Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Dose Size and Dosing Frequency: Determination Methods01:21

Dose Size and Dosing Frequency: Determination Methods

Determining the optimal dose size and dosing frequency in pharmacotherapy is crucial for achieving therapeutic effectiveness while minimizing adverse effects. This article explores the methodologies employed in determining these parameters, focusing on their significance and interplay to tailor dosing regimens.Dose Size: Dose size refers to the amount of a drug administered in a single dose. It is determined based on the drug's pharmacodynamics and pharmacokinetics properties and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluating the impact of conditional micro-incentives on linkage to HIV care in rural South Africa: Results from the Home-Based Intervention to Test and Start (HITS) trial.

Health policy and planning·2026
Same author

Machine Learning-Based Prediction of Dental Caries in 12-Year-Old Adolescents: A Nationwide Cross-Sectional Study in Korea.

International journal of dental hygiene·2026
Same author

Cost-Effectiveness of Differentiated Service Delivery for HIV Treatment: A Combined Mathematical Modeling Study of Four African Settings.

Open forum infectious diseases·2026
Same author

Digital-Human Public Community Care Integration for Chronic Pain in Low-Income Older Adults in a 6-Week Living Lab Setting: Quasi-Experimental Feasibility Study.

JMIR aging·2026
Same author

Treatment of increased intracranial pressure secondary to otitic hydrocephalus.

Frontiers in pediatrics·2026
Same author

How do social network models compare to all-to-all models for forecasting tuberculosis epidemics? A mathematical modeling study.

PloS one·2026

Related Experiment Video

Updated: Jul 14, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Weighting condom use data to account for nonignorable cluster size.

John M Williamson1, Hae-Young Kim, Lee Warner

  • 1National Center for Infectious Diseases, Division of Parasitic Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, GA 30341, USA. jow5@cdc.gov

Annals of Epidemiology
|May 29, 2007
PubMed
Summary

Weighting generalized estimating equations (GEE) by the number of sex acts impacts condom use predictions. Different weighting methods yield varied results, affecting the interpretation of factors influencing recent condom use.

Related Experiment Videos

Last Updated: Jul 14, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Public Health
  • Epidemiology

Background:

  • Condom use is crucial for preventing sexually transmitted infections and unintended pregnancies.
  • Generalized Estimating Equations (GEE) are commonly used for analyzing correlated binary data, such as act-specific condom use.
  • Previous analyses may not have adequately accounted for nonignorable cluster-size effects in condom use studies.

Purpose of the Study:

  • To investigate the impact of weighting generalized estimating equations (GEE) by the inverse of the number of sex acts.
  • To compare the magnitude of association for factors predicting recent condom use using different GEE weighting methods.
  • To assess how weighting affects the interpretation of results in the presence of nonignorable cluster-size data.

Main Methods:

  • Analysis of cross-sectional survey data on condom use among male university students.
  • Application of the standard GEE model to predict binary act-specific condom use.
  • Implementation of a cluster-weighted GEE model, weighting by the inverse of the number of sex acts.

Main Results:

  • Participants with higher sexual activity reported lower condom use, indicating nonignorable cluster-size data.
  • GEE analysis weighted by sex act and cluster-weighted GEE produced different effect size estimates for covariates.
  • Cluster-weighted GEE showed a marginally significant association between age and condom use (OR=0.49), unlike the standard GEE (OR=0.67).

Conclusions:

  • Weighting GEE by sex act or by respondent can lead to divergent results and parameter interpretations.
  • The choice of weighting method is critical when dealing with nonignorable cluster-size in condom use research.
  • Findings highlight the importance of appropriate statistical methods for accurate analysis of sexual behavior data.