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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Distributions to Estimate Population Parameter01:26

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...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...

You might also read

Related Articles

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

Sort by
Same author

Evaluating Estimators in Partially Identified Models.

Epidemiology (Cambridge, Mass.)·2026
Same author

Comparative effectiveness of alternative times to opioid agonist treatment taper initiation on taper completion and all-cause mortality among people with opioid use disorder: A retrospective population-based target trial emulation study in British Columbia, Canada, 2010-2020.

Addiction (Abingdon, England)·2026
Same author

Prediction of exposure to chrysotile asbestos fibers among Quebec miners and millers based on impinger measurements.

Annals of work exposures and health·2026
Same author

Expected Value of Sample Information Calculations for Risk Prediction Model Development.

Statistics in medicine·2026
Same author

Five misconceptions about categorizing exposure variables measured with error in epidemiological research.

American journal of epidemiology·2026
Same author

Bayesian Sample Size Calculations for External Validation Studies of Risk Prediction Models.

Statistics in medicine·2026

Related Experiment Video

Updated: May 23, 2026

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

Double-robust estimators: slightly more Bayesian than meets the eye?

Paul Gustafson1

  • 1University of British Columbia, BC, Canada.

The International Journal of Biostatistics
|April 14, 2012
PubMed
Summary
This summary is machine-generated.

This study compares double-robust and Bayesian methods for estimating causal effects with discrete variables. Both methods offer robust statistical estimation, with Bayesian model averaging providing an alternative approach.

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

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Related Experiment Videos

Last Updated: May 23, 2026

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

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

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

  • Statistics
  • Epidemiology
  • Causal Inference

Background:

  • Parametric modeling of outcome and exposure is common in statistical analysis.
  • Double-robust estimators offer consistency if at least one model is correct.
  • Existing methods provide a compromise between parametric and nonparametric models.

Purpose of the Study:

  • To compare a double-robust estimator with a Bayesian model averaging approach.
  • To explore an alternative compromise strategy for statistical modeling.
  • To evaluate the performance of these methods in a simple discrete variable setting.

Main Methods:

  • Utilized parametric modeling for outcome and exposure given confounders.
  • Investigated a double-robust estimator's properties.
  • Explored Bayesian model averaging as an alternative estimation strategy.

Main Results:

  • Double-robust estimators are consistent if either the outcome or exposure model is correctly specified.
  • Bayesian model averaging presents an alternative compromise between models.
  • The study prompts a direct comparison between these two robust estimation techniques.

Conclusions:

  • Both double-robust and Bayesian methods offer robust estimation strategies.
  • Bayesian model averaging provides a novel alternative to traditional double-robust estimators.
  • Further comparisons are needed to fully understand the implications of each method.