Implementing a multi-wave two-phase study to correct for data errors in a multinational study of HIV and tuberculosis.

Thomas M. Menino Convention & Exhibition Center 

Tuberculosis (TB) is notoriously difficult to diagnose among people with HIV, so many are started on anti-TB medications without a positive diagnosis. There is interest in assessing the association between TB diagnosis (positive, negative, indeterminant) and successful TB treatment completion (alive, without recurrence, and off TB medications 18 months after treatment start). To address this question, we have access to a large multinational cohort of 22,588 people with HIV who started TB treatment. However, these data were routinely collected (e.g., based on electronic health records) and are known to be prone to errors. I will describe our experience designing, carrying out, and analyzing data from a multi-wave, two-phase study. In short, approximately 950 records were selected for internal validation (i.e., chart review). Records were selected in three sampling waves to minimize the variance of coefficient estimates; information from prior waves was used to inform sampling in the next wave. Analyses were performed using a sieve maximum likelihood estimator, which is semiparametric efficient and makes minimal assumptions on nuisance models for the errors.