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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

157
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
157
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

226
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...
226
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

157
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.
157
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

484
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
484
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

435
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
435

You might also read

Related Articles

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

Sort by
Same author

Bayesian phylodynamic inference of population dynamics with dormancy.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Estimand-based inference in the presence of long-term survivors.

Statistical methods in medical research·2025
Same author

Bayesian phylodynamic inference of population dynamics with dormancy.

bioRxiv : the preprint server for biology·2025
Same author

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology.

bioRxiv : the preprint server for biology·2024
Same author

Fragility indices for only sufficiently likely modifications.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

Predicting death and lost to follow-up among adults initiating antiretroviral therapy in resource-limited settings: Derivation and external validation of a risk score in Haiti.

PloS one·2018
Same journal

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same journal

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same journal

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Pharmaceutical statistics·2026
Same journal

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.

Pharmaceutical statistics·2026
Same journal

CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose, Multiple-Outcome Randomized Trials.

Pharmaceutical statistics·2026
Same journal

Will the Pharmaceutical Industry Need Statisticians in an AI World?

Pharmaceutical statistics·2026
See all related articles

Related Experiment Video

Updated: Jul 24, 2025

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

Two-sample inference procedures under nonproportional hazards.

Yi-Cheng Tai1, Weijing Wang1, Martin T Wells2

  • 1Institute of Statistics, National Yang Ming Chiao Tung University, Hsin-Chu City, Taiwan, ROC.

Pharmaceutical Statistics
|July 10, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a new, model-free method for comparing two groups over time, even with nonproportional hazards. The approach offers clinically meaningful measures and robust inference for survival data analysis.

Keywords:
IPCWKendall's tauMaxCombocrossing survival functionsdelayed treatment effectinterpretable estimandnonproportional hazardsrestricted mean survival timesensitivity analysis

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

10.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Related Experiment Videos

Last Updated: Jul 24, 2025

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
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

10.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trials

Background:

  • Assessing relative group performance over time is crucial in clinical research.
  • Traditional methods often assume proportional hazards, which may not hold true in many real-world scenarios, particularly in oncology.
  • Nonproportional hazards can lead to biased results and inaccurate conclusions when using standard statistical techniques.

Purpose of the Study:

  • To introduce a novel, model-free two-sample inference procedure for comparing group performance over time.
  • To develop clinically meaningful and interpretable tau-based measures that summarize treatment effects, especially when hazards are nonproportional.
  • To provide a robust statistical framework for hypothesis testing and confidence interval construction in survival data analysis.

Main Methods:

  • Developed a model-free inference procedure that does not assume proportional hazards.
  • Introduced a diagnostic tau plot to identify changes in hazard timing.
  • Utilized a U-statistic with martingale structure for formal inference, ensuring robustness to censoring distributions.
  • Proposed tau-based measures as interpretable estimands for treatment effects.

Main Results:

  • The proposed method is suitable for scenarios with nonproportional hazards.
  • Tau-based measures offer clinically meaningful interpretations of treatment effects over time.
  • The procedure is robust with respect to the censoring distribution and applicable for sensitivity analysis with missing data.
  • Simulations demonstrated comparable or superior performance to existing statistics like restricted mean survival time and log-rank tests.

Conclusions:

  • The new procedure provides a flexible and robust approach for comparing groups in survival analysis, particularly when proportional hazards do not hold.
  • The tau-based measures enhance the interpretability of treatment effects in time-to-event data.
  • This method offers a valuable tool for analyzing oncology clinical trial data and other research areas with complex hazard dynamics.