IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v83y2021i3p482-504.html
   My bibliography  Save this article

Increasing power for observational studies of aberrant response: An adaptive approach

Author

Listed:
  • Siyu Heng
  • Hyunseung Kang
  • Dylan S. Small
  • Colin B. Fogarty

Abstract

In many observational studies, the interest is in the effect of treatment on bad, aberrant outcomes rather than the average outcome. For such settings, the traditional approach is to define a dichotomous outcome indicating aberration from a continuous score and use the Mantel–Haenszel test with matched data. For example, studies of determinants of poor child growth use the World Health Organization’s definition of child stunting being height‐for‐age z‐score ≤ − 2. The traditional approach may lose power because it discards potentially useful information about the severity of aberration. We develop an adaptive approach that makes use of this information and asymptotically dominates the traditional approach. We develop our approach in two parts. First, we develop an aberrant rank approach in matched observational studies and prove a novel design sensitivity formula enabling its asymptotic comparison with the Mantel–Haenszel test under various settings. Second, we develop a new, general adaptive approach, the two‐stage programming method, and use it to adaptively combine the aberrant rank test and the Mantel–Haenszel test. We apply our approach to a study of the effect of teenage pregnancy on stunting.

Suggested Citation

  • Siyu Heng & Hyunseung Kang & Dylan S. Small & Colin B. Fogarty, 2021. "Increasing power for observational studies of aberrant response: An adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 482-504, July.
  • Handle: RePEc:bla:jorssb:v:83:y:2021:i:3:p:482-504
    DOI: 10.1111/rssb.12424
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssb.12424
    Download Restriction: no

    File URL: https://libkey.io/10.1111/rssb.12424?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
    ---><---

    References listed on IDEAS

    as
    1. Paul R. Rosenbaum, 2011. "A New u-Statistic with Superior Design Sensitivity in Matched Observational Studies," Biometrics, The International Biometric Society, vol. 67(3), pages 1017-1027, September.
    2. Paul R. Rosenbaum, 2014. "Weighted M -statistics With Superior Design Sensitivity in Matched Observational Studies With Multiple Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1145-1158, September.
    3. Paul R. Rosenbaum, 2004. "Design sensitivity in observational studies," Biometrika, Biometrika Trust, vol. 91(1), pages 153-164, March.
    4. Zhang, Kai & Small, Dylan S. & Lorch, Scott & Srinivas, Sindhu & Rosenbaum, Paul R., 2011. "Using Split Samples and Evidence Factors in an Observational Study of Neonatal Outcomes," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 511-524.
    5. Colin B. Fogarty & Kwonsang Lee & Rachel R. Kelz & Luke J. Keele, 2021. "Biased Encouragements and Heterogeneous Effects in an Instrumental Variable Study of Emergency General Surgical Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1625-1636, October.
    6. Paul R. Rosenbaum & Dylan S. Small, 2017. "An adaptive Mantel–Haenszel test for sensitivity analysis in observational studies," Biometrics, The International Biometric Society, vol. 73(2), pages 422-430, June.
    7. Ben B. Hansen & Paul R. Rosenbaum & Dylan S. Small, 2014. "Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 133-144, March.
    8. Joseph L. Gastwirth & Abba M. Krieger & Paul R. Rosenbaum, 2000. "Asymptotic separability in sensitivity analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 545-555.
    9. Qingyuan Zhao & Dylan S. Small & Paul R. Rosenbaum, 2018. "Cross-Screening in Observational Studies That Test Many Hypotheses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1070-1084, July.
    10. Rosenbaum, Paul R., 2010. "Design Sensitivity and Efficiency in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 692-702.
    11. Bryan E. Shepherd & Peter B. Gilbert & Yannis Jemiai & Andrea Rotnitzky, 2006. "Sensitivity Analyses Comparing Outcomes Only Existing in a Subset Selected Post-Randomization, Conditional on Covariates, with Application to HIV Vaccine Trials," Biometrics, The International Biometric Society, vol. 62(2), pages 332-342, June.
    12. Dylan S. Small & Jing Cheng & M. Elizabeth Halloran & Paul R. Rosenbaum, 2013. "Case Definition and Design Sensitivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1457-1468, December.
    13. Samuel D. Pimentel & Rachel R. Kelz & Jeffrey H. Silber & Paul R. Rosenbaum, 2015. "Large, Sparse Optimal Matching With Refined Covariate Balance in an Observational Study of the Health Outcomes Produced by New Surgeons," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 515-527, June.
    14. Colin B. Fogarty & Dylan S. Small, 2016. "Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies Through Quadratically Constrained Linear Programming," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1820-1830, October.
    15. Ashkan Ertefaie & Dylan S. Small & Paul R. Rosenbaum, 2018. "Quantitative Evaluation of the Trade-Off of Strengthened Instruments and Sample Size in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1122-1134, July.
    16. Paul R. Rosenbaum, 2013. "Impact of Multiple Matched Controls on Design Sensitivity in Observational Studies," Biometrics, The International Biometric Society, vol. 69(1), pages 118-127, March.
    17. Heller, Ruth & Rosenbaum, Paul R. & Small, Dylan S., 2009. "Split Samples and Design Sensitivity in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1090-1101.
    18. Paul R. Rosenbaum, 2007. "Sensitivity Analysis for m-Estimates, Tests, and Confidence Intervals in Matched Observational Studies," Biometrics, The International Biometric Society, vol. 63(2), pages 456-464, June.
    19. Rosenbaum, Paul R. & Silber, Jeffrey H., 2008. "Aberrant Effects of Treatment," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 240-247, March.
    20. Ben B. Hansen, 2004. "Full Matching in an Observational Study of Coaching for the SAT," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 609-618, January.
    21. P. R. Rosenbaum, 2012. "Testing one hypothesis twice in observational studies," Biometrika, Biometrika Trust, vol. 99(4), pages 763-774.
    22. José R. Zubizarreta, 2012. "Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1360-1371, December.
    Full references (including those not matched with items on IDEAS)

