Outcome Dependent Sampling for Longitudinal Binary Response Data Based on a Time-Varying Auxiliary Variable

• Activity Number: 146
• Type: Invited
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Paul Rathouz
• Companies: University of Wisconsin

Abstract

Outcome-dependent sampling (ODS) study designs are commonly implemented with rare diseases or when prospective studies are infeasible. In longitudinal data settings, when a repeatedly measured binary response is rare, an ODS design can be highly efficient for maximizing statistical information subject to resource limitations that prohibit covariate ascertainment of all observations. In this talk we will detail an ODS design where individual observations are sampled with probabilities determined by an inexpensive, time-varying auxiliary variable that is related but is not equal to the response. With the goal of validly estimating marginal model parameters based on the resulting biased sample, we propose a semi-parametric, sequential offsetted logistic regressions (SOLR) approach. Motivated by an analysis of the BioCycle Study that aims to describe the relationship between reproductive health (determined by luteinizing hormone levels) and fiber consumption, we examine properties of SOLR estimators and compare them with other common approaches.