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Using propensity score methods to analyse individual patient-level cost-effectiveness data from observational studies

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  • Manca, A
  • Austin, P. C

Abstract

The methodology relating to the statistical analysis of individual patient-level cost-effectiveness data collected alongside randomised controlled trials (RCTs) has evolved dramatically in the last ten years. This body of techniques has been developed and applied mainly in the context of the randomised clinical trial design. There are, however, many situations in which a trial is neither the most suitable nor the most efficient vehicle for the evaluation. This paper provides a tutorial-like discussion of the ways in which propensity score methods could be used to assist in the analysis of observational individual patient-level cost-effectiveness data. As a motivating example, we assessed the cost-effectiveness of CABG versus PTCA – one year post procedure - in a cohort of individuals who received the intervention within 365 days of their index admission for AMI. The data used for this paper were obtained from the Ontario Myocardial Infarction Database (OMID), linking these with data from the Canadian Institute for Health Information (CIHI), the Ontario Health Insurance Plan (OHIP), the Ontario Drug Benefit (ODB) program, and Ontario Registered Persons Database (RPDB). We discuss three ways in which propensity score can be used to control for confounding in the estimation of average cost-effectiveness, and provide syntax codes for both propensity score matching and cost-effectiveness modelling.

Suggested Citation

  • Manca, A & Austin, P. C, 2008. "Using propensity score methods to analyse individual patient-level cost-effectiveness data from observational studies," Health, Econometrics and Data Group (HEDG) Working Papers 08/20, HEDG, c/o Department of Economics, University of York.
    • Handle: RePEc:yor:hectdg:08/20
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    References listed on IDEAS

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    1. Andrea Manca & Nigel Rice & Mark J. Sculpher & Andrew H. Briggs, 2005. "Assessing generalisability by location in trial‐based cost‐effectiveness analysis: the use of multilevel models," Health Economics, John Wiley & Sons, Ltd., vol. 14(5), pages 471-485, May.
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    Cited by:

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    2. Matthew Franklin & James Lomas & Gerry Richardson, 2020. "Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations," PharmacoEconomics, Springer, vol. 38(7), pages 665-681, July.
    3. Caroline Canavan & Joe West & Timothy Card, 2016. "Calculating Total Health Service Utilisation and Costs from Routinely Collected Electronic Health Records Using the Example of Patients with Irritable Bowel Syndrome Before and After Their First Gastr," PharmacoEconomics, Springer, vol. 34(2), pages 181-194, February.
    4. William Crown, 2014. "Propensity-Score Matching in Economic Analyses: Comparison with Regression Models, Instrumental Variables, Residual Inclusion, Differences-in-Differences, and Decomposition Methods," Applied Health Economics and Health Policy, Springer, vol. 12(1), pages 7-18, February.
    5. Caroline Canavan & Joe West & Timothy Card, 2016. "Calculating Total Health Service Utilisation and Costs from Routinely Collected Electronic Health Records Using the Example of Patients with Irritable Bowel Syndrome Before and After Their First Gastr," PharmacoEconomics, Springer, vol. 34(2), pages 181-194, February.

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    Keywords

    Cost; cost-effectiveness; propensity score; revascularisation; statistical methods;
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