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Optimizing the Design of Latent Tuberculosis Treatment Trials: Insights from Mathematical Modeling.

Publication ,  Journal Article
Stout, JE; Turner, NA; Belknap, RW; Horsburgh, CR; Sterling, TR; Phillips, PPJ
Published in: Am J Respir Crit Care Med
March 1, 2020

Rationale: Noninferiority trials of treatment for latent tuberculosis infection (LTBI) are challenging because of imperfect LTBI diagnostic tests.Objectives: To assess the effect on study outcomes of different enrollment strategies for a noninferiority trial of LTBI treatment.Methods: We mathematically simulated a two-arm randomized clinical trial of LTBI in which the experimental therapy was 50% efficacious and the control was 80% efficacious, with an absolute 0.75% noninferiority margin. Five enrollment strategies were assessed: 1) enroll based on no LTBI diagnostic test; 2) enroll based on a positive tuberculin skin test (TST); 3) enroll based on a positive IFN-γ release assay (IGRA); 4) enroll based on either a positive TST or IGRA; and 5) enroll regardless of test result, assuming 70% had negative TSTs, 20% positive TSTs, and 10% unknown results.Measurements and Main Results: Under most LTBI prevalence assumptions, enrolling based on a positive IGRA was least likely to result in falsely declaring noninferiority of the experimental regimen. Enrolling based on no test or regardless of test result led to falsely declaring noninferiority unless LTBI prevalence in the underlying population was higher than 45%. Enrolling based on a mix of TST and IGRA substantially reduced the likelihood of falsely declaring noninferiority over enrolling based on TST alone, even if as many as 70% of participants were enrolled based on positive TST.Conclusions: Noninferiority trials of LTBI should enroll based on the most specific diagnostic tests available (i.e., IGRAs) to avoid misclassifying inferior treatment regimens as noninferior.

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Published In

Am J Respir Crit Care Med

DOI

EISSN

1535-4970

Publication Date

March 1, 2020

Volume

201

Issue

5

Start / End Page

598 / 605

Location

United States

Related Subject Headings

  • Tuberculin Test
  • Sensitivity and Specificity
  • Respiratory System
  • Prevalence
  • Models, Theoretical
  • Latent Tuberculosis
  • Interferon-gamma Release Tests
  • Humans
  • Equivalence Trials as Topic
  • 3202 Clinical sciences
 

Citation

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Stout, J. E., Turner, N. A., Belknap, R. W., Horsburgh, C. R., Sterling, T. R., & Phillips, P. P. J. (2020). Optimizing the Design of Latent Tuberculosis Treatment Trials: Insights from Mathematical Modeling. Am J Respir Crit Care Med, 201(5), 598–605. https://doi.org/10.1164/rccm.201908-1606OC
Stout, Jason E., Nicholas A. Turner, Robert W. Belknap, C Robert Horsburgh, Timothy R. Sterling, and Patrick P. J. Phillips. “Optimizing the Design of Latent Tuberculosis Treatment Trials: Insights from Mathematical Modeling.Am J Respir Crit Care Med 201, no. 5 (March 1, 2020): 598–605. https://doi.org/10.1164/rccm.201908-1606OC.
Stout JE, Turner NA, Belknap RW, Horsburgh CR, Sterling TR, Phillips PPJ. Optimizing the Design of Latent Tuberculosis Treatment Trials: Insights from Mathematical Modeling. Am J Respir Crit Care Med. 2020 Mar 1;201(5):598–605.
Stout, Jason E., et al. “Optimizing the Design of Latent Tuberculosis Treatment Trials: Insights from Mathematical Modeling.Am J Respir Crit Care Med, vol. 201, no. 5, Mar. 2020, pp. 598–605. Pubmed, doi:10.1164/rccm.201908-1606OC.
Stout JE, Turner NA, Belknap RW, Horsburgh CR, Sterling TR, Phillips PPJ. Optimizing the Design of Latent Tuberculosis Treatment Trials: Insights from Mathematical Modeling. Am J Respir Crit Care Med. 2020 Mar 1;201(5):598–605.

Published In

Am J Respir Crit Care Med

DOI

EISSN

1535-4970

Publication Date

March 1, 2020

Volume

201

Issue

5

Start / End Page

598 / 605

Location

United States

Related Subject Headings

  • Tuberculin Test
  • Sensitivity and Specificity
  • Respiratory System
  • Prevalence
  • Models, Theoretical
  • Latent Tuberculosis
  • Interferon-gamma Release Tests
  • Humans
  • Equivalence Trials as Topic
  • 3202 Clinical sciences