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

Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
Randomized Experiments01:13

Randomized Experiments

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...
Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...

You might also read

Related Articles

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

Sort by
Same author

Associations of urinary incontinence, absorbent product use, and confidence gained from absorbent product use with perceived ageism in later adulthood.

The Gerontologist·2026
Same author

Mortality Following Atypical Femoral Fractures in Men.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2026
Same author

A framework for causal estimand selection under positivity violations.

Biometrics·2026
Same author

Dual life stage effects of spray adjuvants combined with phagostimulants on Drosophila suzukii (Diptera: Drosophilidae) egg viability and adult survival in blueberries.

Journal of economic entomology·2026
Same author

The Effect of COVID-19 on Incident Diabetes in Pediatric Patients: Findings From the National COVID-19 Cohort Collaborative (N3C).

Pediatric diabetes·2025
Same author

Insights Into Rural Youth Physical Activity: The Role of Micro- and Macro-Level Environments.

Family & community health·2025
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026
Same journal

Rejoinder to the discussion on "INTACT: A method for integration of longitudinal physical activity data from multiple sources".

Biometrics·2026
See all related articles
  1. Home
  3. Causally-interpretable Random-effects Meta-analysis.
  1. Home
  3. Causally-interpretable Random-effects Meta-analysis.

Related Experiment Videos

Causally-interpretable random-effects meta-analysis.

Justin M Clark1, Kollin W Rott2, James S Hodges1

  • 1Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55414, United States.

Biometrics
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Causally-interpretable meta-analysis methods transport treatment effects to target populations. New frameworks address between-study heterogeneity for more relevant causal estimates in policy and clinical settings.

Keywords:
causal inferenceevidence synthesismeta-analysistransportability

Related Experiment Videos

Area of Science:

  • Biostatistics
  • Epidemiology
  • Causal Inference

Background:

  • Causally-interpretable meta-analysis aims to transport treatment effects from randomized trials to specific populations.
  • Heterogeneity between studies, unrelated to treatment effect modifiers, complicates synthesizing estimates.
  • Existing methods may struggle with this type of between-study variation.

Purpose of the Study:

  • To propose a conceptual framework and estimation procedures to account for between-study heterogeneity in causal meta-analysis.
  • To develop inferential techniques for capturing excess variability in causal estimates.
  • To clarify the types of treatment effects suitable for generalizability and transportability techniques.

Main Methods:

  • Development of a novel conceptual framework for causal meta-analysis.
  • Introduction of new estimation procedures to handle unexplained heterogeneity.
  • Formulation of inferential techniques to manage excess variability in causal estimates.

    • Main Results:

    • The proposed framework accounts for between-study heterogeneity not explained by known modifiers.
    • New methods capture excess variability, improving the reliability of causal estimates.
    • Clarification of the scope of treatment effects addressable by generalizability and transportability.

    Conclusions:

    • The developed framework enhances the interpretability and relevance of meta-analysis findings for specific populations.
    • The methods provide a robust approach to synthesizing evidence despite complex heterogeneity.
    • This work advances the application of causal inference in meta-analytic research.