2015-09-25

Jonathan S. Schildcrout, PhD, Department of Biostatistics, Vanderbilt University School of Medicine

"Outcome related sampling designs for longitudinal binary data with application to spirometry-based COPD diagnosis"

We discuss an epidemiological study design and analyses approach for longitudinal binary response data.  Similar to other epidemiological study designs, we seek to gain efficiency and increase power compared to standard designs by over-sampling relatively informative subjects for inclusion into the sample.  In particular, we will discuss a design that conducts a case-control sample (i.e., sample cases with high probability and control with low probability); however, subjects are then followed longitudinally and case-control status is observed repeatedly for each subject.   If the sampling variable (case-control status at baseline) is closely related to the binary response (case-control status over time), we are able to observe a sample that is highly enriched with case-visits compared to a standard random sampling design.  We may therefore realize a substantial improvements in power and efficiency.  However, because the design over-samples case-days, we must acknowledge the biased sample when conducting statistical analyses.  We will describe a sequentially offsetted regression approach for valid inferences.  Motivated by data provided by the Lung Health Study we will show that targeted sampling designs can yield valid inferences and can be far more resource efficient than standard random sampling designs for longitudinal data.

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