Applied Statistics and the SAS Programming Language by Jfry KSmit

By Jfry KSmit

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3. , members of racial or ethnic minorities), accurate age-specific rates may not exist. 4 BASIC EPIDEMIOLOGICAL STUDY DESIGNS Standardization provides a commonly reported means of “adjusting” observed proportions and rates for the presence of known risk factors. Statistical modeling of epidemiological data extends such ideas, allowing estimation of the impact of particular risk factors and their interactions with observed incidence (or prevalence) counts. We next consider the types of data collected in epidemiological studies, followed by a description of the analytical approaches often used to quantify such associations.

Pickle and White (1995, pp. 617–618) outline the following conditions under any one of which direct and indirect standardization produce identical (or at least proportional) results: 1. , nj /n+ = n(s) j /n+ , for j = 1, . . , J ). 2. , rj = rj(s) for j = 1, . . , J ). In this case, both direct and indirect standardized rates reduce to the crude rates from the study population. 3. , rj = αrj(s) for some constant α), the indirectly standardized rate equals the crude rate in the standard population times α.

Simply put, we seek to control confounding by careful selection and classification of study participants, but we wish to report precisely how an effect modifier changes the association between the exposures and outcome of interest. 4 Ecological Inference and the Ecological Fallacy In epidemiology, the term ecological inference refers to the process of deducing individual behavior from aggregate data. This term is due to Robinson (1950), who noted that in ecological studies, the statistical object is a group of persons.

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