TY - JOUR
T1 - Measurement error, time lag, unmeasured confounding
T2 - Considerations for longitudinal estimation of the effect of the mediation ‘b path’ in randomised clinical trials
AU - Goldsmith, KA
AU - Chalder, T
AU - White, PD
AU - Sharpe, M
AU - Pickles, A
PY - 2018/6/1
Y1 - 2018/6/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85047171208&partnerID=8YFLogxK
U2 - 10.1177/0962280216666111
DO - 10.1177/0962280216666111
M3 - Article
SN - 0962-2802
VL - 27
SP - 1615
EP - 1633
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 6
ER -