Comparing Statistical Models for Analysis of Early Fungicidal Activity Data

Music City Center 

Cryptococcal meningitis (CM) is an infection of the brain that causes over 100,000 HIV-related deaths each year. In clinical studies that aim to evaluate treatment efficacy for CM, early fungicidal activity (EFA) during the first 2 weeks of therapy has been used as a standard measure of the rate of Cryptococcus clearance from longitudinally measured cerebrospinal fluid. In the CM literature, EFA has been estimated using simple linear regression (SLR). However, recent studies have also utilized linear mixed models (LMM) to estimate EFA. While the two models produce quite different estimates in the literature, there has not been a systematic comparison between the approaches. To address this, we conducted a series of simulations to empirically assess the performance of each model under various scenarios. We also compare the two models using real data from CM Phase II Clinical Trial ENACT. Our analysis found that the use of LMM for EFA estimation may produce a biased estimate, especially for subjects who achieved sterility faster. However, when comparing the treatment difference across two arms, the LMM is more efficient than the SLR approach in scenarios with presence of outliers.

HIV

cryptococcal meningitis

early fungicidal activity

clinical trial

longitudinal data

infectious disease 

Biopharmaceutical Section