IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/25049.html
   My bibliography  Save this paper

Behavior within a Clinical Trial and Implications for Mammography Guidelines

Author

Listed:
  • Amanda E. Kowalski

Abstract

Mammography guidelines have weakened in response to evidence that mammograms diagnose breast cancers that would never eventually cause symptoms, a phenomenon called “overdiagnosis.” Given concerns about overdiagnosis, instead of recommending mammograms, US guidelines encourage women aged 40-49 to get them as they see fit. To assess whether these guidelines target women effectively, I propose an approach that examines mammography behavior within an influential clinical trial that followed participants long enough to find overdiagnosis. I find that women who are more likely to receive mammograms are healthier and have higher socioeconomic status. More importantly, I find that the 20-year level of overdiagnosis is at least 3.5 times higher among women who are most likely to receive mammograms. At least 36% of their cancers are overdiagnosed. These findings imply that US guidelines encourage mammograms among healthier women who are more likely to be overdiagnosed by them. Guidelines in other countries do not.

Suggested Citation

  • Amanda E. Kowalski, 2018. "Behavior within a Clinical Trial and Implications for Mammography Guidelines," NBER Working Papers 25049, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:25049
    Note: AG EH LS PE TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w25049.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Willis, Robert J & Rosen, Sherwin, 1979. "Education and Self-Selection," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 7-36, October.
    2. Amanda E. Kowalski, 2023. "Reconciling Seemingly Contradictory Results from the Oregon Health Insurance Experiment and the Massachusetts Health Reform," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 646-664, May.
    3. 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.
    4. 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.
    5. Huber, Martin, 2013. "A simple test for the ignorability of non-compliance in experiments," Economics Letters, Elsevier, vol. 120(3), pages 389-391.
    6. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    7. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    8. Patrick Kline & Christopher R. Walters, 2019. "On Heckits, LATE, and Numerical Equivalence," Econometrica, Econometric Society, vol. 87(2), pages 677-696, March.
    9. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    10. Cutler, David M. & Lleras-Muney, Adriana, 2010. "Understanding differences in health behaviors by education," Journal of Health Economics, Elsevier, vol. 29(1), pages 1-28, January.
    11. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    12. Zanella, Giulio & Banerjee, Ritesh, 2016. "Experiencing breast cancer at the workplace," Journal of Public Economics, Elsevier, vol. 134(C), pages 53-66.
    13. Rebecca Mary Myerson & Darius Lakdawalla & Lisandro D. Colantonio & Monika Safford & David Meltzer, 2018. "Effects of Expanding Health Screening on Treatment - What Should We Expect? What Can We Learn?," NBER Working Papers 24347, National Bureau of Economic Research, Inc.
    14. 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.
    15. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert J. & Smith, Jeffrey A. & Taylor, Evan J., 2015. "Simple Tests for Selection Bias: Learning More from Instrumental Variables," IZA Discussion Papers 9346, Institute of Labor Economics (IZA).
    16. Thomas C. Buchmueller & Léontine Goldzahl, 2018. "The effect of organized breast cancer screening on mammography use: Evidence from France," Health Economics, John Wiley & Sons, Ltd., vol. 27(12), pages 1963-1980, December.
    17. Liran Einav & Amy Finkelstein & Mark R. Cullen, 2010. "Estimating Welfare in Insurance Markets Using Variation in Prices," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 877-921.
    18. Lawrence F. Katz & Jeffrey R. Kling & Jeffrey B. Liebman, 2001. "Moving to Opportunity in Boston: Early Results of a Randomized Mobility Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 607-654.
    19. 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.
    20. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2017. "Using Instrumental Variables for Inference about Policy Relevant Treatment Effects," NBER Working Papers 23568, National Bureau of Economic Research, Inc.
    21. Amanda E. Kowalski, 2018. "How to Examine External Validity Within an Experiment," NBER Working Papers 24834, National Bureau of Economic Research, Inc.
    22. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 555-574.
    23. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    24. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    25. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
    26. 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.
    27. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    28. Kadiyala Srikanth & Strumpf Erin, 2016. "How Effective is Population-Based Cancer Screening? Regression Discontinuity Estimates from the US Guideline Screening Initiation Ages," Forum for Health Economics & Policy, De Gruyter, vol. 19(1), pages 87-139, June.
    29. Marinho Bertanha & Guido W. Imbens, 2020. "External Validity in Fuzzy Regression Discontinuity Designs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 593-612, July.
    30. 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.
    31. Rebecca Myerson & Darius Lakdawalla & Lisandro D. Colantonio & Monika Safford & David Meltzer, 2018. "Effects of expanding health screening on treatment – What should we expect? What can we learn?," Working Papers 2018-014, Human Capital and Economic Opportunity Working Group.
    32. Thomas C. Buchmueller & Léontine Goldzahl, 2018. "The Effect of Organized Breast Cancer Screening on Mammography Use: Evidence from France," NBER Working Papers 24316, National Bureau of Economic Research, Inc.
    33. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
    34. Kim, Hyuncheol Bryant & Lee, Sun-mi, 2017. "When public health intervention is not successful: Cost sharing, crowd-out, and selection in Korea's National Cancer Screening Program," Journal of Health Economics, Elsevier, vol. 53(C), pages 100-116.
    35. 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.
    36. Olsen, Randall J, 1980. "A Least Squares Correction for Selectivity Bias," Econometrica, Econometric Society, vol. 48(7), pages 1815-1820, November.
    37. Hsiao,Cheng & Morimune,Kimio & Powell,James L. (ed.), 2001. "Nonlinear Statistical Modeling," Cambridge Books, Cambridge University Press, number 9780521662468, September.
    38. 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.
    39. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Marianne Bitler & Christopher Carpenter, 2019. "Effects of Direct Care Provision to the Uninsured: Evidence from Federal Breast and Cervical Cancer Programs," NBER Working Papers 26140, National Bureau of Economic Research, Inc.
    2. Emily Oster, 2020. "Health Recommendations and Selection in Health Behaviors," American Economic Review: Insights, American Economic Association, vol. 2(2), pages 143-160, June.
    3. Jisoo Hwang & Seung‐sik Hwang & Hyuncheol Bryant Kim & Jungmin Lee & Junseok Lee, 2024. "Risk compensation after COVID‐19 vaccination: Evidence from vaccine rollout by exact birth date in South Korea," Health Economics, John Wiley & Sons, Ltd., vol. 33(8), pages 1811-1830, August.

