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Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease

Author

Listed:
  • Chris Schilling

    (University of Melbourne)

  • Duncan Mortimer

    (Monash University)

  • Kim Dalziel

    (University of Melbourne)

  • Emma Heeley

    (The University of Sydney and the Royal Prince Alfred Hospital)

  • John Chalmers

    (The University of Sydney and the Royal Prince Alfred Hospital)

  • Philip Clarke

    (University of Melbourne)

Abstract

Background and Objective Many guidelines for clinical decisions are hierarchical and nonlinear. Evaluating if these guidelines are used in practice requires methods that can identify such structures and thresholds. Classification and regression trees (CART) were used to analyse prescribing patterns of Australian general practitioners (GPs) for the primary prevention of cardiovascular disease (CVD). Our aim was to identify if GPs use absolute risk (AR) guidelines in favour of individual risk factors to inform their prescribing decisions of lipid-lowering medications. Methods We employed administrative prescribing information that is linked to patient-level data from a clinical assessment and patient survey (the AusHeart Study), and assessed prescribing of lipid-lowering medications over a 12-month period for patients (n = 1903) who were not using such medications prior to recruitment. CART models were developed to explain prescribing practice. Out-of-sample performance was evaluated using receiver operating characteristic (ROC) curves, and optimised via pruning. Results We found that individual risk factors (low-density lipoprotein, diabetes, triglycerides and a history of CVD), GP-estimated rather than Framingham AR, and sociodemographic factors (household income, education) were the predominant drivers of GP prescribing. However, sociodemographic factors and some individual risk factors (triglycerides and CVD history) only become relevant for patients with a particular profile of other risk factors. The ROC area under the curve was 0.63 (95 % confidence interval [CI] 0.60–0.64). Conclusions There is little evidence that AR guidelines recommended by the National Heart Foundation and National Vascular Disease Prevention Alliance, or conditional individual risk eligibility guidelines from the Pharmaceutical Benefits Scheme, are adopted in prescribing practice. The hierarchy of conditional relationships between risk factors and socioeconomic factors identified by CART provides new insights into prescribing decisions. Overall, CART is a useful addition to the analyst’s toolkit when investigating healthcare decisions.

Suggested Citation

  • Chris Schilling & Duncan Mortimer & Kim Dalziel & Emma Heeley & John Chalmers & Philip Clarke, 2016. "Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease," PharmacoEconomics, Springer, vol. 34(2), pages 195-205, February.
    • Handle: RePEc:spr:pharme:v:34:y:2016:i:2:d:10.1007_s40273-015-0342-3
    DOI: 10.1007/s40273-015-0342-3
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    References listed on IDEAS

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    1. Drakopoulos, S A, 1994. "Hierarchical Choice in Economics," Journal of Economic Surveys, Wiley Blackwell, vol. 8(2), pages 133-153, June.
    2. Knott, Rachel J. & Petrie, Dennis J. & Heeley, Emma L. & Chalmers, John P. & Clarke, Philip M., 2015. "The effects of reduced copayments on discontinuation and adherence failure to statin medication in Australia," Health Policy, Elsevier, vol. 119(5), pages 620-627.
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    1. Schilling, Chris & Knight, Josh & Mortimer, Duncan & Petrie, Dennis & Clarke, Philip & Chalmers, John & Kerr, Andrew & Jackson, Rod, 2017. "Australian general practitioners initiate statin therapy primarily on the basis of lipid levels; New Zealand general practitioners use absolute risk," Health Policy, Elsevier, vol. 121(12), pages 1233-1239.
    2. Banzragch Nandintsetseg & Masato Shinoda & Baasandai Erdenetsetseg, 2018. "Contributions of multiple climate hazards and overgrazing to the 2009/2010 winter disaster in Mongolia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 109-126, November.
    3. Eberechukwu Onukwugha, 2016. "Big Data and Its Role in Health Economics and Outcomes Research: A Collection of Perspectives on Data Sources, Measurement, and Analysis," PharmacoEconomics, Springer, vol. 34(2), pages 91-93, February.
    4. Eberechukwu Onukwugha, 2016. "Big Data and Its Role in Health Economics and Outcomes Research: A Collection of Perspectives on Data Sources, Measurement, and Analysis," PharmacoEconomics, Springer, vol. 34(2), pages 91-93, February.
    5. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.

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