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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

207
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
207
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

184
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
184
Relative Risk01:12

Relative Risk

587
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
587
Randomized Experiments01:13

Randomized Experiments

8.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.3K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

355
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...
355
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

324
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
324

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

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Oct 25, 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

Estimating adjusted risk differences by multiply-imputing missing control binary potential outcomes following

Peter C Austin1,2,3, Donald B Rubin4,5,6, Neal Thomas7

  • 1ICES, Toronto, Ontario, Canada.

Statistics in Medicine
|August 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for treatment-control studies with binary outcomes, improving bias reduction and risk difference estimation. The new approach enhances efficiency by imputing potential outcomes, offering more reliable results for clinical research.

Keywords:
Monte Carlo simulationsmultiple imputationpropensity scorepropensity score matching

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 25, 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:

  • Biostatistics
  • Epidemiology
  • Clinical Research Methodology

Background:

  • Estimating treatment effects with binary outcomes is crucial in clinical research.
  • Propensity-score matching is a common method but can be limited in bias reduction.
  • Clinically meaningful effect measures like risk difference are essential for interpreting study findings.

Purpose of the Study:

  • To introduce a novel method combining propensity-score matching with regression adjustment for binary outcomes.
  • To enable the estimation of risk differences using multiply imputed potential outcomes.
  • To evaluate the impact of imputed potential outcomes on bias and efficiency in treatment-control studies.

Main Methods:

  • Developed a method involving multiply imputing potential outcomes under control for matched treated subjects.
  • Employed Monte Carlo simulations to assess the effect of the number of imputed outcomes.
  • Applied the method to estimate the effect of beta-blockers on 1-year mortality in heart failure patients.

Main Results:

  • Imputing potential outcomes significantly reduced bias compared to conventional nearest neighbor matching alone.
  • Increasing the number of imputed potential outcomes improved the efficiency of risk difference estimation.
  • Diminishing returns in efficiency were observed after imputing 20 potential outcomes per subject.

Conclusions:

  • The novel method offers substantial improvements in bias reduction and estimation efficiency for binary outcomes.
  • Multiple imputation of potential outcomes provides more accurate and reliable risk difference estimates.
  • This approach enhances the validity of causal inference in observational studies, as demonstrated in the heart failure example.