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RESEARCH PRODUCT

Data Analysis of Epidemiological Studies

subject

description

Epidemiology is used to describe the distribution of diseases in the population and to analyze the causes of these diseases. One important objective is to identify risk factors and to quantify their significance. A risk factor can influence the probability that a specific disease develops. Risk factors include: Environmental influences (for example, exposure to radon) Predisposition (for example, genes), or Behavioral characteristics (for example, hormone intake). Epidemiological research employs various different types of study (1–3), depending on the question asked. The most important are Cohort studies Case-control studies, and Cross-sectional studies In cohort studies, persons exposed to specific risk factors are compared with persons not exposed to these factors. The occurrence of diseases or deaths in these two groups is observed prospectively. Data from cohort studies allow the estimation of incidence rate and mortality rate as descriptive measures of frequency, as well as relative risk (RR) or hazard ratio (HR) as comparative effect measures. Standardized incidence ratios (SIR) or standardized mortality ratios (SMR) are used for comparison with the general population. In case-control studies, persons suffering from the studied disease are compared with controls who do not have the disease. Exposure is recorded retrospectively. The odds ratio (OR) is calculated as a comparative effect measure. In cross-sectional studies, the exposure and disease status are examined for a sample from a defined population at the same time point. The prevalence of various diseases and the risk factors, as well as the OR can be determined. Effect estimates, such as RR, are normally calculated with regression models, taking influencing factors into consideration. These lead to statements about the extent of changes in the frequency of a disease due to a specific risk factor. To assess whether the observed effect is statistically significant, the confidence interval (CI) should, for example, be considered for all effect estimates (4). If a statement is to be made about the number of cases of the disease caused by the risk factor, then the risk difference (RD) is considered.