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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

729
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...
729
What are Estimates?01:06

What are Estimates?

6.6K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
6.6K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.4K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.4K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.4K
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...
4.4K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.0K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.0K
Stratified Sampling Method01:16

Stratified Sampling Method

13.5K
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...
13.5K

You might also read

Related Articles

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

Sort by
Same author

On variance estimation of the inverse probability-of-treatment weighting estimator: A tutorial for different types of propensity score weights.

Statistics in medicine·2024
Same author

Cox regression with linked data.

Statistics in medicine·2023
Same author

OMT-France publishes the first French physiotherapy guide for triage of patients with neuromusculoskeletal conditions - a step toward direct access in French speaking countries.

The Journal of manual & manipulative therapy·2022
Same author

Inverse probability weighting to handle attrition in cohort studies: some guidance and a call for caution.

BMC medical research methodology·2022
Same author

Influence of exposure assessment methods on associations between long-term exposure to outdoor fine particulate matter and risk of cancer in the French cohort Gazel.

The Science of the total environment·2022
Same author

Contribution of Long-Term Exposure to Outdoor Black Carbon to the Carcinogenicity of Air Pollution: Evidence regarding Risk of Cancer in the Gazel Cohort.

Environmental health perspectives·2021
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Oct 26, 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

14.8K

Variance estimators for weighted and stratified linear dose-response function estimators using generalized propensity

Valérie Garès1, Guillaume Chauvet2, David Hajage3

  • 1Univ Rennes, INSA, CNRS, IRMAR - UMR 6625, F-35000, Rennes, France.

Biometrical Journal. Biometrische Zeitschrift
|July 30, 2021
PubMed
Summary
This summary is machine-generated.

Generalized propensity score (GPS) methods for continuous exposures showed high variability with weighted estimators. Stratified estimators with bootstrap variance estimation proved more stable and accurate for dose-response function analysis in observational studies.

Keywords:
generalized propensity scoreobservational studyquantitative exposurevariance estimator

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

2.3K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K

Related Experiment Videos

Last Updated: Oct 26, 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

14.8K
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

2.3K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Observational Studies

Background:

  • Propensity score methods are crucial for estimating marginal treatment effects in observational research.
  • The generalized propensity score (GPS) extends these methods to quantitative or continuous exposures.
  • Accurate variance estimation is essential for reliable treatment effect evaluation.

Purpose of the Study:

  • To propose and evaluate variance estimators for treatment effect estimators on continuous outcomes using the generalized propensity score (GPS).
  • To compare the performance of weighted and stratified estimators for dose-response functions (DRFs) with different variance estimators.
  • To assess the impact of covariate variation on the accuracy of variance estimation.

Main Methods:

  • Monte Carlo simulations were used to evaluate proposed variance estimators.
  • Dose-response functions (DRFs) were estimated using inverse probability weighting (IPW) with stabilized weights and stratification based on the GPS.
  • Variance estimators evaluated included bootstrap, closed-form, sandwich, pooled linearized, and pooled model-based approaches.

Main Results:

  • Weighted estimators of the DRF exhibited high variability, with tested variance estimators failing to capture this adequately, leading to below-nominal coverage rates.
  • Stratified estimators demonstrated greater stability.
  • Bootstrap variance estimators for the stratified approach accurately captured empirical variability and provided correct coverage rates, outperforming pooled estimators which tended to overestimate variance.

Conclusions:

  • Stratified estimation with bootstrap variance estimation is recommended for analyzing dose-response functions with continuous exposures using generalized propensity scores (GPS).
  • The proposed bootstrap variance estimator effectively accounts for the GPS estimation step, ensuring reliable coverage.
  • The findings have implications for observational studies assessing continuous exposure-outcome relationships, such as the impact of maternal BMI on birth weight.