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Related Concept Videos

Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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...
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.
Accelerated Curing of Concrete01:25

Accelerated Curing of Concrete

Accelerating concrete curing is achieved by applying heat and additional moisture. This process accelerates the hydration of the cement, resulting in an earlier strength gain in the concrete. Steam curing is a method wherein the concrete products are either transported through a chamber on a conveyor belt or encased in plastic, allowing steam at atmospheric pressure to circulate freely around them. This process begins with a phase of moist curing that typically lasts between 3 to 5 hours, after...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
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: Jun 17, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

An accelerated failure time mixture cure model with masked event.

Jenny J Zhang1, Molin Wang

  • 1Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. jjzhang6@gmail.com

Biometrical Journal. Biometrische Zeitschrift
|December 24, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for time-to-event data with a cure rate, relaxing the proportional hazards assumption. The accelerated failure time model offers more intuitive results for breast cancer data analysis.

Related Experiment Videos

Last Updated: Jun 17, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Existing proportional hazards (PH) cure models may not apply when the PH assumption is violated.
  • Masked event data with a cure rate requires flexible statistical approaches.

Purpose of the Study:

  • To extend the Dahlberg and Wang PH cure model to situations where the PH assumption does not hold.
  • To propose an accelerated failure time (AFT) model for time-to-event data with a cure fraction and masked events.

Main Methods:

  • Utilizing an AFT model with an unspecified error distribution for the time to event.
  • Developing rank-based estimating equations for parameter estimation.
  • Employing a generalized expectation-maximization (EM) algorithm for parameter estimation.

Main Results:

  • The proposed AFT model provides more intuitive results for the treatment arm of a breast cancer dataset compared to the PH model.
  • The method is evaluated through a simulation study to assess its performance.

Conclusions:

  • The developed AFT cure model offers a valuable alternative when the proportional hazards assumption is untenable.
  • This approach enhances the analysis of time-to-event data in the presence of cure rates and masked events.