On the analysis of tuberculosis studies with intermittent missing sputum data

TL;DR

This paper develops methods to analyze tuberculosis treatment studies with intermittent missing sputum data, proposing a novel inference approach that accounts for missingness and applying it to a Brazilian trial.

Contribution

It introduces a new inference framework for TB studies with missing sputum data, including sensitivity analysis, and demonstrates its application on real trial data.

Findings

Proposed a benchmark-based inference method for missing sputum data.

Developed a sensitivity analysis framework for departures from assumptions.

Applied methods to real TB trial data from Brazil.

Abstract

In randomized studies evaluating treatments for tuberculosis (TB), individuals are scheduled to be routinely evaluated for the presence of TB using sputum cultures. One important endpoint in such studies is the time of culture conversion, the first visit at which a patient's sputum culture is negative and remains negative. This article addresses how to draw inference about treatment effects when sputum cultures are intermittently missing on some patients. We discuss inference under a novel benchmark assumption and under a class of assumptions indexed by a treatment-specific sensitivity parameter that quantify departures from the benchmark assumption. We motivate and illustrate our approach using data from a randomized trial comparing the effectiveness of two treatments for adult TB patients in Brazil.