    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. Bo Zhang & Dylan S. Small, 2020. "A calibrated sensitivity analysis for matched observational studies with application to the effect of second‐hand smoke exposure on blood lead levels in children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1285-1305, November.
    2. Siyu Heng & Dylan S. Small & Paul R. Rosenbaum, 2020. "Finding the strength in a weak instrument in a study of cognitive outcomes produced by Catholic high schools," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 935-958, June.
    3. Kwonsang Lee & Dylan S. Small & Paul R. Rosenbaum, 2018. "A powerful approach to the study of moderate effect modification in observational studies," Biometrics, The International Biometric Society, vol. 74(4), pages 1161-1170, December.
    4. Paul R. Rosenbaum, 2015. "Bahadur Efficiency of Sensitivity Analyses in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 205-217, March.
    5. Samuel D. Pimentel & Dylan S. Small & Paul R. Rosenbaum, 2016. "Constructed Second Control Groups and Attenuation of Unmeasured Biases," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1157-1167, July.
    6. Ruoqi Yu, 2021. "Evaluating and improving a matched comparison of antidepressants and bone density," Biometrics, The International Biometric Society, vol. 77(4), pages 1276-1288, December.
    7. Xinkun Nie & Guido Imbens & Stefan Wager, 2021. "Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations," Papers 2112.04723, arXiv.org.
    8. Bikram Karmakar, 2022. "An approximation algorithm for blocking of an experimental design," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1726-1750, November.
    9. Paul R. Rosenbaum, 2023. "Sensitivity analyses informed by tests for bias in observational studies," Biometrics, The International Biometric Society, vol. 79(1), pages 475-487, March.
    10. Paul R. Rosenbaum & Dylan S. Small, 2017. "An adaptive Mantel–Haenszel test for sensitivity analysis in observational studies," Biometrics, The International Biometric Society, vol. 73(2), pages 422-430, June.
    11. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
    12. Colin B. Fogarty, 2023. "Testing weak nulls in matched observational studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2196-2207, September.
    13. Paul R. Rosenbaum, 2013. "Impact of Multiple Matched Controls on Design Sensitivity in Observational Studies," Biometrics, The International Biometric Society, vol. 69(1), pages 118-127, March.
    14. Paul R. Rosenbaum, 2015. "Some Counterclaims Undermine Themselves in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1389-1398, December.
    15. Luke Keele & Steve Harris & Samuel D. Pimentel & Richard Grieve, 2020. "Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to intensive care units," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1501-1521, October.
    16. Raiden B. Hasegawa & Sameer K. Deshpande & Dylan S. Small & Paul R. Rosenbaum, 2020. "Causal Inference With Two Versions of Treatment," Journal of Educational and Behavioral Statistics, , vol. 45(4), pages 426-445, August.
    17. María de los Angeles Resa & José R. Zubizarreta, 2020. "Direct and stable weight adjustment in non‐experimental studies with multivalued treatments: analysis of the effect of an earthquake on post‐traumatic stress," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1387-1410, October.
    18. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
    19. Bo Zhang, 2023. "Efficient algorithms for building representative matched pairs with enhanced generalizability," Biometrics, The International Biometric Society, vol. 79(4), pages 3981-3997, December.
    20. Nicholas T. Longford, 2020. "Performance assessment as an application of causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1363-1385, October.

    More about this item

    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:bla:jorssb:v:83:y:2021:i:3:p:482-504. 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: https://edirc.repec.org/data/rssssea.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.