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We detail study design options that generalize case-control sampling when longitudinal outcome data are already collected as part of a primary cohort study, but new exposure data must be retrospectively processed for a secondary analysis. Furthermore, we assume that cost will limit the size of the subsample that can be evaluated. We describe a novel class of stratified outcome-dependent sampling designs for longitudinal binary response data where distinct strata are created for subjects who never, sometimes, and always experienced the event of interest during longitudinal follow-up. Individual designs within this class are differentiated by the stratum-specific sampling probabilities. We show for parameters associated with time-varying exposures, subjects who experience the event/outcome at some but not at all of the follow-up times (i.e., those who exhibit response variation) are highly informative. If the time-varying exposure varies exclusively within individuals (i.e., intraclass correlation coefficient is 0), then sampling all subjects with response variability can yield highly precise parameter estimates even when compared with an analysis of the original cohort. The flexibility of the designs and analysis procedures also permits estimation of parameters that correspond to time-fixed covariates, and we show that with an imputation-based estimation procedure, baseline covariate associations can be estimated with very high precision irrespective of the design. We demonstrate features of the designs and analysis procedures via a plasmode simulation using data from the Lung Health Study. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Recent epidemiologic studies have found that self-reported duration of sleep is associated with obesity, diabetes, hypertension, and mortality. The extent to which self reports of sleep duration are similar to objective measures and whether individual characteristics influence the degree of similarity are not known.Methods: Eligible participants at the Chicago site of the Coronary Artery Risk Development in Young Adults Study were invited to participate in a 2003-2005 ancillary sleep study; 82% (n = 669) agreed. Sleep measurements collected in 2 waves included 3 days each of wrist actigraphy, a sleep log, and questions about usual sleep duration. We estimate the average difference and correlation between subjectively and objectively measured sleep by using errors-in-variables regression models.Results: Average measured sleep was 6 hours, whereas the average from subjective reports was 6.8 hours. Subjective reports increased on average by 34 minutes for each additional hour of measured sleep. Overall, the correlation between reported and measured sleep duration was 0.47. Our model suggests that persons sleeping 5 hours over-reported their sleep duration by 1.2 hours, and those sleeping 7 hours over-reported by 0.4 hours. The correlations and average differences between self-reports and measured sleep varied by health, sociodemographic, and sleep characteristics.Conclusion: In a population-based sample of middle-aged adults, subjective reports of habitual sleep are moderately correlated with actigraph-measured sleep, but are biased by systematic over-reporting. The true associations between sleep duration and health may differ from previously reported associations between self-reported sleep and health. [ABSTRACT FROM AUTHOR]