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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

150
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,...
150
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

195
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...
195
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K
Cancer Survival Analysis01:21

Cancer Survival Analysis

355
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
355
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

134
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.
134
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

371
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
371

You might also read

Related Articles

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

Sort by
Same author

Personal and Family History of Cancer and Primary Lung Cancer Prevalence Among Never Smoking Disaggregated Asian American Women.

Cancers·2026
Same author

Cross-country comparison of perinatal outcomes between japanese mothers in the united states and japan: a cross-sectional study.

BMJ public health·2026
Same author

Trends in cardiovascular mortality among disaggregated Asian American subgroups: 2018-2022.

American journal of preventive cardiology·2026
Same author

Analysis of plasma extracellular vesicles in normal-weight and overweight type 2 diabetes mellitus using multimodal SERS and RNA-Seq.

Diabetes research and clinical practice·2026
Same author

MonotonicityTest: An R Package for Efficient Nonparametric Monotonicity Testing.

Observational studies·2026
Same author

Racial and geographic differences of opioid overdose deaths involving additional drugs of abuse: analysis of US mortality database.

British journal of anaesthesia·2026
Same journal

Hepatitis C Virus Cascade of Care in Florida Emergency Departments.

Medical care·2026
Same journal

Association of Neighborhood Socioeconomic Disadvantage and Uptake of Diabetes Prevention Interventions.

Medical care·2026
Same journal

Machine Learning for Evaluating the Heterogeneous Effects of Intensive In-Hospital Rehabilitation During the Postacute Phase After Hip Fracture Surgery on Activities of Daily Living.

Medical care·2026
Same journal

Hospital-Physician Integration and Differences in the Use of Orthopedic Care Across Race and Ethnicity.

Medical care·2026
Same journal

Temporal Misalignment and Selection Bias in "Burn Pit Smoke Exposure and Sleep Apnea in US Veterans.

Medical care·2026
Same journal

The Impact of an Oncology Hospital at Home Program on Health Care Costs.

Medical care·2026
See all related articles

Related Experiment Video

Updated: Jul 8, 2025

Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates
08:16

Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates

Published on: May 9, 2016

10.7K

Statistical Methods to Evaluate Surrogate Markers.

Layla Parast1, Lu Tian2, Tianxi Cai3,4

  • 1Department of Statistics and Data Science, The University of Texas at Austin, Austin, TX.

Medical Care
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

Evaluating surrogate markers is crucial for efficient clinical trials. This study shows a surrogate explained 18.2% of treatment effect for biopsy scores and 59.6% for diabetes incidence, offering tools for future research.

More Related Videos

Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
10:03

Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty

Published on: January 28, 2020

5.3K
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

18.7K

Related Experiment Videos

Last Updated: Jul 8, 2025

Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates
08:16

Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates

Published on: May 9, 2016

10.7K
Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
10:03

Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty

Published on: January 28, 2020

5.3K
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

18.7K

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Pharmacometrics

Background:

  • Surrogate markers can reduce clinical trial duration, cost, and patient burden.
  • Evaluating the quality of surrogate markers is essential for their reliable use.
  • Methods are needed to assess how well a surrogate marker captures the treatment effect on the primary outcome.

Purpose of the Study:

  • To describe and illustrate methods for evaluating surrogate markers.
  • To assess surrogate marker quality using the proportion of treatment effect (PTE) explained.
  • To apply these methods in settings with fully observed and time-to-event primary outcomes.

Main Methods:

  • Utilized two randomized trials: one with a biopsy score outcome (nonalcoholic fatty liver disease) and another with a time-to-event outcome (diabetes incidence).
  • Employed statistical methods to calculate the proportion of treatment effect explained by a surrogate marker.
  • Illustrated methods using the Rsurrogate package with provided R code.

Main Results:

  • For the biopsy score outcome, the surrogate marker explained 18.2% (95% CI: 0.121, 0.240) of the treatment effect.
  • For the time-to-event diabetes outcome, the surrogate marker explained 59.6% (95% CI: 0.404, 0.760) of the treatment effect.
  • These results quantify the performance of the surrogate markers in distinct clinical settings.

Conclusions:

  • The study provides practical tools and methods for evaluating surrogate markers.
  • These tools support researchers in assessing the validity of surrogate markers in clinical trials.
  • The findings highlight the varying performance of surrogate markers across different outcome types.