Related Experiment Video
Updated: Jun 17, 2025
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.0K
AFFECT: an R package for accelerated functional failure time model with error-contaminated survival times and
Li-Pang Chen1, Hsiao-Ting Huang2
1Department of Statistics, National Chengchi University, Taipei, Taiwan, ROC. lchen723@nccu.edu.tw.
BMC Bioinformatics
|August 13, 2024
Summary
This study introduces the AFFECT R package to address measurement errors in survival time data for cancer research. It identifies informative genes and nonlinear relationships, improving survival analysis accuracy.
Area of Science:
- Biostatistics
- Bioinformatics
- Genomics
Background:
- Survival analysis is crucial for time-to-event data, particularly in medical studies analyzing cancer survival times using gene expressions.
- Challenges include non-informative genes, nonlinear relationships, and ubiquitous measurement errors in survival time and censoring status.
- Ignoring these errors can lead to biased estimators and incorrect conclusions in medical research.
Purpose of the Study:
- To develop a robust R package for survival analysis that accounts for measurement errors in survival times.
- To identify informative gene expressions and their nonlinear relationships with survival time in high-dimensional data.
- To provide an efficient computational tool for reliable survival data estimation.
Main Methods:
- Development of the Accelerated Functional Failure time model with Error-Contaminated survival Times (AFFECT) R package.
- Implementation of a boosting algorithm to handle corrected survival time data.
- Utilizing high-dimensional gene expression data for survival time analysis.
Main Results:
- The AFFECT package effectively corrects for measurement error effects in survival times.
- It successfully identifies informative gene expressions relevant to survival outcomes.
- The package derives corresponding nonlinear functions, enhancing the accuracy of survival models.
Conclusions:
- The AFFECT R package offers a reliable solution for survival analysis with measurement error-corrupted data.
- It enables accurate identification of key genes and nonlinear associations in high-dimensional datasets.
- This tool improves the precision of survival time estimations and clinical outcome predictions.
Related Concept Videos
01:18Introduction To Survival Analysis
199
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...
The primary goal of survival analysis is to estimate survival time—the time...
199
01:20Comparing the Survival Analysis of Two or More Groups
164
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...
164
01:24Kaplan-Meier Approach
115
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,...
115
01:11Hazard Rate
95
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...
95
01:15Assumptions of Survival Analysis
111
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.
111
01:09Censoring Survival Data
72
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...
72