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Activity Number: 172 - Prediction and Misclassification in Biomedical Research
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #313032
Title: Organic Direct and Indirect Effects with Mediator Values Below a Limit of Detection
Author(s): Ariel Chernofsky* and Ronald Bosch and Judith Lok
Companies: Boston University School of Public Health and Center for Biostatistics in AIDS Research, Harvard School of Public Health and Boston University Department of Mathematics & Statistics
Keywords: HIV/AIDS; causal mediation ; organic direct and indirect effects; assay limit of detection; Monte Carlo EM algorithm

The effect of a new HIV curative treatment on time to viral rebound may be mediated through the viral reservoir. Organic direct and indirect effects provide an interpretable method for quantifying the causal mediated effect. However, for many subjects the viral reservoir (the mediator) is below the assay limit of limit detection. Since the mediator is an effect of the treatment and a putative cause of the outcome, assay lower limit presents a compounded problem in mediation analysis. We propose two general approaches to address informatively missing mediator values. The first approach directly maximizes the observed data likelihood through numerical optimization. The second approach treats missing mediator values as a latent variable and estimates parameters through adaptations of the EM algorithm, such as the Monte Carlo EM algorithm. A simulation study compares both approaches to ad hoc solutions, e.g. imputing by half limit of detection. We illustrate our methods with ACTG HIV interruption study data described in [Li et al. 2016, AIDS] to estimate the indirect effect of a curative treatment on viral rebound by week 4 mediated by the viral reservoir.

Authors who are presenting talks have a * after their name.

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