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Relative risk (cohort studies only): (a/(a + b))/(c/(c + d)) Risk difference (cohort studies only): (a/(a + b)) — (c/(c + d)) Odds ratio (cohort and case-control studies): ad/bc

Relative risk (cohort studies only): (a/(a + b))/(c/(c + d)) Risk difference (cohort studies only): (a/(a + b)) — (c/(c + d)) Odds ratio (cohort and case-control studies): ad/bc studies, utmost care has to be taken that the controls are selected in such a way that they can be assumed to be representative of the population base from which the cases are recruited. This is often best done in a population-based study. For example, one might decide to recruit all cases diagnosed with a certain disease within some time window in a state, and to select a control group of comparable age and sex by some stratified random sampling from the general population of the same state. Case-control studies usually comprise more cases with age-related diseases than the current cohort studies. Several hundreds of cases and a similar (or an up to four times larger) number of controls are typically included, but even much larger studies are needed for the assessment of specific questions, such as the interaction of certain environmental and genetic factors in the development of chronic diseases.

Cohort studies

In cohort studies, participants are classified with respect to potential risk factors or protective factors in the first place, and they are then followed over time with respect to the occurrence of certain health outcomes. Unlike case-control studies, this study design allows for the assessment of etiologic factors of multiple health outcomes (all of which may be ascertained during follow-up), but it may be inefficient for rare health outcomes. Given that the frequency of multimorbidity strongly increases with age, the former argument strongly favors the use of cohort studies in epidemiologic aging research in many instances. Also, the quality of data on risk factors or protective factors is often superior in cohort studies, where this information is prospectively collected, compared to case-control studies, where such information has to be collected retrospectively, often over many decades. The cohort approach also provides more unequivocal evidence regarding the temporal relation between the occurrence of putative risk or preventive factors and the health outcome, and is therefore more suitable for establishing causal effects. Cohort studies do have their drawbacks, though, as conduction of large-scale cohort studies poses major challenges in terms of costs and logistics. Typical cohort studies include thousands (sometimes even tens of thousands and occasionally even hundreds of thousands) of participants who are followed over many years.

Prominent examples of cohort studies in aging research include the Baltimore Longitudinal Study on Aging, the Longitudinal Aging Study Amsterdam, the Rotterdam Study, the Health and Retirement Study (USA), and many others. A comprehensive overview of longitudinal studies on aging has been prepared by Health Canada and can be accessed online (Health Canada, 2002).

Cross-sectional studies Somewhere in-between cohort studies and case-control studies are cross-sectional studies, in which the prevalence of certain health outcomes is assessed in relation with the prevalence of certain putative risk factors or protective factors at a given point of time. This approach is often less challenging in terms of costs and logistics, particularly in comparison with cohort studies, but is more prone to certain biases, which may be particularly relevant at old age. For example, the association between physical inactivity and presence of a certain disease, such as cardiovascular disease, may be difficult to interpret in a cross-sectional study, as temporality and causality of the association may be ambiguous.

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