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Examining Ways to Handle Non-Random Missingness in CEA through Econometric and Statistics Lenses

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Listed:
  • Jackie, Yenerall
  • Wen, You
  • George, Davis
  • Paul, Estabrooks

Abstract

Missing data in experiments can bias estimates if not appropriately addressed. This is of particular concern in cost-effectiveness analysis where bias in either the cost or effect estimate could bias the entire cost effectiveness estimate. Complicated experimental designs, such as cluster randomized trials (CRT) or longitudinal data call for even greater care when addressing missingness. The purpose of this paper is to compare two sample selection models designed to address bias resulting from non-random missingless when applied to a longitudinal CRT. From the statistics literature we consider the Diggle Kenward model and from the econometrics literature we consider the Heckman model. Both of these models will be used to analyze the twelve-month outcomes of a worksite weight loss program, as well as used in a simulation experiment.

Suggested Citation

  • Jackie, Yenerall & Wen, You & George, Davis & Paul, Estabrooks, 2015. "Examining Ways to Handle Non-Random Missingness in CEA through Econometric and Statistics Lenses," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205690, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea15:205690
    DOI: 10.22004/ag.econ.205690
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    File URL: https://ageconsearch.umn.edu/record/205690/files/MissingCEAAAEA.pdf
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    References listed on IDEAS

    as
    1. Gilleskie, Donna B. & Mroz, Thomas A., 2004. "A flexible approach for estimating the effects of covariates on health expenditures," Journal of Health Economics, Elsevier, vol. 23(2), pages 391-418, March.
    2. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    3. Koopmanschap, Marc A. & Rutten, Frans F. H. & van Ineveld, B. Martin & van Roijen, Leona, 1995. "The friction cost method for measuring indirect costs of disease," Journal of Health Economics, Elsevier, vol. 14(2), pages 171-189, June.
    4. Manning, Willard G. & Basu, Anirban & Mullahy, John, 2005. "Generalized modeling approaches to risk adjustment of skewed outcomes data," Journal of Health Economics, Elsevier, vol. 24(3), pages 465-488, May.
    5. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    6. Manuel Gomes & Karla Díaz-Ordaz & Richard Grieve & Michael G. Kenward, 2013. "Multiple Imputation Methods for Handling Missing Data in Cost-effectiveness Analyses That Use Data from Hierarchical Studies," Medical Decision Making, , vol. 33(8), pages 1051-1063, November.
    7. Jolene Birmingham & Andrea Rotnitzky & Garrett M. Fitzmaurice, 2003. "Pattern–mixture and selection models for analysing longitudinal data with monotone missing patterns," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 275-297, February.
    8. Rita Faria & Manuel Gomes & David Epstein & Ian White, 2014. "A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials," PharmacoEconomics, Springer, vol. 32(12), pages 1157-1170, December.
    9. K. Díaz-Ordaz & Michael G. Kenward & Richard Grieve, 2014. "Handling missing values in cost effectiveness analyses that use data from cluster randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 457-474, February.
    10. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
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    Keywords

    Health Economics and Policy; Research Methods/ Statistical Methods;

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