A Latent Variable CACE Model for Multidimensional Endpoints and Treatment Noncompliance with Application to a Longitudinal Trial of Arthritis Health Journal

Randomized clinical trials (RCTs) are the preferred study design for assessing the causal effects of medical interventions on healthcare policymaking. Real-world RCTs evaluating multifaceted interventions often employ multiple study endpoints to measure treatment success on a small set of underlying constructs. We propose a latent variable model with principal strata of latent compliance types for parsimonious estimation of intervention effects in RCTs with multidimensional longitudinal outcomes and treatment noncompliance. Within each compliance type, a factor regression model is used to relate observed multiple endpoints to latent constructs, which are then modelled by hierarchical mixed-effects regression models. Under this model, high dimensional outcomes are reduced to low dimensional latent factors. This dimension reduction leads to a more parsimonious and efficient test of overall complier average causal effects (CACE) on multiple endpoints, mitigating the potential multiple testing issues associated with multiple endpoints. Furthermore, the inference based on factors can be more interpretable and scientifically relevant. We evaluate the performance of the proposed model using simulation studies, which shows study power can be increased substantially compared with estimating CACE for each endpoint separately. The proposed approach is illustrated by evaluating the treatment efficacy of the Arthritis Health Journal online tool. We evaluated the treatment efficacy of Arthritis Health Journal under one latent variable and two latent variables separately. Significant and beneficial treatment effects on latent variables are detected in both two situations.

Causal inference

Potential outcome model

Treatment effects estimation

Factor analysis

Mixed-effects regression model

Principal Stratification