    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. 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.
    2. 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).
    3. 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.
    4. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    5. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    6. Patrick Kline & Christopher R. Walters, 2019. "On Heckits, LATE, and Numerical Equivalence," Econometrica, Econometric Society, vol. 87(2), pages 677-696, March.
    7. 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.
    8. Marx, Philip, 2024. "Sharp bounds in the latent index selection model," Journal of Econometrics, Elsevier, vol. 238(2).
    9. Michael R.M. Abrigo & Timothy J. Halliday & Teresa Molina, 2022. "Expanding health insurance for the elderly of the Philippines," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 500-520, April.
    10. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    11. 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.
    12. Peter Hull & Michal Kolesár & Christopher Walters, 2022. "Labor by design: contributions of David Card, Joshua Angrist, and Guido Imbens," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 603-645, July.
    13. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    14. Tymon S{l}oczy'nski, 2018. "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights," Papers 1810.01576, arXiv.org, revised May 2020.
    15. James J. Heckman, 2008. "The Principles Underlying Evaluation Estimators with an Application to Matching," Annals of Economics and Statistics, GENES, issue 91-92, pages 9-73.
    16. James J. Heckman, 2005. "Micro Data, Heterogeneity and the Evaluation of Public Policy Part 2," The American Economist, Sage Publications, vol. 49(1), pages 16-44, March.
    17. Sokbae Lee & Bernard Salanié, 2018. "Identifying Effects of Multivalued Treatments," Econometrica, Econometric Society, vol. 86(6), pages 1939-1963, November.
    18. Amanda E. Kowalski, 2018. "Extrapolation using Selection and Moral Hazard Heterogeneity from within the Oregon Health Insurance Experiment," Cowles Foundation Discussion Papers 2135, Cowles Foundation for Research in Economics, Yale University.
    19. 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.
    20. 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.

    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • I1 - Health, Education, and Welfare - - Health
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nbr:nberwo:25049. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.