Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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Process evaluation using latent variables: applications and extensions of finite mixture models
Richard Andrew Emsley, Graham Dunn

Last modified: 2013-06-16

Abstract


Understanding treatment effect heterogeneity is an important aspect of randomised trials, and process variables describing the intervention content are crucial components of this. Frequently these variables can only be measured in intervention groups. Principal stratification, whereby control group participants are assigned to the latent class they would have been in had they been randomised to intervention, has been proposed for analysing this problem and is often estimated using finite mixture models. The standard principal stratification approach generally makes use of a single observation of the process and outcome variables, which more realistically have repeated measures collected. In this paper, we extend the use of principal stratification to account for repeated measures of the process variables and outcomes using general growth mixture models. We illustrate these methods using randomised trials comparing psychotherapy with treatment as usual in patients with recent onset of psychosis where the process measure is the therapeutic alliance or therapeutic empathy between the therapist and patient.

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