IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v75y2019i1p289-296.html
   My bibliography  Save this article

Double robust estimation for multiple unordered treatments and clustered observations: Evaluating drug‐eluting coronary artery stents

Author

Listed:
  • Sherri Rose
  • Sharon‐Lise Normand

Abstract

Postmarket comparative effectiveness and safety analyses of therapeutic treatments typically involve large observational cohorts. We propose double robust machine learning estimation techniques for implantable medical device evaluations where there are more than two unordered treatments and patients are clustered in hospitals. This flexible approach also accommodates high‐dimensional covariates drawn from clinical databases. The Massachusetts Data Analysis Center percutaneous coronary intervention cohort is used to assess the composite outcome of 10 drug‐eluting stents among adults implanted with at least one drug‐eluting stent in Massachusetts. We find remarkable discrimination between stents. A simulation study designed to mimic this coronary intervention cohort is also presented and produced similar results.

Suggested Citation

  • Sherri Rose & Sharon‐Lise Normand, 2019. "Double robust estimation for multiple unordered treatments and clustered observations: Evaluating drug‐eluting coronary artery stents," Biometrics, The International Biometric Society, vol. 75(1), pages 289-296, March.
    • Handle: RePEc:bla:biomet:v:75:y:2019:i:1:p:289-296
    DOI: 10.1111/biom.12927
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.12927
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.12927?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. Sylvie Goetgeluk & Stijn Vansteelandt, 2008. "Conditional Generalized Estimating Equations for the Analysis of Clustered and Longitudinal Data," Biometrics, The International Biometric Society, vol. 64(3), pages 772-780, September.
    2. Porter Kristin E. & Gruber Susan & van der Laan Mark J. & Sekhon Jasjeet S., 2011. "The Relative Performance of Targeted Maximum Likelihood Estimators," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, August.
    3. van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
    4. Li Y.P. & Propert K. J. & Rosenbaum P. R., 2001. "Balanced Risk Set Matching," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 870-882, September.
    5. David M. Cutler & Dan P. Ly, 2011. "The (Paper)Work of Medicine: Understanding International Medical Costs," Journal of Economic Perspectives, American Economic Association, vol. 25(2), pages 3-25, Spring.
    6. Gruber, Susan & Laan, Mark van der, 2012. "tmle: An R Package for Targeted Maximum Likelihood Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i13).
    7. Rosenblum Michael & van der Laan Mark J., 2010. "Targeted Maximum Likelihood Estimation of the Parameter of a Marginal Structural Model," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-30, April.
    8. Shiquan Ren & Hong Lai & Wenjing Tong & Mostafa Aminzadeh & Xuezhang Hou & Shenghan Lai, 2010. "Nonparametric bootstrapping for hierarchical data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1487-1498.
    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. Irina Degtiar & Tim Layton & Jacob Wallace & Sherri Rose, 2023. "Conditional cross‐design synthesis estimators for generalizability in Medicaid," Biometrics, The International Biometric Society, vol. 79(4), pages 3859-3872, December.

    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. Susan Gruber & Mark J. van der Laan, 2013. "An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data," Biometrics, The International Biometric Society, vol. 69(1), pages 254-262, March.
    2. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    3. Chaffee Paul H. & van der Laan Mark J., 2012. "Targeted Maximum Likelihood Estimation for Dynamic Treatment Regimes in Sequentially Randomized Controlled Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-32, June.
    4. Ronald Herrera & Ursula Berger & Ondine S. Von Ehrenstein & Iván Díaz & Stella Huber & Daniel Moraga Muñoz & Katja Radon, 2017. "Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation," IJERPH, MDPI, vol. 15(1), pages 1-15, December.
    5. Gruber Susan & van der Laan Mark J., 2010. "An Application of Collaborative Targeted Maximum Likelihood Estimation in Causal Inference and Genomics," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-31, May.
    6. Youmi Suk & Jee-Seon Kim & Hyunseung Kang, 2021. "Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 323-347, June.
    7. Rose Sherri & van der Laan Mark J., 2011. "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, March.
    8. Youmi Suk, 2024. "A Within-Group Approach to Ensemble Machine Learning Methods for Causal Inference in Multilevel Studies," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 61-91, February.
    9. Jeremiah Jones & Ashkan Ertefaie & Susan M. Shortreed, 2023. "Rejoinder to “Reader reaction to ‘Outcome‐adaptive Lasso: Variable selection for causal inference’ by Shortreed and Ertefaie (2017)”," Biometrics, The International Biometric Society, vol. 79(1), pages 521-525, March.
    10. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
    11. Díaz Iván & Rosenblum Michael, 2015. "Targeted Maximum Likelihood Estimation using Exponential Families," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 233-251, November.
    12. Veronica Sciannameo & Gian Paolo Fadini & Daniele Bottigliengo & Angelo Avogaro & Ileana Baldi & Dario Gregori & Paola Berchialla, 2022. "Assessment of Glucose Lowering Medications’ Effectiveness for Cardiovascular Clinical Risk Management of Real-World Patients with Type 2 Diabetes: Targeted Maximum Likelihood Estimation under Model Mi," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
    13. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    14. Stitelman Ori M. & De Gruttola Victor & van der Laan Mark J., 2012. "A General Implementation of TMLE for Longitudinal Data Applied to Causal Inference in Survival Analysis," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-39, September.
    15. Stitelman Ori M & Wester C. William & De Gruttola Victor & van der Laan Mark J., 2011. "Targeted Maximum Likelihood Estimation of Effect Modification Parameters in Survival Analysis," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-34, March.
    16. van der Laan Mark J. & Gruber Susan, 2012. "Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-41, May.
    17. Ziyun Xu & Éric Archambault, 2015. "Chinese interpreting studies: structural determinants of MA students’ career choices," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1041-1058, November.
    18. Yunda Huang & Lily Zhang & Shelly Karuna & Philip Andrew & Michal Juraska & Joshua A. Weiner & Heather Angier & Evgenii Morgan & Yasmin Azzam & Edith Swann & Srilatha Edupuganti & Nyaradzo M. Mgodi & , 2023. "Adults on pre-exposure prophylaxis (tenofovir-emtricitabine) have faster clearance of anti-HIV monoclonal antibody VRC01," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    19. van der Laan Mark, 2017. "A Generally Efficient Targeted Minimum Loss Based Estimator based on the Highly Adaptive Lasso," The International Journal of Biostatistics, De Gruyter, vol. 13(2), pages 1-35, November.
    20. Brooks Jordan & van der Laan Mark J. & Go Alan S., 2012. "Targeted Maximum Likelihood Estimation for Prediction Calibration," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-35, 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:biomet:v:75:y:2019:i:1:p:289-296. 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://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.