02. Accounting for Nonignorable Dropout in Longitudinal Studies using Outcome Dependent Sampling

Clinical trials and cohort studies often collect clinical data paired with stored biospecimens. An increasing focus of biomedical research is aimed at leveraging these existing specimens to address new and important research questions. When hypotheses of interest proposes to utilize costly, limited or difficult to obtain samples (e.g. peripheral blood mononuclear cells), informed sampling strategies (ISS) can be used to minimize costs and preserve biospecimens by providing methods to select more informative samples of subjects. The resulting data can be assayed and analyzed in concert with an analytical correction. Dropout is common in longitudinal studies but existing ISS methods assume complete follow-up on all individuals. Ignoring cases where poor outcomes may influence the propensity to dropout, such as persons with HIV (PWH), may bias study results. We propose an expansion to current ISS frameworks to include dropout. Mixture models, commonly used to adjust for informative dropout, are modified to accommodate analysis of data from our design. A software package, developed in R, is used to facilitate analyses.

informative dropout

informed sampling strategies

Mixture models

Case-control