IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v76y2020i3p1007-1016.html
   My bibliography  Save this article

Estimating treatment importance in multidrug‐resistant tuberculosis using Targeted Learning: An observational individual patient data network meta‐analysis

Author

Listed:
  • Guanbo Wang
  • Mireille E. Schnitzer
  • Dick Menzies
  • Piret Viiklepp
  • Timothy H. Holtz
  • Andrea Benedetti

Abstract

Persons with multidrug‐resistant tuberculosis (MDR‐TB) have a disease resulting from a strain of tuberculosis (TB) that does not respond to at least isoniazid and rifampicin, the two most effective anti‐TB drugs. MDR‐TB is always treated with multiple antimicrobial agents. Our data consist of individual patient data from 31 international observational studies with varying prescription practices, access to medications, and distributions of antibiotic resistance. In this study, we develop identifiability criteria for the estimation of a global treatment importance metric in the context where not all medications are observed in all studies. With stronger causal assumptions, this treatment importance metric can be interpreted as the effect of adding a medication to the existing treatments. We then use this metric to rank 15 observed antimicrobial agents in terms of their estimated add‐on value. Using the concept of transportability, we propose an implementation of targeted maximum likelihood estimation, a doubly robust and locally efficient plug‐in estimator, to estimate the treatment importance metric. A clustered sandwich estimator is adopted to compute variance estimates and produce confidence intervals. Simulation studies are conducted to assess the performance of our estimator, verify the double robustness property, and assess the appropriateness of the variance estimation approach.

Suggested Citation

  • Guanbo Wang & Mireille E. Schnitzer & Dick Menzies & Piret Viiklepp & Timothy H. Holtz & Andrea Benedetti, 2020. "Estimating treatment importance in multidrug‐resistant tuberculosis using Targeted Learning: An observational individual patient data network meta‐analysis," Biometrics, The International Biometric Society, vol. 76(3), pages 1007-1016, September.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:3:p:1007-1016
    DOI: 10.1111/biom.13210
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13210
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13210?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. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Kara E. Rudolph & Mark J. Laan, 2017. "Robust estimation of encouragement design intervention effects transported across sites," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1509-1525, November.
    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. van der Laan Mark J., 2006. "Statistical Inference for Variable Importance," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-33, February.
    5. Shama D Ahuja & David Ashkin & Monika Avendano & Rita Banerjee & Melissa Bauer & Jamie N Bayona & Mercedes C Becerra & Andrea Benedetti & Marcos Burgos & Rosella Centis & Eward D Chan & Chen-Yuan Chia, 2012. "Multidrug Resistant Pulmonary Tuberculosis Treatment Regimens and Patient Outcomes: An Individual Patient Data Meta-analysis of 9,153 Patients," PLOS Medicine, Public Library of Science, vol. 9(8), pages 1-16, August.
    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. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    2. S Ariane Christie & Amanda S Conroy & Rachael A Callcut & Alan E Hubbard & Mitchell J Cohen, 2019. "Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.
    3. Kara E. Rudolph & Jonathan Levy & Mark J. van der Laan, 2021. "Transporting stochastic direct and indirect effects to new populations," Biometrics, The International Biometric Society, vol. 77(1), pages 197-211, March.
    4. Tuglus Catherine & van der Laan Mark J., 2011. "Repeated Measures Semiparametric Regression Using Targeted Maximum Likelihood Methodology with Application to Transcription Factor Activity Discovery," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-31, January.
    5. Rose Sherri & van der Laan Mark J., 2008. "Simple Optimal Weighting of Cases and Controls in Case-Control Studies," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-24, September.
    6. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    7. Antoine Chambaz & Mark J. Laan, 2014. "Inference in Targeted Group-Sequential Covariate-Adjusted Randomized Clinical Trials," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 104-140, March.
    8. Iván Díaz & Alan Hubbard & Anna Decker & Mitchell Cohen, 2015. "Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
    9. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
    10. Geeven Geert & van der Laan Mark J. & de Gunst Mathisca C.M., 2012. "Comparison of Targeted Maximum Likelihood and Shrinkage Estimators of Parameters in Gene Networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-29, September.
    11. Elise D Riley & Torsten B Neilands & Kelly Moore & Jennifer Cohen & David R Bangsberg & Diane Havlir, 2012. "Social, Structural and Behavioral Determinants of Overall Health Status in a Cohort of Homeless and Unstably Housed HIV-Infected Men," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    12. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    13. Tuglus Catherine & van der Laan Mark J., 2009. "Modified FDR Controlling Procedure for Multi-Stage Analyses," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-15, February.
    14. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    15. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
    16. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    17. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    18. Masakazu Higuchi & Mitsuteru Nakamura & Shuji Shinohara & Yasuhiro Omiya & Takeshi Takano & Daisuke Mizuguchi & Noriaki Sonota & Hiroyuki Toda & Taku Saito & Mirai So & Eiji Takayama & Hiroo Terashi &, 2022. "Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach," IJERPH, MDPI, vol. 19(18), pages 1-13, September.
    19. Vincent, Martin & Hansen, Niels Richard, 2014. "Sparse group lasso and high dimensional multinomial classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 771-786.
    20. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.

    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:76:y:2020:i:3:p:1007-1016. 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.