Many regulatory agencies require that manufacturers establish both efficacy and cost-effectiveness. The statistical analysis of the randomized, controlled trial (RCT) outcomes should be the same for both purposes. The question addressed by this article is the following: for survival outcomes, what is the relationship between the statistical analyses used to support inference and the statistical model used to support decision making based on cost-effectiveness analysis (CEA)?
We performed a review of CEAs alongside trials and CEAs based on a synthesis of RCT results, which were submitted to the National Institute for Health and Clinical Excellence (NICE) Technology Appraisal program and included survival outcomes. We recorded the summary statistics and the statistical models used in both efficacy and cost-effectiveness analyses as well as procedures for model diagnosis and selection.
In no case was the statistical model for efficacy and CEA the same. For efficacy, relative risks or Cox regression was used. For CEA, the common practice was to fit a parametric model to the control arm, then to apply the hazard ratio from the efficacy analysis to predict the treatment arm. The proportional hazards assumption was seldom checked; the choice of model was seldom based on formal criteria, and uncertainty in model choice was seldom addressed and never propagated through the model.
Both inference and decisions based on CEAs should be based on the same statistical model. This article shows that for survival outcomes, this is not the case. In the interests of transparency, trial protocols should specify a common procedure for model choice for both purposes. Further, the sufficient statistics and the life tables for each arm should be reported to improve transparency and to facilitate secondary analyses of results of RCTs.