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Measurement error, time lag, unmeasured confounding: Considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials.

Authors
  • Goldsmith, K A1
  • Chalder, T2
  • White, P D3
  • Sharpe, M4
  • Pickles, A1
  • 1 1 Biostatistics & Health Informatics Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
  • 2 2 Academic Department of Psychological Medicine, Weston Education Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
  • 3 3 Centre for Psychiatry, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine, Queen Mary University, London, UK.
  • 4 4 Psychological Medicine Research, Department of Psychiatry, University of Oxford, Oxford, UK.
Type
Published Article
Journal
Statistical Methods in Medical Research
Publisher
SAGE Publications
Publication Date
Jun 01, 2018
Volume
27
Issue
6
Pages
1615–1633
Identifiers
DOI: 10.1177/0962280216666111
PMID: 27647810
Source
Medline
Keywords
License
Unknown

Abstract

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.

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