Jove
Visualize
Contact Us
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 Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Multiple Regression01:25

Multiple Regression

4.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.3K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.8K
Regression Analysis01:11

Regression Analysis

8.9K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.9K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.4K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.4K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

710
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...
710

You might also read

Related Articles

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

Sort by
Same author

Nonparametric Estimation of the Patient-Weighted While-Alive Estimand.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

Axial length as a risk factor for pseudophakic rhegmatogenous retinal detachment: A Danish registry study.

Acta ophthalmologica·2026
Same author

Doubly robust nonparametric estimators of the predictive value of covariates for survival data.

Biometrics·2025
Same author

<sup>18</sup>F-FDG PET/CT assessment of metabolic tumor burden predicts survival in patients with metastatic posterior uveal melanoma.

Scientific reports·2025
Same author

Comparing glaucoma risk in children receiving low-dose and high-dose glucocorticoid treatment after cataract surgery.

Acta ophthalmologica·2024
Same author

Relationship between syringomyelia and myxomatous mitral valve disease in Cavalier King Charles spaniels.

Journal of veterinary internal medicine·2024
Same journal

Shared frailty sieve estimation for dependent left truncated and interval censored data.

Lifetime data analysis·2026
Same journal

Functional win-fractions regression models for composite outcomes.

Lifetime data analysis·2026
Same journal

Variable selection in causal semiparametric transformation models with all-or-nothing treatment compliance.

Lifetime data analysis·2026
Same journal

Correction to: A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model.

Lifetime data analysis·2026
Same journal

Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia.

Lifetime data analysis·2026
Same journal

Variable selection with broken adaptive ridge regression for interval-censored competing risks data.

Lifetime data analysis·2026
See all related articles

Related Experiment Video

Updated: Mar 31, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Cox regression with missing covariate data using a modified partial likelihood method.

Torben Martinussen1, Klaus K Holst2, Thomas H Scheike2

  • 1Department of Biostatistics, University of Copenhagen, Øster Farimagsgade 5B, 1014, Copenhagen K, Denmark. tma@sund.ku.dk.

Lifetime Data Analysis
|October 24, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new Cox regression method to handle missing covariate data in survival analysis without the EM-algorithm. The novel approach offers consistent estimation and variance calculation for categorical covariates.

Keywords:
Cox modelMissing covariate dataRecursive estimationSurvival data

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

11.2K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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

15.5K

Related Experiment Videos

Last Updated: Mar 31, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K
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

11.2K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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

15.5K

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Missing covariate values are a frequent challenge in survival analysis.
  • Existing methods like the EM-algorithm can be computationally intensive or require distributional assumptions.

Purpose of the Study:

  • To develop a novel, efficient method for Cox regression with missing covariate data.
  • To provide a statistically sound alternative to the EM-algorithm.
  • To address situations with categorical covariates without distributional assumptions.

Main Methods:

  • A new method for Cox regression is proposed, approximating maximum likelihood estimation.
  • The method profiles out the baseline hazard function using a Breslow-type estimator.
  • Focuses on categorical covariates, avoiding distributional assumptions for estimators.

Main Results:

  • The proposed estimator is shown to be consistent and asymptotically normal.
  • A consistent variance-covariance matrix estimator is derived, avoiding perturbation parameters.
  • Performance is validated through simulations and a real-data example.

Conclusions:

  • The novel method effectively handles missing covariate data in Cox regression for categorical variables.
  • It offers a computationally efficient and statistically robust alternative.
  • The approach provides reliable estimation and variance calculation without strong assumptions.