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Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

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 interest.
Assumptions of Survival Analysis01:15

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.
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...

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Related Experiment Video

Updated: Jul 15, 2026

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

Commensurate prior models with random effects for interval-censored data to accommodate historical controls.

Xi Fang1, Brent Logan2, Anjishnu Banerjee2

  • 1Yale School of Public Health, Department of Biostatistics, Connecticut, U.S.A.

Communications in Statistics: Simulation and Computation
|July 14, 2026
PubMed
Summary

Borrowing information from historical controls can improve rare disease clinical trials. New commensurate prior models handle interval-censored survival data, reducing bias from assessment timing differences.

Keywords:
Borrowing from historical dataCommensurate priorInterval-censored dataMatching

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An R-Based Landscape Validation of a Competing Risk Model
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Last Updated: Jul 15, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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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

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Historical controls can enhance statistical power and reduce sample sizes in rare disease research.
  • Existing methods primarily address right-censored survival data, overlooking interval-censored events common in clinical practice.
  • Differential assessment timing between historical and clinical data can introduce bias.

Purpose of the Study:

  • To propose novel commensurate prior models with random effects for matched interval-censored survival data.
  • To address bias arising from differential assessment timing in historical control data.
  • To effectively borrow information based on data comparability.

Main Methods:

  • Development of commensurate prior models with random effects for interval-censored survival data.
  • Utilizing matched data structures to account for comparability.
  • Simulation studies to evaluate Type I error control under varying exchangeability assumptions.

Main Results:

  • The proposed models effectively control Type I error in simulations, regardless of data exchangeability.
  • Demonstrated ability to borrow information based on the degree of comparability between datasets.
  • Successful application to real-world clinical trial data (BMT CTN 1101 and 0901).

Conclusions:

  • Commensurate prior models offer a robust approach for analyzing interval-censored survival data with historical controls.
  • The methods mitigate bias caused by differential assessment timing.
  • These models enhance the utility of historical data in rare disease clinical trials.