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Time for a change: considering regimen changes in analyses of observational drug-resistant TB treatment cohort data

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Randomized clinical trials represent the gold standard in therapeutic research. Nevertheless, observational cohorts of patients treated for multidrug-resistant TB (MDR-TB) or rifampin-resistant TB (RR-TB) also play an important role in generating evidence to guide drug-resistant TB care. Generally, summary exposure classifications (e.g., ‘ever vs. never´, ‘exposed at baseline´) have been used to characterize drug exposure in the absence of detailed longitudinal data on MDR-TB regimen changes. These summary classifications, along with an absence of data on covariates that change throughout the course of treatment, constrain researchers´ ability to answer the most relevant questions while accounting for known biases. In this paper, we highlight the importance of regimen changes in improving inference from observational studies of longer MDR-TB treatment regimens, and offer an overview of the data and analytic strategies required to do so.

Keywords: bias; causal inference; confounding; misclassification; multidrug-resistant tuberculosis

Document Type: Research Article

Affiliations: 1: Department of Global Health Social Medicine, Harvard Medical School, Boston, MA 2: Department of Global Health Social Medicine, Harvard Medical School, Boston, MA, Division of Global Health Equity, Brigham and Women´s Hospital, Boston, MA, USA

Publication date: 01 November 2020

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