Rationale, aims, and objectives: The study aimed at developing a method for modelling the Western Ontario and McMaster Universities index (WOMAC), accounting for correlation between its subscales and for heterogeneity of treatment effect (HTE), using data from 2 twin trials on knee osteoarthritis.
Method: Two randomized, double-blind, placebo-controlled clinical trials (twin trials). Studies aimed at investigating the effectiveness of a pharmacological treatment on clinical outcomes of knee osteoarthritis, measured using WOMAC index. To take into account that the WOMAC subscales are correlated and skewed, we proposed and compared multivariate gamma and Gaussian approaches with latent variable capturing correlation between outcomes. Besides the latent term, the interaction between the latent term and treatment, accounting for HTE, was further estimated.
Results: Modelling the subscales by using a gamma approach accounting for skewness of data, we found out different results compared with Gaussian models. The main difference regarded the latent variable interacting with treatment (accounting for unobserved heterogeneity), which is not significant for the Gaussian approach (P value = .102) and significant in the gamma model (P value < .002). Thus, indicating that unobserved covariates affect treatment's performance. Additionally, plotting the observed and the estimated values of WOMAC index of the Gaussian and gamma models, we showed that, compared with the Gaussian, the gamma one best fits the data, especially among poor responders.
Conclusion: Multivariate gamma approach accounting for correlation between outcomes and for HTE has been demonstrated to be more suitable to model WOMAC subscales and to provide more information on effect of therapy.
Keywords: Gaussian approach; WOMAC; gamma approach; latent variable.
© 2017 John Wiley & Sons, Ltd.