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

Randomized Experiments01:13

Randomized Experiments

6.9K
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...
6.9K
Group Design02:01

Group Design

8.9K
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...
8.9K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

217
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
217
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

186
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...
186
What is an Experiment?01:12

What is an Experiment?

11.6K
An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
11.6K

You might also read

Related Articles

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

Sort by
Same author

Bleeding Risk With Apixaban Versus Rivaroxaban: A Reference Trial Emulation Predicting the Results of COBRRA-VTE and COBRRA-AF Using US Health Care Claims.

Circulation. Population health and outcomes·2026
Same author

Systematic Evaluation of Data and Trial Fitness for Oncology Trial Emulation: Empirical Findings from the CARE Initiative.

Clinical pharmacology and therapeutics·2026
Same author

A Plasmode Simulation-Based Bias Analysis for Residual Confounding by Unmeasured Variables Leveraging Information-Rich Subsets.

Pharmacoepidemiology and drug safety·2026
Same author

Safety of IL-17A Inhibitors in Patients With Moderate to Severe Psoriasis in a US Claims Data-Based Cohort Study.

Journal of psoriasis and psoriatic arthritis·2026
Same author

Adaptive Multi-Wave Sampling for Efficient Chart Validation.

Clinical epidemiology·2026
Same author

Characterization and comparison of structured and unstructured electronic health record data mapped to MedDRA for post-marketing surveillance.

JAMIA open·2026

Related Experiment Video

Updated: Jul 3, 2025

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

517

Design differences and variation in results between randomised trials and non-randomised emulations: meta-analysis of

Rachel Heyard1, Leonhard Held1, Sebastian Schneeweiss2

  • 1Center for Reproducible Science, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.

BMJ Medicine
|February 13, 2024
PubMed
Summary

Design emulation differences significantly explain variations between randomized controlled trials (RCTs) and real-world evidence (RWE) studies. Addressing these emulation gaps in RWE studies improves consistency with RCT findings.

Keywords:
Clinical trialResearch designStatistics

More Related Videos

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

14.5K
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

6.9K

Related Experiment Videos

Last Updated: Jul 3, 2025

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

517
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

14.5K
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

6.9K

Area of Science:

  • Health economics and outcomes research
  • Epidemiological study design and methodology
  • Comparative effectiveness research

Background:

  • Randomized controlled trials (RCTs) are the gold standard for evaluating treatment efficacy, but real-world evidence (RWE) studies offer insights into treatment effects in routine clinical practice.
  • Discrepancies often exist between RCT and RWE study results, potentially due to differences in study design, patient populations, and data sources.
  • The RCT-DUPLICATE initiative aimed to understand these variations by emulating RCTs using RWE data.

Purpose of the Study:

  • To investigate the relationship between design emulation and population differences and the variation in results between RCTs and non-randomized RWE studies.
  • To identify specific emulation differences that contribute to heterogeneity in effect estimates between paired RCT-RWE studies.
  • To quantify the impact of these emulation differences on the consistency of findings from RCTs and RWE studies.

Main Methods:

  • A meta-analysis was conducted on data from the RCT-DUPLICATE initiative.
  • The study included 29 pairs of RCT-RWE studies where the primary analysis yielded a hazard ratio.
  • Three large real-world data sources were used to emulate 32 RCTs: Optum Clinformatics Data Mart, IBM MarketScan, and Medicare data.

Main Results:

  • Most heterogeneity in effect estimates between RCT-RWE study pairs was explained by three key emulation differences: treatment initiation in hospital, discontinuation of baseline treatments at randomization, and delayed drug effects.
  • Incorporating these three emulation differences into a meta-regression model substantially reduced heterogeneity from 1.9 to nearly 1.
  • These findings suggest that differences in how RWE studies emulate RCT designs significantly contribute to observed variations in study outcomes.

Conclusions:

  • A significant portion of the variation in results between RCTs and RWE studies can be attributed to differences in design emulation.
  • Careful consideration and adjustment for emulation differences are crucial for improving the reliability and comparability of RWE studies with RCT findings.
  • This analysis underscores the importance of robust emulation strategies in real-world evidence research to ensure accurate comparative effectiveness assessments.