Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

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...

Bootstrapping

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

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...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

You might also read

Related Articles

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

Sort by

Same author

Bayesian shrinkage priors for penalized synthetic control estimators in the presence of spillovers.

Biometrics·2026

Same author

Beyond Missingness: Systematizing Methods for Comprehensive Data Fitness Assessment in Clinical Research.

Journal of medical Internet research·2026

Same author

Proactively Delivered Digital Mental Health Support for Health Care Workers: Usability and Acceptability Evaluation.

JMIR formative research·2025

Same author

Analysis of categorical data from biological experiments with logistic regression and CMH tests.

PloS one·2025

Same author

Bidirectional Associations Between Seborrheic Dermatitis and Epithelial Barrier Diseases: A Retrospective Cohort Study.

Allergy·2025

Same author

Nationwide Insights on Immunotherapy in a Low- and Middle-Income Country: Armenia's Struggle for Equitable Cancer Care in an Out-of-Pocket System.

JCO global oncology·2025

Same journal

Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis.

The international journal of biostatistics·2026

Same journal

Predicting birth weight by multivariate functional principal component regressions.

The international journal of biostatistics·2026

Same journal

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026

Same journal

Handling the uncertainty issue of missingness via a mixture-structure-based method.

The international journal of biostatistics·2026

Same journal

Statistical method for pooling categorical biomarker data from multi-center matched/nested case-control studies.

The international journal of biostatistics·2026

Same journal

Prognostic score methods for the estimation of the average causal effect.

The international journal of biostatistics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 15, 2025

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

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

Published on: July 3, 2020

Hierarchical Bayesian bootstrap for heterogeneous treatment effect estimation.

Arman Oganisian¹, Nandita Mitra², Jason A Roy³

¹Department of Biostatistics, Brown University, Providence, RI, USA.

The International Journal of Biostatistics

|December 30, 2022

Summary

This summary is machine-generated.

This study introduces a hierarchical Bayesian bootstrap (HBB) method to improve heterogeneous average treatment effect (HTE) estimation in sparse data. The HBB method enhances causal inference by enabling information sharing across strata.

Keywords:

Bayesian nonparametrics Dirichlet process bootstrap causal inference heterogenous treatment effects

More Related Videos

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

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

Published on: January 8, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Aug 15, 2025

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

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

Published on: July 3, 2020

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

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

Published on: January 8, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Causal Inference
Statistical Modeling
Biostatistics

Background:

Estimating heterogeneous average treatment effects (HTE) is crucial for understanding treatment variations across subgroups.
Standard HTE estimation methods struggle with sparsely populated strata, leading to unreliable confounder distribution estimates.
Independent estimation of stratum-specific confounder distributions limits statistical power and introduces bias in low-data strata.

Purpose of the Study:

To develop a novel nonparametric hierarchical Bayesian bootstrap (HBB) prior for improved HTE estimation.
To address the challenge of sparse confounder data in stratum-specific analyses.
To enable principled borrowing of confounder information across strata.

Main Methods:

Developed a nonparametric hierarchical Bayesian bootstrap (HBB) prior.
Applied HBB to partially pool stratum-specific confounder distributions.
Evaluated HBB for HTE estimation, focusing on efficiency gains and robustness to sparsity.

Main Results:

The HBB approach demonstrated efficiency gains over standard marginalization techniques.
Partial pooling of confounder information across strata improved estimation in sparse settings.
The method avoided strong parametric assumptions about confounder distributions.

Conclusions:

The proposed HBB prior offers a robust and efficient method for HTE estimation, particularly in the presence of sparse data.
This approach enhances causal inference by leveraging information across strata.
The methodology was successfully applied to estimate adverse event risks in cancer chemoradiotherapy.