Estimating causal effects of complex interventions in longitudinal studies with intermediate variables
Valid causal inference remains the key goal motivating the analysis of data from observational studies and randomised trials. Identifying the causal mechanisms is crucial to understand the epidemiology of disease and for developing successful treatments. The motivation underpinning this proposal is the analysis of randomised trials of complex interventions where the estimation of the effects of treatment may be influenced by the presence of indirect effects acting through intermediate variables such as mediators or surrogates; for example, the effect of CBT in a psychotherapy trial may be mediated by the therapeutic alliance between therapist and patient.
Traditional methods of statistical analysis make very strong assumptions requiring the absence (through lack of acknowledgement) of unmeasured confounders between the intermediate variable and outcome. Recent advances recognizing this problem have been proposed that make use of instrumental variables (IVs), where the key instruments are randomisation and its interaction with baseline covariates. An alternative method, principal stratification (PS), stratifies the population into latent classes based on the potential values of the intermediate variables at each level of treatment. Both methods rely on having good predictors of the intermediate variables from baseline covariates. However, there are still further problems; for example, there is likely to be treatment effect heterogeneity, and if a patient’s idiosyncratic response to treatment is related to their decision to select treatment then the standard IV approach may not estimate a valid casual effect.
This proposal aims to extend the IV and PS methods by addressing this and other complications such as measurement error, and developing the methods to allow for multiple intermediate variables. Additionally, most studies are longitudinal in design and so a particular focus is on extensions to take account of repeated measures and missing data on the outcomes and intermediate variables. The new methods will be applicable to observational studies and randomised trials in all areas, since questions about the causal mechanisms are universal.
Exploratory analysis of randomised trials investigating complex interventions from the field of mental health will be used to illustrate the new techniques, and the empirical results will provide greater understanding of how these particular treatments work. The identification of predictors of intermediate variables such as surrogate outcomes or therapeutic alliance is also of benefit. Wider clinical opportunities will derive from application of the methods to other studies, which will allow a greater insight into the nature of many existing and future treatments.
Duration of the project
30th September 2009 - 30th April 2013
Medical Research Council - Career Development Award in Biostatistics
Members of the project
|Professor Richard Emsley||Principal investigator|
|Professor Andrew Pickles||Co-investigator|
|Professor Graham Dunn||Co-investigator|
|Professor Jonathan Green||Collaborator|
|Professor Linda Davies||Collaborator|
|Mr Ian White||Collaborator|
|Professor Frank Windmeijer||Collaborator|
|Professor Garrett Fitzmaurice||Collaborator|
|Professor Jonathan Sterne||Collaborator|