Examining the correlation between treatment effects in clinical trials and economic modeling.

Many diseases have a sequential treatment pathway. Compared with patients without previous treatment, patients who fail initial treatment may have lower success rates with a second treatment. This phenomenon can be explained by a correlation between treatment effects. We developed a statistical model of covariance for the underlying unobserved correlation between treatments and established a mathematical expression for the magnitude of the latent correlation term. We conducted a simulation study of clinical trials to investigate the correlation between two treatments and explored clinical examples based on published literature to illustrate the identification and evaluation of these correlations. Our simulation study confirmed that a treatment correlation reduces the probability of success for the second treatment, compared with no correlation. We found that treatment correlations may be observable in clinical trials, such as for depression and lung cancer, and the magnitude of correlation may be estimated. We illustrated that treatment correlations can be incorporated into an economic model, with possible impacts on cost-effectiveness results. Additional applications of correlation concepts are also discussed. We evaluated the correlation between treatment effects and our approach can be applied to clinical trial design and economic modeling of sequential clinical treatment pathways.