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The Impact of Regression to the Mean on Economic Evaluation in Quasi‐Experimental Pre–Post Studies: The Example of Total Knee Replacement Using Data from the Osteoarthritis Initiative

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  • Chris Schilling
  • Dennis Petrie
  • Michelle M. Dowsey
  • Peter F. Choong
  • Philip Clarke

Abstract

Many treatments are evaluated using quasi‐experimental pre–post studies susceptible to regression to the mean (RTM). Ignoring RTM could bias the economic evaluation. We investigated this issue using the contemporary example of total knee replacement (TKR), a common treatment for end‐stage osteoarthritis of the knee. Data (n = 4796) were obtained from the Osteoarthritis Initiative database, a longitudinal observational study of osteoarthritis. TKR patients (n = 184) were matched to non‐TKR patients, using propensity score matching on the predicted hazard of TKR and exact matching on osteoarthritis severity and health‐related quality of life (HrQoL). The economic evaluation using the matched control group was compared to the standard method of using the pre‐surgery score as the control. Matched controls were identified for 56% of the primary TKRs. The matched control HrQoL trajectory showed evidence of RTM accounting for a third of the estimated QALY gains from surgery using the pre‐surgery HrQoL as the control. Incorporating RTM into the economic evaluation significantly reduced the estimated cost effectiveness of TKR and increased the uncertainty. A generalized ICER bias correction factor was derived to account for RTM in cost‐effectiveness analysis. RTM should be considered in economic evaluations based on quasi‐experimental pre–post studies. Copyright © 2017 John Wiley & Sons, Ltd.

Suggested Citation

  • Chris Schilling & Dennis Petrie & Michelle M. Dowsey & Peter F. Choong & Philip Clarke, 2017. "The Impact of Regression to the Mean on Economic Evaluation in Quasi‐Experimental Pre–Post Studies: The Example of Total Knee Replacement Using Data from the Osteoarthritis Initiative," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 35-51, December.
  • Handle: RePEc:wly:hlthec:v:26:y:2017:i:12:p:e35-e51
    DOI: 10.1002/hec.3475
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    1. Noémi Kreif & Richard Grieve & M. Zia Sadique, 2013. "Statistical Methods For Cost‐Effectiveness Analyses That Use Observational Data: A Critical Appraisal Tool And Review Of Current Practice," Health Economics, John Wiley & Sons, Ltd., vol. 22(4), pages 486-500, April.
    2. Bo Lu, 2005. "Propensity Score Matching with Time-Dependent Covariates," Biometrics, The International Biometric Society, vol. 61(3), pages 721-728, September.
    3. Silviya Nikolova & Mark Harrison & Matt Sutton, 2016. "The Impact of Waiting Time on Health Gains from Surgery: Evidence from a National Patient‐reported Outcome Dataset," Health Economics, John Wiley & Sons, Ltd., vol. 25(8), pages 955-968, August.
    4. Friedman, Milton, 1992. "Do Old Fallacies Ever Die?," Journal of Economic Literature, American Economic Association, vol. 30(4), pages 2129-2132, December.
    5. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
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