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Clinical trials are expensive and time-consuming and so should also be used to study how treatments work, allowing for the evaluation of theoretical treatment models and refinement and improvement of treatments. These treatment processes can be studied using mediation analysis. Randomised treatment makes some of the assumptions of mediation models plausible, but the mediator-outcome relationship could remain subject to bias. In addition, mediation is assumed to be a temporally ordered longitudinal process, but estimation in most mediation studies to date has been cross-sectional and unable to explore this assumption. This study used longitudinal structural equation modelling of mediator and outcome measurements from the PACE trial of rehabilitative treatments for chronic fatigue syndrome (ISRCTN 54285094) to address these issues. In particular, autoregressive and simplex models were used to study measurement error in the mediator, different time lags in the mediator-outcome relationship, unmeasured confounding of the mediator and outcome, and the assumption of a constant mediator-outcome relationship over time. Results showed that allowing for measurement error and unmeasured confounding were important. Contemporaneous rather than lagged mediator-outcome effects were more consistent with the data, possibly due to the wide spacing of measurements. Assuming a constant mediator-outcome relationship over time increased precision.

Original publication

DOI

10.1177/0962280216666111

Type

Journal article

Journal

Stat Methods Med Res

Publication Date

19/09/2016

Keywords

Mediation, chronic fatigue syndrome, clinical trials, longitudinal mediation models, measurement error, structural equation models