[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v29y2020is1p110-126.html
   My bibliography  Save this article

Explaining the causal effect of adherence to medication on cholesterol through the marginal patient

Author

Listed:
  • Domenico Depalo
Abstract
This paper investigates the relation between adherence to prescribed medication and reduction of cholesterol in Italy, taking into account the possible sorting of patients into treatment and the heterogeneity of the effect. As predicted by a theoretical model, I find that patients who benefit most from medication are more likely to adhere to prescribed regime than those who benefit least. These results are used to study the effects of three hypothetical policies that aim at increasing the share of patients adherent to prescribed medication: one policy is directed toward patients, one toward physicians, and one toward both patients and physicians. For each policy, I describe the observable characteristics of patients induced into treatment. Although the policy with the highest return is directed toward patients, the policies differ substantially with respect to the population affected. Therefore, a policy with lower return that targets better the desired population may be preferred to the policy with the highest return.

Suggested Citation

  • Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
  • Handle: RePEc:wly:hlthec:v:29:y:2020:i:s1:p:110-126
    DOI: 10.1002/hec.4030
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.4030
    Download Restriction: no

    File URL: https://libkey.io/10.1002/hec.4030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71, Elsevier.
    2. Pedro Carneiro & Michael Lokshin & Nithin Umapathi, 2017. "Average and Marginal Returns to Upper Secondary Schooling in Indonesia," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 16-36, January.
    3. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    4. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    5. Congressional Budget Office, 2018. "The Budget and Economic Outlook: 2018 to 2028," Reports 53651, Congressional Budget Office.
    6. Thomas Cornelissen & Christian Dustmann & Anna Raute & Uta Schönberg, 2018. "Who Benefits from Universal Child Care? Estimating Marginal Returns to Early Child Care Attendance," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2356-2409.
    7. Amanda Kowalski, 2016. "Doing more when you're running LATE: Applying marginal treatment effect methods to examine treatment effect heterogeneity in experiments," Artefactual Field Experiments 00560, The Field Experiments Website.
    8. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    9. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    10. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    11. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    12. Deb, Partha & Munkin, Murat K. & Trivedi, Pravin K., 2006. "Private Insurance, Selection, and Health Care Use: A Bayesian Analysis of a Roy-Type Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 403-415, October.
    13. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    14. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    15. Eric French & Jae Song, 2014. "The Effect of Disability Insurance Receipt on Labor Supply," American Economic Journal: Economic Policy, American Economic Association, vol. 6(2), pages 291-337, May.
    16. Lisa Oberlander & Anne‐Célia Disdier & Fabrice Etilé, 2017. "Globalisation and national trends in nutrition and health: A grouped fixed‐effects approach to intercountry heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 26(9), pages 1146-1161, September.
    17. Eleonora Fichera & James Banks & Luigi Siciliani & Matt Sutton, 2017. "Does Patient Health Behaviour respond to Doctor’s Effort?," Department of Economics Working Papers 62/17, University of Bath, Department of Economics.
    18. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    19. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    20. Nicole Maestas & Kathleen J. Mullen & Alexander Strand, 2013. "Does Disability Insurance Receipt Discourage Work? Using Examiner Assignment to Estimate Causal Effects of SSDI Receipt," American Economic Review, American Economic Association, vol. 103(5), pages 1797-1829, August.
    21. Vincenzo Atella & Federico Belotti & Domenico Depalo, 2017. "Drug therapy adherence and health outcomes in the presence of physician and patient unobserved heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 106-126, September.
    22. Arabmazar, Abbas & Schmidt, Peter, 1981. "Further evidence on the robustness of the Tobit estimator to heteroskedasticity," Journal of Econometrics, Elsevier, vol. 17(2), pages 253-258, November.
    23. Card, David, 1999. "The causal effect of education on earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 30, pages 1801-1863, Elsevier.
    24. Carneiro, Pedro & Lee, Sokbae, 2009. "Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enrollment and wage inequality," Journal of Econometrics, Elsevier, vol. 149(2), pages 191-208, April.
    25. Anirban Basu, 2014. "ESTIMATING PERSON‐CENTERED TREATMENT (PeT) EFFECTS USING INSTRUMENTAL VARIABLES: AN APPLICATION TO EVALUATING PROSTATE CANCER TREATMENTS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 671-691, June.
    26. Karine Lamiraud & Pierre‐Yves Geoffard, 2007. "Therapeutic non‐adherence: a rational behavior revealing patient preferences?," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1185-1204, November.
    27. Jeffrey R. Kling, 2006. "Incarceration Length, Employment, and Earnings," American Economic Review, American Economic Association, vol. 96(3), pages 863-876, June.
    28. Panos Kasteridis & Andrew Street & Matthew Dolman & Lesley Gallier & Kevin Hudson & Jeremy Martin & Ian Wyer, 2014. "The importance of multimorbidity in explaining utilisation and costs across health and social care settings: evidence from South Somersets Symphony Project," Working Papers 096cherp, Centre for Health Economics, University of York.
    29. 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.
    30. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    31. James J. Heckman, 2010. "Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 356-398, June.
    32. Federico Belotti & Joanna Kopinska & Alessandro Palma & Andrea Piano Mortari, 2022. "Health status and the Great Recession. Evidence from electronic health records," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1770-1799, August.
    33. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: An application in breast cancer patients," Health, Econometrics and Data Group (HEDG) Working Papers 07/07, HEDG, c/o Department of Economics, University of York.
    34. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    35. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    36. Joseph J. Doyle Jr., 2007. "Child Protection and Child Outcomes: Measuring the Effects of Foster Care," American Economic Review, American Economic Association, vol. 97(5), pages 1583-1610, December.
    37. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
    38. Arabmazar, Abbas & Schmidt, Peter, 1982. "An Investigation of the Robustness of the Tobit Estimator to Non-Normality," Econometrica, Econometric Society, vol. 50(4), pages 1055-1063, July.
    39. Christian N. Brinch & Magne Mogstad & Matthew Wiswall, 2017. "Beyond LATE with a Discrete Instrument," Journal of Political Economy, University of Chicago Press, vol. 125(4), pages 985-1039.
    40. Toru Kitagawa, 2015. "A Test for Instrument Validity," Econometrica, Econometric Society, vol. 83(5), pages 2043-2063, September.
    41. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, vol. 66(1), pages 33-39, January.
    42. repec:wly:hlthec:v:26:y:2017:i::p:106-126 is not listed on IDEAS
    43. O. Ashenfelter & D. Card (ed.), 1999. "Handbook of Labor Economics," Handbook of Labor Economics, Elsevier, edition 1, volume 3, number 3.
    44. James H. Stock & Motohiro Yogo, 2002. "Testing for Weak Instruments in Linear IV Regression," NBER Technical Working Papers 0284, National Bureau of Economic Research, Inc.
    45. 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.
    46. Domenico Depalo & Jay Bhattacharya & Vincenzo Atella & Federico Belotti, 2019. "When Technological Advance Meets Physician Learning in Drug Prescribing," NBER Working Papers 26202, National Bureau of Economic Research, Inc.
    47. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70, Elsevier.
    48. Fichera, Eleonora & Banks, James & Siciliani, Luigi & Sutton, Matt, 2018. "Does patient health behaviour respond to doctor effort?," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 225-251.
    49. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    50. Karine Lamiraud & Pierre-Yves Geoffard, 2007. "Therapeutic non-adherence: a rational behavior revealing patient preferences?," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1185-1204.
    51. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments for the Young and Privately Insured"," Cowles Foundation Discussion Papers 2045, Cowles Foundation for Research in Economics, Yale University.
    52. David Card & Alessandra Fenizia & David Silver, 2018. "The Health Effects of Cesarean Delivery for Low-Risk First Births," NBER Working Papers 24493, National Bureau of Economic Research, Inc.
    53. Chernozhukov, Victor & Imbens, Guido W. & Newey, Whitney K., 2007. "Instrumental variable estimation of nonseparable models," Journal of Econometrics, Elsevier, vol. 139(1), pages 4-14, July.
    54. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
    55. Dyfrig A. Hughes & Adrian Bagust & Alan Haycox & Tom Walley, 2001. "The impact of non‐compliance on the cost‐effectiveness of pharmaceuticals: a review of the literature," Health Economics, John Wiley & Sons, Ltd., vol. 10(7), pages 601-615, October.
    56. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 28th December 2020
      by Chris Sampson in The Academic Health Economists' Blog on 2020-12-28 12:00:00

