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

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 indivi...

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Published in:Biometrics Vol. 76; no. 3; pp. 1007 - 1016
Main Authors: Wang, Guanbo, Schnitzer, Mireille E., Menzies, Dick, Viiklepp, Piret, Holtz, Timothy H., Benedetti, Andrea
Format: Journal Article
Language:English
Published: England Blackwell Publishing Ltd 01.09.2020
Subjects:
Antibiotic resistance
Antibiotics
Antiinfectives and antibacterials
Antimicrobial agents
Antitubercular Agents - therapeutic use
Causality
Computer simulation
Confidence intervals
double robustness
Drug resistance
drug therapy
Humans
individual patient data
Isoniazid
Maximum likelihood estimation
Meta-analysis
Multidrug resistant organisms
multidrug‐resistant tuberculosis
multiple drug resistance
Observational learning
Observational Studies as Topic
patients
rifampicin
Rifampin
statistical analysis
targeted maximum likelihood estimation
transportability
treatment importance
Tuberculosis
Tuberculosis - drug therapy
Tuberculosis, Multidrug-Resistant - drug therapy
variance
individual patient data
targeted maximum likelihood estimation
treatment importance
meta-analysis
multidrug-resistant tuberculosis
transportability
double robustness
ISSN:0006-341X, 1541-0420, 1541-0420
Online Access:Get full text
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Summary: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.
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.13210