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

Bootstrapping01:24

Bootstrapping

667
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...
667
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

579
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...
579
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

276
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...
276
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

195
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,...
195
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

190
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
190
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

1.8K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Extending the Median Odds Ratio (MOR), the Interval Odds Ratio (IOR), and the Proportion of Opposed Odds Ratios (POOR) for Use With 3-Level Multilevel Logistic Regression Models.

Statistics in medicine·2026
Same author

Using Propensity Score Weighting With Clustered Data When the Treatment Is Applied at the Level of the Cluster and Outcomes Are Assessed at the Level of the Individual: The Observational Analog of Cluster Randomization Trials.

Statistics in medicine·2026
Same author

The Impact of Two Data-Generating Processes for Competing Risk Data on the Discrimination and Calibration of Two Types of Competing Risk Regression Models.

Statistics in medicine·2026
Same author

Patterns and Outcomes of Completeness of Revascularization in Patients With Diabetes and Non-ST-Segment-Elevation Myocardial Infarction in Ontario, Canada.

Circulation. Population health and outcomes·2026
Same author

Positive Airway Pressure Therapy Initiation and Continued Benzodiazepine Use Among Chronic Drug Users.

Journal of sleep research·2025
Same author

The impact of the number and the size of clusters on prediction performance of the stratified and the conditional shared gamma frailty Cox proportional hazards models.

medRxiv : the preprint server for health sciences·2025
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Sep 4, 2025

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

Bootstrap vs asymptotic variance estimation when using propensity score weighting with continuous and binary

Peter C Austin1,2,3

  • 1ICES, Toronto, Ontario, Canada.

Statistics in Medicine
|July 16, 2022
PubMed
Summary
This summary is machine-generated.

The bootstrap method offers more accurate standard error estimates than asymptotic methods for propensity score weighting with average treatment effect (ATE) or average treatment effect in the treated (ATT) weights, especially in smaller sample sizes. Matching and overlap weights performed well with both methods.

Keywords:
bootstrapinverse probability of treatment weightingpropensity scorevariance estimation

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
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

Related Experiment Videos

Last Updated: Sep 4, 2025

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
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
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

Area of Science:

  • Statistics
  • Epidemiology
  • Biostatistics

Background:

  • Propensity score weighting is crucial for causal inference in observational studies.
  • Accurate estimation of standard errors is essential for reliable statistical inference.

Purpose of the Study:

  • To compare the performance of bootstrap and asymptotic variance estimators for standard errors in propensity score weighting.
  • To evaluate these estimators across different weighting strategies, sample sizes, and treatment prevalences.

Main Methods:

  • Monte Carlo simulations were employed to assess estimator performance.
  • Four weighting methods were simulated: inverse probability of treatment weights (ATE), average treatment effect in the treated (ATT) weights, matching weights, and overlap weights.
  • Simulations varied sample sizes (250-10,000) and treatment prevalences (0.1-0.9).

Main Results:

  • Bootstrap provided more accurate standard error estimates than asymptotic methods for ATE and ATT weights when sample sizes were ≤1000.
  • Both bootstrap and asymptotic estimators yielded accurate standard errors with matching and overlap weights across all conditions.
  • Confidence interval coverage was suboptimal with bootstrap for ATE/ATT weights in small to moderate samples with extreme treatment prevalences.

Conclusions:

  • Bootstrap is recommended for ATE and ATT weighting in smaller sample sizes (≤1000) for improved standard error accuracy.
  • Matching and overlap weights offer robust standard error estimation with both methods.
  • Careful consideration of sample size and treatment prevalence is needed when using bootstrap with ATE/ATT weights to ensure adequate confidence interval coverage.