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Activity Number: 655 - Improving Power and Generalizability in Causal Effect Estimation Using Multicenter and Network Designs
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #328574 Presentation
Title: Methodological Considerations for the Analysis of Relative Treatment Effects in Multi-Drug-Resistant Tuberculosis from Fused Observational Studies
Author(s): Mireille Schnitzer* and Andrea Benedetti and Guanbo Wang and Arman Alam Siddique and Asma Bahamyirou
Companies: University of Montreal and Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre and McGill University and McMaster University and Université de Montréal
Keywords: individual patient data; meta-analysis; transportability; causal inference; tuberculosis; targeted maximum likelihood estimation

Multi-drug-resistant tuberculosis (MDR-TB) is defined as strains of tuberculosis that do not respond to at least the two most used anti-TB drugs. After diagnosis, the intensive treatment phase for MDR-TB involves taking several alternative antibiotics concurrently. The Collaborative Group for Meta-analysis of Individual Patient Data in MDR-TB has assembled a large, fused dataset of over 30 observational studies comparing the effectiveness of 15 antibiotics. The particular challenges that we have considered in the analysis of this dataset are the large number of potential drug regimens, the resistance of MDR-TB strains to specific antibiotics, and the identifiability of a generalized parameter of interest though most drugs were not observed in all studies. In this talk, I describe causal inference theory and methodology that we have appropriated or developed for the estimation of treatment importance and relative effectiveness of different antibiotic regimens with a particular emphasis on targeted learning approaches.

Authors who are presenting talks have a * after their name.

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