Using CART to identify thresholds and hierarchies in the determinants of funding decisions

Chris Schilling, Duncan Mortimer, Kim Dalziel

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

There is much interest in understanding decision-making processes that determine funding outcomes for health interventions. We use classification and regression trees (CART) to identify cost-effectiveness thresholds and hierarchies in the determinants of funding decisions. The hierarchical structure of CART is suited to analyzing complex conditional and nonlinear relationships. Our analysis uncovered hierarchies where interventions were grouped according to their type and objective. Cost-effectiveness thresholds varied markedly depending on which group the intervention belonged to: lifestyle-type interventions with a prevention objective had an incremental cost-effectiveness threshold of $2356, suggesting that such interventions need to be close to cost saving or dominant to be funded. For lifestyle-type interventions with a treatment objective, the threshold was much higher at $37,024. Lower down the tree, intervention attributes such as the level of patient contribution and the eligibility for government reimbursement influenced the likelihood of funding within groups of similar interventions. Comparison between our CART models and previously published results demonstrated concurrence with standard regression techniques while providing additional insights regarding the role of the funding environment and the structure of decision-maker preferences.
Original languageEnglish
Pages (from-to)173-182
Number of pages10
JournalMedical Decision Making
Volume37
Issue number2
DOIs
Publication statusPublished - Feb 2017

Keywords

  • CART
  • cost-effectiveness
  • funding of health care

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