Related Experiment Video
Updated: Jul 13, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Improved procedures for estimation of disease prevalence using ranked set sampling
Haiying Chen1, Elizabeth A Stasny, Douglas A Wolfe
1Department of Biostatistical Sciences, Wake Forest University, Winston Salem, NC 27157, USA. hchen@wfubmc.edu
Abstract:
Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). When the variable of interest is binary, ranking of the sample observations can be implemented using the estimated probabilities of success obtained from a logistic regression model developed for the binary variable. The main objective of this study is to use substantial data sets to investigate the application of RSS to estimation of a proportion for a population that is different from the one that provides the logistic regression. Our results indicate that precision in estimation of a population proportion is improved through the use of logistic regression to carry out the RSS ranking and, hence, the sample size required to achieve a desired precision is reduced. Further, the choice and the distribution of covariates in the logistic regression model are not overly crucial for the performance of a balanced RSS procedure.
Related Concept Videos
Ranks
Sampling Plans
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Stratified Sampling Method
To choose a stratified sample, divide the population into groups called strata and then take a...
Statistical Methods for Analyzing Epidemiological Data
Systematic Sampling Method
Systematic sampling is one of the simplest methods...
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

