Using CART to Identify Thresholds and Hierarchies in the Determinants of Funding Decisions

An examination of machine learning to map non-preference based patient reported outcome measures to health state utility values

Non-preference-based patient-reported outcome measures (PROMs) are popular in health outcomes research. These measures, however, cannot be used to estimate health state utilities, limiting their usefulness for economic evaluations. Mapping PROMs to a multi-attribute utility instrument is one solution. While mapping is commonly conducted using econometric techniques, failing to specify the complex interactions between variables may lead to inaccurate prediction of utilities, resulting in inaccurate estimates of cost-effectiveness and suboptimal funding decisions. These issues can be addressed using machine learning. This paper evaluates the use of machine learning as a mapping tool. We adopt a comprehensive approach to compare six machine learning techniques with eight econometric techniques to map the Patient-Reported Outcomes Measurement Information System Global Health 10 (PROMIS-GH10) to the EuroQol five dimensions (EQ-5D-5L). Using data collected from 2015 Australians, we find the least absolute shrinkage and selection operator (LASSO) model out-performed all machine learning techniques and the adjusted limited dependent variable mixture model (ALDVMM) out-performed all econometric techniques, with the LASSO performing better than ALDVMM. The variable selection feature of LASSO was then used to enhance the performance of the ALDVMM in a hybrid model. Our analysis identifies the potential benefits and challenges of using machine learning techniques for mapping and offers important insights for future research.

Revealed and Stated Preferences of Decision Makers for Priority Setting in Health Technology Assessment: A Systematic Review

BACKGROUND

There is much interest from stakeholders in understanding how health technology assessment (HTA) committees make national funding decisions for health technologies. A growing literature has analysed past decisions by committees (revealed preference, RP studies) and hypothetical decisions by committee members (stated preference, SP studies) to identify factors influencing decisions and assess their importance.

OBJECTIVES

A systematic review of the literature was undertaken to provide insight into committee preferences for these factors (after controlling for other factors) and the methods used to elicit them.

METHODS

Ovid Medline, Embase, Econlit and Web of Science were searched from inception to 11 May 2017. Included studies had to have investigated factors considered by HTA committees and to have conducted multivariate analysis to identify the effect of each factor on funding decisions. Factors were classified as being important based on statistical significance, and their impact on decisions was compared using marginal effects.

RESULTS

Twenty-three RP and four SP studies (containing 42 analyses) of 14 HTA committees met the inclusion criteria. Although factors were defined differently, the SP literature generally found clinical efficacy, cost-effectiveness and equity factors (such as disease severity) were each important to the Pharmaceutical Benefits Advisory Committee (PBAC), the National Institute for Health and Care Excellence (NICE) and the All Wales Medicines Strategy Group. These findings were supported by the RP studies of the PBAC, but not the other committees, which found funding decisions by these and other committees were mostly influenced by the acceptance of the clinical evidence and, where applicable, cost-effectiveness. Trust in the evidence was very important for decision makers, equivalent to reducing the incremental cost-effectiveness ratio (cost per quality-adjusted life-year) by A$38,000 (Australian dollars) for the PBAC and £15,000 for NICE.

CONCLUSIONS

This review found trust in the clinical evidence and, where applicable, cost-effectiveness were important for decision makers. Many methodological differences likely contributed to the diversity in some of the other findings across studies of the same committee. Further work is needed to better understand how competing factors are valued by different HTA committees.