Testing for causality and prognosis: etiological and prognostic models.

Etiological research aims to investigate the causal relationship between putative risk factors (or determinants) and a given disease or other outcome. In contrast, prognostic research aims to predict the probability of a given clinical outcome and in this perspective the pathophysiology of the disease is not an issue. Multivariate modeling is a fundamental tool both to infer causality and to investigate prognostic factors in epidemiological research.

Linear and logistic regression analysis.

In previous articles of this series, we focused on relative risks and odds ratios as measures of effect to assess the relationship between exposure to risk factors and clinical outcomes and on control for confounding. In randomized clinical trials, the random allocation of patients is hoped to produce groups similar with respect to risk factors. In observational studies, exposed and unexposed individuals may differ not only for the presence of the risk factor being tested but also for a series of other factors that are potentially related to the study outcome, thus making 'confounding' very likely.

Measures of effect: relative risks, odds ratios, risk difference, and 'number needed to treat'.

Epidemiological studies aim at assessing the relationship between exposures and outcomes. Clinicians are interested in knowing not only whether a link between a given exposure (e.g.