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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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,...

Hazard Ratio

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...

Relative Risk

Relative Risk

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...

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...

Odds Ratio

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

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...

You might also read

Related Articles

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

Sort by

Same author

Patterns of Antibiotic Utilization and Bacterial Susceptibility of Prenatal Urinary Tract Infections.

Journal of women's health (2002)·2026

Same author

Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.

The annals of applied statistics·2026

Same author

Predicting and differentiating accidental and self-harm drug poisonings using health records data.

PLOS mental health·2026

Same author

Interrupted Time Series Methods for Nonrandom Sampling Study Designs With Known Sampling Weights.

Statistics in medicine·2026

Same author

Preventing Severe Hypoglycemia in Type 2 Diabetes: Randomized Controlled Trial of Proactive Care With Versus Without Psychoeducation.

Journal of general internal medicine·2026

Same author

A pilot randomized controlled trial of an outreach intervention to improve depression treatment initiation among racial and ethnic groups.

General hospital psychiatry·2026

Same journal

The Association Between Sepsis Coding and Payment to U.S. Hospitals.

Health services research·2026

Same journal

Stagnation in Achieving Recommended Methadone Doses in Opioid Use Disorder Treatment.

Health services research·2026

Same journal

Promoting Transplant Access Through Dialysis Facility Performance Metrics: A Double-Edged Sword.

Health services research·2026

Same journal

Understanding Medicaid Estate Recovery: The Experience of North Carolina and Policy Implications for Future Reforms.

Health services research·2026

Same journal

Racial Disparities and Personal Responsibility Incentives in Medicaid.

Health services research·2026

Same journal

Under-Documentation of Z-Codes in Hospitalizations of Homeless Shelter Users in New York City.

Health services research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 14, 2025

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

Evaluating Clinical Implementation of Risk Prediction Based Interventions Using Difference-In-Differences.

Maricela Cruz^1,2, Susan M Shortreed^1,2, Gregory E Simon¹

¹Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.

Health Services Research

|July 21, 2025

Summary

This summary is machine-generated.

The traditional Difference-in-Differences (DID) model is suitable for evaluating risk-stratified interventions on an absolute difference scale. A risk score adjusted model is appropriate for all average treatment effect on the treated (ATT) estimands.

Keywords:

binary outcomes difference‐in‐differences prediction modeling preventing missed healthcare visits pre‐post designs quasi‐experimental methods

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: Sep 14, 2025

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

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Health Services Research
Biostatistics
Epidemiology

Background:

Evaluating risk-stratified interventions requires robust statistical methods.
Difference-in-Differences (DID) is a common quasi-experimental approach.
Assessing interventions targeting specific at-risk groups presents unique challenges for standard DID models.

Purpose of the Study:

To compare the performance of alternative Difference-in-Differences (DID) methods.
To evaluate the effectiveness of risk-stratified interventions on binary outcomes.
To identify the most appropriate DID model for analyzing interventions targeting at-risk populations.

Main Methods:

Simulations were conducted to compare three DID models: traditional, risk score adjusted, and risk score adjusted with interactions.
Recycled prediction estimators were used for common average treatment effect on the treated (ATT) estimands.
DID estimates were compared against randomized evaluation data from a real-world intervention at Kaiser Permanente Washington (KPWA).

Main Results:

The traditional DID and risk score adjusted models demonstrated lower bias, smaller standard errors, and better coverage probabilities in simulations.
The traditional DID model and the risk score adjusted model (with various links) provided estimates closest to the randomized evaluation results for the ATT on the absolute difference scale.
The risk score adjusted model with log or logit links was appropriate for all considered ATT estimands.

Conclusions:

The traditional DID model is appropriate for estimating the average treatment effect on the treated (ATT) on the absolute difference scale for risk-stratified interventions.
The risk score adjusted model, particularly with log or logit links, is suitable for all ATT estimands when evaluating such interventions.
These findings offer guidance on selecting appropriate DID methodologies for complex intervention evaluations.