Incorporating external trial data to improve survival extrapolations: a pilot study of the COU-AA-301 trial

Survival extrapolation plays a key role within cost effectiveness analysis and is often subject to substantial uncertainty. Use of external data to improve extrapolations has been identified as a key research priority. We present findings from a pilot study using data from the COU-AA-301 trial of abiraterone acetate for metastatic castration-resistant prostate cancer, to explore how external trial data may be incorporated into survival extrapolations. External trial data were identified via a targeted search of technology assessment reports. Four methods using external data were compared to simple parametric models (SPMs): informal reference to external data to select appropriate SPMs, piecewise models with, and without, hazard ratio adjustment, and Bayesian models fitted with a prior on the shape parameter(s). Survival and hazard plots were compared, and summary metrics (point estimate accuracy and restricted mean survival time) were calculated. Without consideration of external data, several SPMs may have been selected as the ‘best-fitting’ model. The range of survival probability estimates was generally reduced when external data were included in model estimation, and external hazard plots aided model selection. Different methods yielded varied results, even with the same data source, highlighting potential issues when integrating external trial data within model estimation. By using external trial data, the most (in)appropriate models may be more easily identified. However, benefits of using external data are contingent upon their applicability to the research question, and the choice of method can have a large impact on extrapolations.

Estimating heterogeneous policy impacts using causal machine learning: a case study of health insurance reform in Indonesia

Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers’ health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia.

Incidence rate and financial burden of medical errors and policy interventions to address them: a multi-method study protocol

Medical error is one of the most critical challenges facing medical services. They pose a substantial threat to patient safety, and their costs draw attention from policymakers, health care planners and researchers. We aim to make a realistic estimation of medical error incidence and related costs and identify factors influencing this incidence in Iranian hospitals. In the first phase of this multi-method study, through two reviews of systematic reviews and a meta-analysis, we will estimate the incidence of medical errors and the strategies to reduce them. We will extract available data among 41 hospitals supervised by the East Azerbaijan University in the second phase. We will also develop a model and use a Delphi method to predict medical errors incidence and calibrate our model output using the Monte Carlo simulation. We will compare this estimation with the incidence rate based on meta-analysis results from the first phase. In the third phase, we will investigate the relationship between several factors potentially influencing medical error incidence. In the fourth phase, we will estimate costs associated with medical errors by conducting a patient records review and matching those with claims related to medical errors. In the fifth phase, we will present a policy brief related to strategies for medical errors and associated costs reduction in Iran. Our findings could benefit Iranian and policymakers in other countries to reduce medical errors and associated costs.