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

Causality in Epidemiology01:21

Causality in Epidemiology

847
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
847
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

536
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
536
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

262
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
262
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

683
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
683
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

298
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
298
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

287
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...
287

You might also read

Related Articles

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

Sort by
Same author

Health literacy among residents in communities implementing healthy community initiatives: a cross-sectional study.

Frontiers in public health·2026
Same author

Combination therapy with moderate-intensity statins and ezetimibe and risk of incident PCI/CABG in atherosclerotic cardiovascular disease: a propensity-matched cohort study.

The Lancet regional health. Western Pacific·2026
Same author

CARS_SPA optimized UV-Vis spectroscopy for rapid and robust COD prediction in water samples.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Responses of the plant community characteristics and diversity of abandoned grasslands in the Loess Hilly Region in China to slope aspect and year of abandonment.

Frontiers in plant science·2026
Same author

Aggregation-induced electrochemiluminescence of zirconium metal-organic framework with strain-promoted azide-alkyne cycloaddition ligated DNA tetrahedral nanotags for microRNA detection.

Biosensors & bioelectronics·2025
Same author

A simple, wedged DNA walker electrochemical biosensor-enabled DNA logic system for miRNA diagnostics.

The Analyst·2025

Related Experiment Video

Updated: Sep 12, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Bayesian doubly robust estimation of causal effects for clustered observational data.

Qi Zhou1, Haonan He1, Jie Zhao1

  • 1School of Economics and Management, Chang'an University, Xi'an, People's Republic of China.

Journal of Applied Statistics
|August 6, 2025
PubMed
Summary

Ignoring clustered data structure can bias causal effect estimates. This study introduces a Bayesian doubly robust estimator using random intercept BART to improve accuracy and robustness against model misspecification in clustered observational data.

Keywords:
Bayesian bootstrapBayesian doubly robust estimatorclustered observational datapropensity scorerandom intercept BART

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K
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

14.6K

Related Experiment Videos

Last Updated: Sep 12, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K
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

14.6K

Area of Science:

  • Causal Inference
  • Biostatistics
  • Health Services Research

Background:

  • Observational data frequently display clustered structures (e.g., patients within hospitals).
  • Ignoring this clustering can lead to biased and inaccurate estimations of exposure effects.
  • Complex confounder effects in clustered data pose significant modeling challenges.

Purpose of the Study:

  • To propose a novel Bayesian doubly robust estimator for causal effects in clustered data.
  • To enhance robustness against model misspecification using random intercept BART.
  • To improve the accuracy and coverage probability of interval estimations for causal effects.

Main Methods:

  • Developed a Bayesian doubly robust estimator incorporating random intercept BART.
  • Accounted for uncertainty in propensity score and potential outcome estimations.
  • Integrated individual- and cluster-level confounder distribution uncertainty.

Main Results:

  • The proposed Bayesian method demonstrated improved robustness and accuracy in simulations.
  • It outperformed frequentist doubly robust estimators with parametric and nonparametric multilevel models.
  • The approach effectively addressed challenges in modeling complex confounder effects in clustered data.

Conclusions:

  • The Bayesian doubly robust estimator with random intercept BART is a valuable tool for analyzing clustered observational data.
  • It provides more reliable causal effect estimates, particularly when model misspecification is a concern.
  • Applied to study the impact of limited food access on cardiovascular disease mortality in seniors.