Related Experiment Video
Updated: Jun 15, 2026
06:55
Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Penalized full likelihood approach to variable selection for Cox's regression model under nested case-control
Jie-Huei Wang1,2, Chun-Hao Pan2, I-Shou Chang3,4
1Division of Biostatistics and Bioinformatics, Institute of Population Health Science, National Health Research Institutes, 35, Keyan Rd., Zhunan Town, Miaoli County, 35053, Taiwan.
Lifetime Data Analysis
|May 9, 2019
Summary
This study introduces a penalized full likelihood approach for variable selection in nested case-control (NCC) studies. The proposed method, penalized non-parametric maximum likelihood estimates (PNPMLE), demonstrates superior performance over existing techniques for risk estimation and covariate identification.
Area of Science:
- Biostatistics
- Epidemiology
- Statistical Modeling
Background:
- Nested case-control (NCC) sampling is an efficient design for case-control studies within a cohort.
- Variable selection is crucial for identifying risk factors in epidemiological research.
- Existing methods like weighted partial likelihood may have limitations in estimating relative risks and selecting covariates under NCC sampling.
Purpose of the Study:
- To develop a penalized full likelihood approach for variable selection in NCC studies.
- To evaluate the performance of penalized non-parametric maximum likelihood estimates (PNPMLE) compared to weighted partial likelihood.
- To establish theoretical properties and practical estimation methods for the proposed PNPMLE.
Main Methods:
- Utilized Cox's regression model within a penalized full likelihood framework.
- Derived self-consistency equations for penalized non-parametric maximum likelihood estimates (PNPMLE).
- Employed a cross-validation method based on profile likelihood for tuning parameter selection.
- Investigated the performance of LASSO and SCAD penalties, with SCAD showing better results for larger cohorts.
Main Results:
- PNPMLE showed better performance than weighted partial likelihood in estimating log-relative risk and identifying covariates under NCC sampling.
- LASSO penalty performed best for small cohort sizes, while SCAD performed best for large cohort sizes.
- The SCAD penalty established consistency, asymptotic normality, and oracle properties for PNPMLE, along with sparsity.
- A consistent estimate of asymptotic variance using observed profile likelihood was proposed.
Conclusions:
- The penalized full likelihood approach using PNPMLE is a robust method for variable selection in NCC studies.
- The SCAD penalty offers desirable statistical properties, including oracle properties, for PNPMLE.
- The method was successfully applied to analyze liver cancer diagnosis in a type 2 diabetes mellitus dataset.
Related Concept Videos
01:18Introduction To Survival Analysis
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 until a...
The primary goal of survival analysis is to estimate survival time—the time until a...
01:15Assumptions 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.
01:19The Mantel-Cox Log-Rank Test
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
01:09Truncation in Survival Analysis
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
01:09Censoring 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...
01:14Parametric 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...
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...