Related Experiment Video
Updated: Oct 17, 2025
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
3.5K
Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction
Pao-Sheng Shen1, Yingwei Peng2, Hsin-Jen Chen3
1Department of Statistics, Tunghai University, Xitun District, Taichung, 40704, Taiwan, ROC.
Lifetime Data Analysis
|October 8, 2021
Summary
This study analyzes length-biased and interval-censored data with a nonsusceptible fraction, crucial for epidemiological cohort studies. It introduces methods for valid inferences and survival function estimation, even with covariates.
Area of Science:
- Epidemiology
- Biostatistics
- Survival Analysis
Background:
- Left-truncated data are common in cohort studies.
- Length-biased data is a subset of left-truncated data.
- Analysis of interval-censored data with nonsusceptible fractions is complex.
Purpose of the Study:
- To analyze length-biased and interval-censored data with a nonsusceptible fraction.
- To develop methods for valid inferences from length-biased samples.
- To estimate survival functions and nonsusceptible rates.
Main Methods:
- Utilizing a discrete survival function for susceptible individuals.
- Employing the Expectation-Maximization (EM) algorithm for nonparametric maximum likelihood estimates.
- Developing a graphical method to assess stationarity assumptions.
- Applying Cox proportional hazards and logistic regression models when covariates are present.
Main Results:
- Nonparametric maximum likelihood estimates for nonsusceptible rate and survival function were obtained.
- Large sample properties of the estimates were established.
- The proposed methods demonstrated good performance in simulations.
- The model was successfully applied to diabetes mellitus data.
Conclusions:
- The study provides robust methods for analyzing complex survival data in epidemiology.
- The EM algorithm and graphical methods offer valuable tools for researchers.
- The findings have implications for understanding disease progression in populations with nonsusceptible fractions.
Related Concept Videos
01:24Kaplan-Meier Approach
304
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,...
304
01:09Censoring Survival Data
284
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...
284
01:21Confidence Intervals
8.0K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
A...
8.0K
01:14Parametric Survival Analysis: Weibull and Exponential Methods
707
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...
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...
707
01:15Assumptions of Survival Analysis
218
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.
218
01:11Hazard Rate
217
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...
217