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Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding.

ABSTRACT: In this article, we will present statistical methods to assess to what extent the effect of a randomised treatment (versus control) on a time-to-event endpoint might be explained by the effect of treatment on a mediator of interest, a variable that is measured longitudinally at planned visits throughout the trial. In particular, we will show how to identify and infer the path-specific effect of treatment on the event time via the repeatedly measured mediator levels. The considered proposal addresses complications due to patients dying before the mediator is assessed, due to the mediator being repeatedly measured, and due to posttreatment confounding of the effect of the mediator by other mediators. We illustrate the method by an application to data from the LEADER cardiovascular outcomes trial.

SUBMITTER: Vansteelandt S

PROVIDER: S-EPMC6852414 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding.

Vansteelandt Stijn S Linder Martin M Vandenberghe Sjouke S Steen Johan J Madsen Jesper J

Statistics in medicine 20190814 24

In this article, we will present statistical methods to assess to what extent the effect of a randomised treatment (versus control) on a time-to-event endpoint might be explained by the effect of treatment on a mediator of interest, a variable that is measured longitudinally at planned visits throughout the trial. In particular, we will show how to identify and infer the path-specific effect of treatment on the event time via the repeatedly measured mediator levels. The considered proposal addre ...[more]

PMID: 31411779

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Project description:Background: Stepped-wedge designs (SWDs) are currently being used in the investigation of interventions to reduce opioid-related deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and COVID-19 social distancing mandates. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods: We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of components of the intervention, and time-varying effect modificationâ€" in which external factors differentially impact intervention and control clusters. Results: In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions: Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects; misspecification can result in bias of the intervention effect estimate, under coverage of confidence intervals, and Type 1 error inflation. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to choosing appropriate models that account for potential external factors.

Dataset Information

Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding.

Publications

Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding.

Similar Datasets

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