
Contents
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Introduction to cohort studies Introduction to cohort studies
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Types of cohort studies Types of cohort studies
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Design issues in cohort studies Design issues in cohort studies
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Study population Study population
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Baseline visit Baseline visit
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Follow-up Follow-up
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Types and measures of exposures Types and measures of exposures
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Types of exposures Types of exposures
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Measures of exposures Measures of exposures
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Timescales and types of outcomes Timescales and types of outcomes
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Time to event Time to event
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Types of timescales Types of timescales
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Incomplete observation from the origin and to the event Incomplete observation from the origin and to the event
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Change of biomarkers Change of biomarkers
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Analysis Analysis
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Analysis based on person-time Analysis based on person-time
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Calculation of person-time and rates Calculation of person-time and rates
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Univariate analysis Univariate analysis
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Internal comparison Internal comparison
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External comparison External comparison
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Multivariable analysis Multivariable analysis
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Survival analysis Survival analysis
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Descriptors of the distribution of times to event Descriptors of the distribution of times to event
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Estimation of the survival function Estimation of the survival function
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Univariate analysis Univariate analysis
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Multivariable analysis Multivariable analysis
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Longitudinal data analysis Longitudinal data analysis
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Joint analysis of survival and longitudinal data Joint analysis of survival and longitudinal data
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Validity and biases Validity and biases
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Biases Biases
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Applications and conclusions Applications and conclusions
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References References
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Cite
Abstract
The simplest cohort design is to obtain exposure data at baseline and follow-up individuals to obtain data up to the point when the event of interest occurs. A richer design includes regularly scheduled visits at which data on exposures are updated. The exposures can be either fixed over time (e.g. race), change directly with time (e.g. age and calendar), or change at their own pace (e.g. biological markers). According to the scientific aims of a cohort study, disease occurrence can be measured as an event in person-time, time-to-endpoint of interest, or change in a biomarker repeatedly measured at follow-up visits. Analytical methods include survival analyses to handle censored observations and late entries due to incomplete observation of the development of events and origin, and longitudinal data analyses for the trajectories of markers of disease progression. Stratification, multivariate regression, and causal inference methods are key tools to accomplish comparability among exposed and unexposed groups. Identification of exposures and risk factors for disease provides a basis for prevention strategies. Data from cohort studies can be used to assess the effects of interventions by using data at the individual level to determine individual effectiveness or by comparing occurrence of disease in the population when typically none or only a few are intervened to determine population effectiveness.
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