Jove
Visualize
Contact Us

Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

261
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...
261
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

202
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.
202
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

284
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,...
284
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

656
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...
656
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

414
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
414
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

309
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...
309

You might also read

Related Articles

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

Sort by
Same author

Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data.

Scientific reports·2023
Same author

Quantifying conditional probability tables in Bayesian networks: Bayesian regression for scenario-based encoding of elicited expert assessments on feral pig habitat.

Journal of applied statistics·2022
Same author

Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection.

Computational intelligence and neuroscience·2022
Same author

Brand Awareness via Online Media: An Evidence Using Instagram Medium with Statistical Analysis.

Computational intelligence and neuroscience·2022
Same author

Inferences for Exponentiated Gamma Constant-Stress Partially Accelerated Life Test Model Based on Generalized Type-I Hybrid Censored Data.

Computational intelligence and neuroscience·2022
Same author

The Role of Twitter Medium in Business with Regression Analysis and Statistical Modelling.

Computational intelligence and neuroscience·2021
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 Experiment Video

Updated: Sep 24, 2025

Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine
05:56

Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine

Published on: October 27, 2023

1.2K

Parameter Estimation in Step Stress Partially Accelerated Life Testing under Different Types of Censored Data.

Mustafa Kamal1, Sabir Ali Siddiqui2, Ahmadur Rahman3

  • 1Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Dammam 32256, Saudi Arabia.

Computational Intelligence and Neuroscience
|May 9, 2022
PubMed
Summary

Accelerated life tests (ALTs) shorten product testing. This study compares two censoring schemes for step stress partially accelerated life tests (SSPALT), finding type-I PHCS offers superior parameter estimation and confidence intervals compared to type-II PCS.

More Related Videos

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

10.8K
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.1K

Related Experiment Videos

Last Updated: Sep 24, 2025

Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine
05:56

Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine

Published on: October 27, 2023

1.2K
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

10.8K
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.1K

Area of Science:

  • Reliability Engineering
  • Statistical Inference
  • Accelerated Life Testing

Background:

  • High-reliability product life testing requires extensive durations, necessitating methods like accelerated life tests (ALTs) to expedite evaluation.
  • ALTs employ harsher conditions, reducing product life expectancy and often resulting in censored data due to practical constraints.
  • Censored data, where complete failure times are unknown, presents challenges in accurately estimating product reliability.

Purpose of the Study:

  • To investigate and compare the effectiveness of two censoring schemes—type-I progressive hybrid censoring (PHCS) and type-II progressive censorship (PCS)—within a step stress partially accelerated life test (SSPALT) framework.
  • To estimate unknown parameters using maximum likelihood estimation (MLE) and construct approximate confidence intervals (ACIs) for reliability assessment.
  • To evaluate the performance of estimators based on root mean squared error (RMSE), relative absolute bias (RAB), interval length, and coverage probability (CP).

Main Methods:

  • The study utilizes the step stress partially accelerated life test (SSPALT) model.
  • Failure times are modeled using the NH distribution, with the tampered random variable (TRV) model accounting for stress level changes.
  • Maximum Likelihood Estimation (MLE) is employed for parameter estimation, and asymptotic theory is used for constructing approximate confidence intervals (ACIs).

Main Results:

  • Simulation studies indicate that the type-I progressive hybrid censoring scheme (type-I PHCS) generally outperforms the type-II progressive censorship scheme (type-II PCS).
  • Type-I PHCS demonstrates superior performance in terms of lower RMSEs and RABs for point estimates.
  • Approximate confidence intervals derived from type-I PHCS exhibit better characteristics, including shorter lengths and higher coverage probabilities (CPs).

Conclusions:

  • The type-I progressive hybrid censoring scheme is recommended for step stress partially accelerated life tests due to its superior estimation accuracy and interval precision.
  • The findings provide valuable insights for optimizing life testing strategies for high-reliability products under accelerated conditions.
  • A practical application using insulating fluid failure times validates the proposed methodology.