Standardization of non-aggregated data: theory and practice
R Weitkunat1, A Crispin, E Grill
1Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Universität München, Marchioninistr. 15, 81377 Munich, Germany. weit@ibe.med.uni-muenchen.de
Abstract:
Direct standardization of rates is a classical method in epidemiology by which the relationship between confounders and variables of analysis is balanced between the samples or populations to be compared. While the method is appropriate if single variables of aggregated data sets are to be compared, it is limited with respect to handling many variables of different measurement scale, especially in primary data situations. Therefore the method of direct standardization is adapted to the analysis of non-aggregated data. Computation of not only rates but other statistics of central tendency and dispersion as well as confidence intervals hence becomes feasible. In addition it is possible to re-adjust the primary data weights in order to perform stratified analyses. A procedure to do so, based solely on the available original weights without the need for analyzing the standard population is proposed along with an extensive SAS macro. The utility allows for the rapid and standardized analysis and tabulation of large multi-variable data sets. Both the statistical and technical properties of the macro are discussed and its usage is explained as well as exemplified.
Related Concept Videos
Introduction to Normal Distributions
Variation: Normal Distribution, Range, and Standard Deviation
Nominal Level of Measurement
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal scale is...
Central Limit Theorem
The sample size, n, that...
Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Data: Types and Distribution
Distributions in...