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Joan Costa‐Font & Rosella Levaggi, 2020. "Innovation, aging, and health care: Unraveling “silver” from “red” herrings?," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 3-7, October.
    2. Di Novi, Cinzia & Leporatti, Lucia & Levaggi, Rosella & Montefiori, Marcello, 2022. "Adherence during COVID-19: The role of aging and socio-economics status in shaping drug utilization," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 1-14.
    3. Lucia Leporatti & Rosella Levaggi & Marcello Montefiori, 2021. "Beyond price: the effects of non-financial barriers on access to drugs and health outcomes," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(4), pages 519-529, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    2. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    3. Robert A. Moffitt & Matthew V. Zahn, 2019. "The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation," NBER Working Papers 26028, National Bureau of Economic Research, Inc.
    4. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    5. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    6. Amanda E Kowalski, 2023. "Behaviour within a Clinical Trial and Implications for Mammography Guidelines," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(1), pages 432-462.
    7. Pedro Carneiro & Michael Lokshin & Nithin Umapathi, 2017. "Average and Marginal Returns to Upper Secondary Schooling in Indonesia," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 16-36, January.
    8. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    9. Matthias Westphal & Daniel A Kamhöfer & Hendrik Schmitz, 2022. "Marginal College Wage Premiums Under Selection Into Employment," The Economic Journal, Royal Economic Society, vol. 132(646), pages 2231-2272.
    10. Sokbae Lee & Bernard Salanié, 2018. "Identifying Effects of Multivalued Treatments," Econometrica, Econometric Society, vol. 86(6), pages 1939-1963, November.
    11. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    12. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.
    13. Gerten, Elisa & Beckmann, Michael & Kräkel, Matthias, 2022. "Information and Communication Technology, Hierarchy, and Job Design," IZA Discussion Papers 15491, Institute of Labor Economics (IZA).
    14. James J. Heckman, 2010. "Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 356-398, June.
    15. Bartalotti, Otávio & Kédagni, Désiré & Possebom, Vitor, 2023. "Identifying marginal treatment effects in the presence of sample selection," Journal of Econometrics, Elsevier, vol. 234(2), pages 565-584.
    16. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    17. Ivan A Canay & Magne Mogstad & Jack Mount, 2024. "On the Use of Outcome Tests for Detecting Bias in Decision Making," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(4), pages 2135-2167.
    18. Yu-Chang Chen & Haitian Xie, 2022. "Personalized Subsidy Rules," Papers 2202.13545, arXiv.org, revised Mar 2022.
    19. Akabayashi, Hideo & Ruberg, Tim & Shikishima, Chizuru & Yamashita, Jun, 2023. "Education-oriented and care-oriented preschools: Implications on child development," Labour Economics, Elsevier, vol. 84(C).
    20. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.

    More about this item

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:hlthec:v:29:y:2020:i:s1:p:110-126. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.