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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

621
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...
621
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

639
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
639
Bioequivalence of Drugs: Drugs with Multiple Indications01:09

Bioequivalence of Drugs: Drugs with Multiple Indications

168
The concept of therapeutic equivalence (TE) in drugs with multiple indications is complex. A generic drug may be therapeutically equivalent to a brand-name product for one specific indication, but this doesn't necessarily mean it's equivalent for all other indications. Evidence of TE in one patient group and bioequivalence shown in healthy volunteers can support—but not confirm—TE for other indications. However, definitive proof requires individual clinical studies for each...
168
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

488
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,...
488
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

447
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.
447
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

233
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
233

You might also read

Related Articles

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

Sort by
Same author

Adjusting for non-compliance and contamination with a more plausible assumption.

Journal of medical screening·2026
Same author

The efficient stored specimen design for evaluating multiple screening technologies: Application to multicancer detection tests.

Journal of medical screening·2025
Same author

The Role of the Extracellular Matrix in Cancer Prevention.

Cancers·2025
Same author

Quantifying Overdiagnosis for Multicancer Detection Tests: A Novel Method.

Statistics in medicine·2024
Same author

Calibrating machine learning approaches for probability estimation: A short expansion.

Statistics in medicine·2024
Same author

Prediagnostic evaluation of multicancer detection tests: design and analysis considerations.

Journal of the National Cancer Institute·2024

Related Experiment Video

Updated: Feb 18, 2026

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

15.4K

Five criteria for using a surrogate endpoint to predict treatment effect based on data from multiple previous trials.

Stuart G Baker1

  • 1Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Dr, Room 5E606, MSC 9789, Bethesda, MD, 20892-9789, USA.

Statistics in Medicine
|November 23, 2017
PubMed
Summary

This study proposes 5 criteria for using surrogate endpoints in clinical trials to predict treatment effects. These criteria balance statistical and clinical factors to ensure reliable treatment recommendations.

Keywords:
Prentice criterionmeta-analysisrandomized trialsurrogate endpoint

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Related Experiment Videos

Last Updated: Feb 18, 2026

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

15.4K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Evidence-Based Medicine

Background:

  • Surrogate endpoints in clinical trials can accelerate treatment evaluation but risk misleading conclusions.
  • Rigorous criteria are essential for validating surrogate endpoints before their use in new trials.

Purpose of the Study:

  • To formulate 5 criteria for determining the suitability of a surrogate endpoint in a new randomized clinical trial.
  • To enable prediction of treatment effects on the true clinical endpoint using surrogate endpoint data from previous trials.

Main Methods:

  • Development of a meta-analytic framework for multiple trials with shared surrogate and true endpoints.
  • Formulation of 2 statistical criteria (sample size multiplier, prediction separation score) using a linear random effects model.
  • Inclusion of 3 clinical/biological criteria: treatment mechanism similarity, secondary treatment similarity, and risk of adverse events.

Main Results:

  • The proposed 5 criteria provide a comprehensive framework for surrogate endpoint validation.
  • Statistical criteria assess the predictive power and sample size efficiency.
  • Clinical criteria ensure biological plausibility and patient safety.

Conclusions:

  • The 5 criteria establish a high standard for using surrogate endpoints to make definitive treatment recommendations.
  • This framework aims to enhance the reliability and validity of clinical trial outcomes.
  • Adoption of these criteria can mitigate the risks associated with misleading surrogate endpoint conclusions.