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Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration

Author

Listed:
  • Qi Cao

    (University of Groningen
    University of Groningen, University Medical Center Groningen)

  • Erik Buskens

    (University of Groningen, University Medical Center Groningen)

  • Hans L. Hillege

    (University of Groningen, University Medical Center Groningen
    University of Groningen, University Medical Center Groningen, Groningen)

  • Tiny Jaarsma

    (Linköping University)

  • Maarten Postma

    (University of Groningen
    University of Groningen, University Medical Center Groningen
    University of Groningen, University Medical Center Groningen (UMCG)
    University of Groningen, University Medical Center Groningen (UMCG))

  • Douwe Postmus

    (University of Groningen, University Medical Center Groningen)

Abstract

Objectives We sought to explore to what extent the use of Subpopulation Treatment Effect Pattern Plot (STEPP) may help to identify efficient treatment allocation strategy. Methods The analysis was based on data from the COACH study, in which 1023 patients with heart failure were randomly assigned to three treatments: care-as-usual, basic support, and intensive support. First, using predicted 18-month mortality risk as the stratification basis, a suitable strategy for assigning different treatments to different risk groups of patients was developed. To that end, a graphical exploration of the difference in net monetary benefit (NMB) across treatment regimens and baseline risk was used. Next, the efficiency gains resulting from this proposed subgroup strategy were quantified by computing the difference in NMB between our stratified approach and the best performing population-wide strategy. Results The analysis using STEPPs suggested that a differentiated approach, based on offering intensive support to low-risk patients (18-month mortality risk ≤ 0.16) and basic support to intermediate- to high-risk patients (18-month mortality risk > 0.16) would be an economically efficient treatment allocation strategy. This was confirmed in the subsequent cost-effectiveness analysis, where the average gain in NMB resulting from the proposed stratified approach compared to basic support for all was found to be €1312 (95% CI €390–€2346) per patient. Conclusions STEPP provides a systematic approach to assess the interaction between baseline risk and the difference in NMB between competing interventions and to identify cutoffs to stratify patients in a health economically optimal manner.

Suggested Citation

  • Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    • Handle: RePEc:spr:eujhec:v:20:y:2019:i:3:d:10.1007_s10198-018-1013-z
    DOI: 10.1007/s10198-018-1013-z
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    References listed on IDEAS

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    1. Andrew R. Willan & Andrew H. Briggs & Jeffrey S. Hoch, 2004. "Regression methods for covariate adjustment and subgroup analysis for non‐censored cost‐effectiveness data," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 461-475, May.
    2. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629, November.
    3. Douglas Coyle & Martin J. Buxton & Bernie J. O'Brien, 2003. "Stratified cost‐effectiveness analysis: a framework for establishing efficient limited use criteria," Health Economics, John Wiley & Sons, Ltd., vol. 12(5), pages 421-427, May.
    4. George Miller & Stephen Randolph & Emma Forkner & Brad Smith & Autumn Dawn Galbreath, 2009. "Long-Term Cost-Effectiveness of Disease Management in Systolic Heart Failure," Medical Decision Making, , vol. 29(3), pages 325-333, May.
    5. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884, November.
    6. Janneke Grutters & Mark Sculpher & Andrew Briggs & Johan Severens & Math Candel & James Stahl & Dirk Ruysscher & Albert Boer & Bram Ramaekers & Manuela Joore, 2013. "Acknowledging Patient Heterogeneity in Economic Evaluation," PharmacoEconomics, Springer, vol. 31(2), pages 111-123, February.
    7. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    8. Anirban Basu & David Meltzer, 2007. "Value of Information on Preference Heterogeneity and Individualized Care," Medical Decision Making, , vol. 27(2), pages 112-127, March.
    9. Jeffrey S. Hoch & Andrew H. Briggs & Andrew R. Willan, 2002. "Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 11(5), pages 415-430, July.
    10. Qi Cao & Erik Buskens & Talitha Feenstra & Tiny Jaarsma & Hans Hillege & Douwe Postmus, 2016. "Continuous-Time Semi-Markov Models in Health Economic Decision Making," Medical Decision Making, , vol. 36(1), pages 59-71, January.
    11. Rena Conti & David L. Veenstra & Katrina Armstrong & Lawrence J. Lesko & Scott D. Grosse, 2010. "Personalized Medicine and Genomics: Challenges and Opportunities in Assessing Effectiveness, Cost-Effectiveness, and Future Research Priorities," Medical Decision Making, , vol. 30(3), pages 328-340, May.
    12. Manuel A. Espinoza & Andrea Manca & Karl Claxton & Mark J. Sculpher, 2014. "The Value of Heterogeneity for Cost-Effectiveness Subgroup Analysis," Medical Decision Making, , vol. 34(8), pages 951-964, November.
    13. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
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