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

Stratified Sampling Method01:16

Stratified Sampling Method

16.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
16.2K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

711
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
711
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

548
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...
548
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

742
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
742
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.1K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.1K
Actuarial Approach01:20

Actuarial Approach

374
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
374

You might also read

Related Articles

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

Sort by
Same author

Population-level health gains from PM2.5 reduction in an intermediate-pollution region: A multistate analysis of metabolic multimorbidity in Korea.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

A causal inference framework for poststratification: a method for improving external validity in epidemiological studies.

BMC medical research methodology·2026
Same author

Normal liver enzymes do not indicate safety from alcohol-related liver disease: evidence from a Korean nationwide cohort.

Epidemiology and health·2026
Same author

Population-attributable Fractions of Lifestyle Factors for Prediabetes in Korea: A Regression-based Analysis of National Survey Data.

Journal of preventive medicine and public health = Yebang Uihakhoe chi·2025
Same author

Mental Disorders Mediate the Relationship Between Adverse Childhood Experiences and Suicidal Behavior in a High-risk Population: A Counterfactual Analysis From Jeju Island.

Journal of preventive medicine and public health = Yebang Uihakhoe chi·2025
Same author

Association between parental and child influenza vaccination: A national health survey analysis.

Vaccine·2025

Related Experiment Video

Updated: Apr 6, 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

15.5K

Illustrating Poststratification Methods in Medical Claims Data: A Korean Example.

Yeon Woo Oh1,2

  • 1From the Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, South Korea.

Epidemiology (Cambridge, Mass.)
|April 4, 2026
PubMed
Summary

Poststratification methods effectively adjust for sampling bias in health examination data, improving population-level estimates for obesity prevalence. This technique enhances the generalizability of findings from nonprobability samples in epidemiological research.

Keywords:
Diagnostic screening programsObservational studyPoststratificationSelection bias

More Related Videos

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

1.0K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

863

Related Experiment Videos

Last Updated: Apr 6, 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

15.5K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

1.0K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

863

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Observational studies often use nonprobability samples, limiting generalizability.
  • Health examination data (e.g., NHIS) can suffer from voluntary participation bias.
  • Poststratification reweights samples to match population distributions, addressing bias.

Purpose of the Study:

  • To evaluate poststratification methods for correcting sampling bias in Korean National Health Insurance Service (NHIS) data.
  • To estimate obesity prevalence in adults aged 20-39 years using NHIS data.
  • To assess the accuracy of self-reported disease history after applying poststratification.

Main Methods:

  • Compared simple poststratification, raking, and multilevel regression with poststratification.
  • Used NHIS-National Sample Cohort (NHIS-NSC) data and KNHANES as a reference.
  • Applied methods to estimate obesity prevalence and evaluate self-reported disease history accuracy.

Main Results:

  • Crude obesity prevalence in NHIS-NSC (36.3%) was higher than KNHANES (31.4%).
  • Poststratification methods (simple, raking, multilevel) reduced estimates to 33.9%-34.7%, comparable to inverse probability weights.
  • Poststratification showed modest decreases in sensitivity for self-reported diseases, suggesting better accuracy in health examination data.

Conclusions:

  • Poststratification is a valuable framework for improving inferences from nonprobability samples.
  • These methods enhance the generalizability of epidemiological research using administrative and EHR data.
  • Broader application of poststratification is recommended for health data analysis.