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

Longitudinal Research02:20

Longitudinal Research

12.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.8K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

376
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...
376
Longitudinal Studies01:26

Longitudinal Studies

300
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
300
Group Design02:01

Group Design

9.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
9.9K
Crossover Experiments01:16

Crossover Experiments

4.2K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
4.2K
Data Collection by Experiments01:13

Data Collection by Experiments

26.3K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
26.3K

You might also read

Related Articles

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

Sort by
Same author

Longitudinal association of switching to dual cigarette and e-cigarette use vs. continued exclusive cigarette smoking with tobacco-specific nitrosamine and nicotine intake.

Addiction (Abingdon, England)·2026
Same author

CACE-MM: using mixed methods to strengthen causal inference in medicine and public health.

BMC medical research methodology·2026
Same author

Large language models for full-text methods assessment: a case study on mediation analysis.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

A <i>Cautionary Tale</i> on Integrating Studies with Disparate Outcome Measures for Causal Inference.

Advances in neural information processing systems·2026
Same author

Comparative effectiveness of antidepressants for depression using EHRs from two health systems.

BMC psychiatry·2026
Same author

The TARGET guideline for reporting observational studies of interventions.

Nature medicine·2026

Related Experiment Video

Updated: Nov 5, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.1K

A Trial Emulation Approach for Policy Evaluations with Group-level Longitudinal Data.

Eli Ben-Michael1, Avi Feller2,3, Elizabeth A Stuart4

  • 1From the Institute for Quantitative Social Science, Harvard University, Cambridge, MA.

Epidemiology (Cambridge, Mass.)
|May 18, 2021
PubMed
Summary

Governments worldwide used physical distancing policies, like stay-at-home orders, to curb COVID-19 spread. Policy trial emulation, a method for analyzing group-level data, helps estimate the effects of these public health interventions.

More Related Videos

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

1.5K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.1K

Related Experiment Videos

Last Updated: Nov 5, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.1K
The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
06:05

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

Published on: February 19, 2021

1.5K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.1K

Area of Science:

  • Epidemiology
  • Public Health Policy
  • Econometrics

Background:

  • Governments globally implemented physical distancing policies, including stay-at-home orders, to mitigate the novel coronavirus (COVID-19) pandemic.
  • Statistical and econometric methods, such as difference-in-differences, are frequently used to estimate policy effects, leveraging repeated measurements and timing variations.
  • While common in other fields, these methods are less frequently applied in epidemiology, despite epidemiologic researchers' experience with complex individual-level intervention studies.

Purpose of the Study:

  • To introduce and advocate for a rigorous study design approach, termed "policy trial emulation," for evaluating public health policies using longitudinal group-level data.
  • To highlight the importance of careful study design, including inclusion/exclusion criteria, covariate selection, exposure definition, and outcome measurement timing, analogous to target trial emulation for individual-level studies.
  • To demonstrate the application of policy trial emulation in analyzing the impact of state-level stay-at-home orders on coronavirus cases.

Main Methods:

  • Propose "policy trial emulation" as a framework for policy evaluation using longitudinal panel data, especially when intervention timing varies across jurisdictions.
  • Advocate for constructing separate target trials for each treatment cohort (jurisdictions implementing policies simultaneously) and subsequently aggregating the results.
  • Utilize stylized analysis of state-level stay-at-home orders and their association with total coronavirus cases.

Main Results:

  • Policy trial emulation provides a structured approach to analyzing the impact of group-level interventions like stay-at-home orders.
  • Estimates derived from panel methods, when supported by appropriate data, careful modeling, and diagnostics, can enhance understanding of policy impacts.
  • The application of policy trial emulation is particularly valuable in contexts with varied intervention timing across different regions.

Conclusions:

  • Policy trial emulation offers a robust methodology for evaluating the effectiveness of public health policies using longitudinal panel data.
  • This approach enhances the rigor of policy evaluations, particularly in complex scenarios with staggered implementation of interventions.
  • Careful application of panel methods within a policy trial emulation framework can contribute significantly to understanding the effects of various public health policies, despite inherent challenges.