Abstract
BackgroundMultidrug-resistant tuberculosis (MDR-TB) cohorts often lack long-term survival data, and are summarized instead by initial treatment outcomes. When using Cox proportional hazards models to analyze these cohorts, this leads to censoring subjects at the time of the initial treatment outcome, instead of them providing full survival data. This may violate the non-informative censoring assumption of the model and may produce biased effect estimates. To address this problem, we develop a tool to predict vital status at the end of a cohort period using the initial treatment outcome and assess its ability to reduce bias in treatment effect estimates.MethodsWe derive and apply a logistic regression model to predict vital status at the end of the cohort period and modify the unobserved survival outcomes to better match the predicted survival experience of study subjects. We compare hazard ratio estimates for effect of an aggressive treatment regimen from Cox proportional hazards models using time to initial treatment outcome, predicted vital status, and true vital status at the end of the cohort period.ResultsModels fit from initial treatment outcomes underestimate treatment effects by up to 22.1%, while using predicted vital status reduced this bias by 5.4%. Models utilizing the predicted vital status produce effect estimates consistently stronger and closer to the true treatment effect than estimates produced by models using the initial treatment outcome.ConclusionsAlthough studies often use initial treatment outcomes to estimate treatment effects, this may violate the non-informative censoring assumption of the Cox proportional hazards model and result in biased treatment effect estimates. Using predicted vital status at the end of the cohort period may reduce this bias in the analyses of MDR-TB treatment cohorts, yielding more accurate, and likely larger, treatment effect estimates. Further, these larger effect sizes can have downstream impacts on future study design by increasing power and reducing sample size needs.
Highlights
Multidrug-resistant tuberculosis (MDR-TB) cohorts often lack long-term survival data, and are summarized instead by initial treatment outcomes
This cohort has patient data available up to six years after treatment initiation. These data include the date that the initial treatment outcome was assigned and date on which long-term vital status was assessed. This long-term vital status is rarely available in such cohorts and provides a unique opportunity to assess the true outcome of patients after MDR-TB treatment and the relationship between the initial treatment outcome and this outcome after longer follow-up
Cohort participants were included if they had baseline MDR-TB, if data were available regarding treatment start and initial treatment outcome, and if vital status at the end of the study cohort period was discernable
Summary
Multidrug-resistant tuberculosis (MDR-TB) cohorts often lack long-term survival data, and are summarized instead by initial treatment outcomes. When using Cox proportional hazards models to analyze these cohorts, this leads to censoring subjects at the time of the initial treatment outcome, instead of them providing full survival data. This may violate the non-informative censoring assumption of the model and may produce biased effect estimates. The advent of two novel MDR-TB drugs [2, 3] and shortened regimens [4] offer opportunities for improved treatment access and outcomes These developments further intensify the need for accurate estimation of treatment effectiveness. A common approach to assessing the effects of MDR-TB treatment on the risk of death is the Cox proportional hazards model [5], in part because of the variable treatment duration and for the ability to allow each individual to contribute survival time [6]
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