitudinal outcome, Dropout process, Joint modelling, Competing risks
Depositing User: Symplectic Admin
Date Deposited: 04 Jan 2019 15:08
Last Modified: 14 Jun 2021 07:10
DOI: 10.1186/s13063-016-1342-0
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3030796

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