Related Experiment Video
Updated: Apr 28, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Logistic analysis of epidemiologic studies with augmentation sampling involving re-stratification and population
Yan Li1, Mahboobeh Safaeian2, Hilary A Robbins2
1Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742, USA yli6@umd.edu.
Abstract:
Epidemiologic cross-sectional, case-cohort, or case-control studies often select augmentation samples to supplement an existing (baseline) sample, primarily for the two reasons: (1) to increase the sample sizes from certain subdomains of interest that were not originally considered in the design of the baseline study and (2) to obtain samples from an extension of the target population. To address these two objectives, two-stage stratified sample designs are considered, where the stratification based on the expanded population at the second stage is not nested in the first stage strata. The sample weighting and Taylor linearization variance estimation for the two-stage stratified sample designs, involving re-stratification and population expansion, are provided for estimating population totals and logistic regression coefficients. Results from limited simulation studies and a logistic regression analysis of a study of human papillomavirus serology are provided.
More Related Videos
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
08:03Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
Published on: December 7, 2021
Related Concept Videos
Statistical Methods for Analyzing Epidemiological Data
Analysis of Population Pharmacokinetic Data
Stratified Sampling Method
To choose a stratified sample, divide the population into groups called strata and then take a...
Steps in Outbreak Investigation
Population Growth
Modeling with Differential Equations