ArticleEpidemiology (Cambridge, Mass.)2018
Outcome-related, Auxiliary Variable Sampling Designs for Longitudinal Binary Data.
Article in Epidemiology (Cambridge, Mass.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Reconciling Binary Replicates: Beyond the Average.Statistics in medicine · 2026Article
- Statistical Methods for Binary Outcomes Adjusting for Outcome Dependent Sampling in Longitudinal Studies with Nonignorable Dropout.bioRxiv : the preprint server for biology · 2025Article
- Two-Phase, Generalized Case-Control Designs for the Study of Quantitative Longitudinal Outcomes.American journal of epidemiology · 2020Article
- Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use.Genetics · 2019Article
- New Designs for New Epidemiology.Epidemiology (Cambridge, Mass.) · 2018Article
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4 authors.
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Abstract
backgroundEpidemiologists 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.
methodsWe 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.
resultsTargeted 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.
conclusionsThese designs hold promise for efficient use of resources in longitudinal cohort studies.
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