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

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  • 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
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    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
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