Related Experiment Video
Updated: Jan 4, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.7K
A semiparametric linear transformation model to estimate causal effects for survival data
Huazhen Lin1, Yi Li1, Liang Jiang2
1Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, P. R. China.
The Canadian Journal of Statistics = Revue Canadienne De Statistique
|October 30, 2019
Summary
Semiparametric linear transformation models offer an alternative for analyzing survival data with selective compliance. This study demonstrates their consistency and asymptotic normality, achieving parametric convergence rates for accurate parameter and function estimation.
Area of Science:
- Biostatistics
- Survival Analysis
- Clinical Trials
Background:
- Cox proportional hazard models are standard for survival data.
- Selective compliance presents analytical challenges in clinical trials.
- Semiparametric models offer flexible alternatives to traditional methods.
Purpose of the Study:
- To apply semiparametric linear transformation models to survival data with selective compliance.
- To estimate regression parameters and the transformation function.
- To assess the statistical properties and practical utility of the proposed methods.
Main Methods:
- Utilized semiparametric linear transformation models.
- Employed pseudo-likelihood and estimating equations for parameter estimation.
- Evaluated consistency and asymptotic normality of estimators.
Main Results:
- Demonstrated that estimators for regression parameters and the transformation function are consistent.
- Showed asymptotic normality for both types of estimators.
- Confirmed convergence rates of n^(-1/2), matching parametric models.
Conclusions:
- Semiparametric linear transformation models are effective for survival data with selective compliance.
- The proposed estimation methods yield consistent and asymptotically normal results.
- The approach is validated through simulations and a clinical trial application.
Related Concept Videos
01:14Parametric Survival Analysis: Weibull and Exponential Methods
961
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...
961
01:18Introduction To Survival Analysis
689
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...
689
01:15Assumptions of Survival Analysis
361
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.
361
01:24Kaplan-Meier Approach
519
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,...
519
01:18Survival Curves
603
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
603
01:20Comparing the Survival Analysis of Two or More Groups
519
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...
519