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

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...

Causality in Epidemiology

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...

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,...

Criteria for Causality: Bradford Hill Criteria - II

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:

Assumptions of Survival Analysis

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

Theory of Attribution I: Correspondent Inference Theory

Theory of Attribution I: Correspondent Inference Theory

Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...

You might also read

Related Articles

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

Sort by

Same author

Time-Scale Target Parameters and Two-Step Estimation in Longitudinal Trials for Progressive Diseases.

Statistics in medicine·2026

Same author

Efgartigimod in Sjögren's disease: a phase 2, randomised, placebo-controlled, parallel-group, double-blinded, proof-of-concept study (RHO).

Annals of the rheumatic diseases·2026

Same author

A geometrically intuitive framework for the synthesis method in non-inferiority trials.

Journal of biopharmaceutical statistics·2026

Same author

Evaluating Hemagglutination Inhibition Antibody Titers as a Correlate of Protection for Influenza: A Sensitivity Analysis Based on Information Theory and Causal Inference.

Journal of global infectious diseases·2025

Same author

Fit for purpose testing and independent GMP validation of the monocyte activation test.

Current research in toxicology·2025

Same author

Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference.

Lifetime data analysis·2024

Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026

Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026

Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026

Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026

Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026

Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 10, 2026

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

Assessing a surrogate predictive value: a causal inference approach.

Ariel Alonso¹, Wim Van der Elst², Paul Meyvisch³

¹I-BioStat, Katholieke Universiteit Leuven, Leuven, B-3000, Belgium.

Statistics in Medicine

|December 15, 2016

Summary

This summary is machine-generated.

This study introduces the surrogate predictive function (SPF) to evaluate surrogate endpoints in clinical trials. The SPF, combined with individual causal association, quantifies surrogate value, addressing identifiability issues with a novel two-step method.

Keywords:

R package Surrogate causal inference sensitivity analysis surrogate endpoint

More Related Videos

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

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Mar 10, 2026

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

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

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Biostatistics
Clinical Trial Methodology
Causal Inference

Background:

Evaluating surrogate endpoints is crucial for assessing treatment effects.
Existing methods focus on predicting causal treatment effects using surrogate measures.
The need for robust quantification of surrogate predictive value remains.

Purpose of the Study:

Introduce the surrogate predictive function (SPF) for evaluating surrogate endpoints.
Investigate the relationship between SPF and individual causal association.
Address identifiability challenges associated with potential outcomes and SPF.

Main Methods:

Utilized potential outcomes framework to define the surrogate predictive function (SPF).
Studied the relationship between SPF and individual causal association.
Developed a two-step procedure to address identifiability issues: geometric characterization of potential outcome distributions and Monte Carlo simulation of SPF behavior.
Illustrated the method with clinical trial data from schizophrenic patients.

Main Results:

The surrogate predictive function (SPF) offers a quantifiable measure of surrogate predictive value when combined with individual causal association.
Identifiability issues of potential outcomes and SPF were successfully tackled using the proposed two-step method.
The methodology was validated using real-world clinical trial data.

Conclusions:

The surrogate predictive function (SPF) provides a valuable tool for assessing surrogate endpoint performance.
The proposed method effectively addresses identifiability challenges in surrogate endpoint evaluation.
An R package 'Surrogate' is available for practical application of this validation exercise.