Building bridges between populations and samples in epidemiological studies.
The increased use of rigorous population-sampling methods and the analysis of data from those samples in cross-sectional surveys, case-control studies, longitudinal-cohort investigations, and other epidemiological research efforts have raised important statistical issues for health analysts. We describe the origin, implications, and some plausible resolutions for several of these issues. Some of the main issues we consider include (a) establishing whom the sample represents; (b) using sample weights; (c) understanding the role of other important features, such as the use of sampling stratification and the selection of clustered groups of population members; and (d) finding ways to analyze study data with key sampling features in mind. Ultimately, resolution of all of these issues requires that analysts clearly define a reference population and then understand the role of design features in relating sample results to that population.
Kalsbeek
W
W
Department of Biostatistics, School of Public Health, University of North Carolina, Chapel Hill 27599-2400, USA. bill_kalsbeek@unc.edu
Heiss
G
G
D015982:Bias; D016000:Cluster Analysis; D003625:Data Collection / Q000379:methods; D003627:Data Interpretation, Statistical; D015340:Epidemiologic Research Design; D016021:Epidemiologic Studies; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D011897:Random Allocation; D018401:Sample Size; D012494:Sampling Studies