Outcome-related, Auxiliary Variable Sampling Designs for Longitudinal Binary Data

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Abstract

Background:

Epidemiologists have long used case–control and related study designs to enhance variability of response and information available to estimate exposure–disease associations. Less has been done for longitudinal data.

Methods:

We discuss an epidemiological study design and analysis approach for longitudinal binary response data. We seek to gain statistical efficiency by oversampling relatively informative subjects for inclusion into the sample. In this methodological demonstration, we develop this concept by sampling repeatedly from an existing cohort study to estimate the relationship of chronic obstructive pulmonary disease to past-year smoking in a panel of baseline smokers. To account for oversampling, we describe a sequential offsetted regressions approach for valid inferences in this setting.

Results:

Targeted sampling can lead to increased statistical efficiency when combined with sequential offsetted regressions. Efficiency gains are degraded with increased prevalence of the disease response variable, with decreased association between the sampling variable and the response, and with other design and analysis parameters, providing guidance to those wishing to use these types of designs in the future.

Conclusions:

These designs hold promise for efficient use of resources in longitudinal cohort studies.

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