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Measurement error, time lag, unmeasured confounding: Considerations for longitudinal estimation of the effect of the mediation ‘b path’ in randomised clinical trials

Research output: Contribution to journalArticle

KA Goldsmith, T Chalder, PD White, M Sharpe, A Pickles

Original languageEnglish
Pages (from-to)1615-1633
Number of pages19
JournalStatistical Methods in Medical Research
Volume27
Issue number6
Early online date19 Sep 2016
DOIs
Publication statusPublished - 1 Jun 2018

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Abstract

Clinical trials are expensive and time-consuming and so should also be used to study how treatments work. This would allow evaluation of theoretical treatment models and refinement and improvement of treatments. Treatment processes can be studied using mediation analysis. Randomised treatment makes some of the assumptions of mediation models plausible, but the mediator – outcome relationship remains one that can be subject to bias. In addition, mediation is assumed to be a temporally ordered longitudinal process, but most mediation studies to date have 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. Concurrent rather than lagged mediator – outcome effects were more consistent with the data, possibly due to the wide spacing of measurements. Assuming a constant mediatoroutcome relationship over time increased precision.

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