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

Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

6.9K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
6.9K
Hazard Rate01:11

Hazard Rate

437
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...
437
Hazard Ratio01:12

Hazard Ratio

614
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
614
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.0K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.0K
Partial Fractions01:28

Partial Fractions

225
A partial fraction is a component of a rational expression represented as the sum of simpler fractions. When a rational function is expressed as a ratio of two polynomials, it can often be decomposed into a sum of fractions whose denominators are simpler polynomials, typically linear or irreducible quadratic factors. This process is called partial fraction decomposition, and it is used to simplify complex expressions for integration, solving equations, or analysis.Partial fraction decomposition...
225
Linear Circuits01:17

Linear Circuits

882
A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
882

You might also read

Related Articles

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

Sort by
Same author

Adverse and positive childhood experiences and suicidal ideation onset in a US pre-early adolescent cohort.

BMC medicine·2026
Same author

Penalized estimation of linear transformation models for interval-censored data with time-dependent covariates.

Statistical methods in medical research·2026
Same author

Evaluation of an Emergency Department-Based Peer Recovery Support Intervention for People Who Use Substances in Nevada.

Substance use & addiction journal·2026
Same author

COVID-19 stay-at-home orders impacts on suicide attempts among adolescents in the United States.

Public health·2026
Same author

Penalized estimation of general frailty Poisson models for recurrent count events.

Statistical methods in medical research·2025
Same author

Behavioral and Sociodemographic Determinants of Influenza Vaccination Among Caregivers During the COVID-19 Pandemic.

American journal of health promotion : AJHP·2025
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Feb 8, 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

10.9K

A partially linear proportional hazards model for current status data.

Minggen Lu1, Christopher S McMahan2

  • 1School of Community Health Sciences, University of Nevada-Reno, Reno, Nevada 89557, U.S.A.

Biometrics
|July 6, 2018
PubMed
Summary
This summary is machine-generated.

A new flexible partially linear proportional hazards model enhances survival data analysis using monotone and B-splines. This approach offers efficient computation and optimal convergence rates for analyzing complex health data.

Keywords:
Current status dataEM algorithmMonotone splinesPartially linear modelsProportional hazards model

More Related Videos

A Mouse Model of Intestinal Partial Obstruction
07:33

A Mouse Model of Intestinal Partial Obstruction

Published on: March 5, 2018

22.6K
Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status
13:18

Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status

Published on: May 18, 2019

12.6K

Related Experiment Videos

Last Updated: Feb 8, 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

10.9K
A Mouse Model of Intestinal Partial Obstruction
07:33

A Mouse Model of Intestinal Partial Obstruction

Published on: March 5, 2018

22.6K
Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status
13:18

Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status

Published on: May 18, 2019

12.6K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Analyzing current status data requires flexible statistical models.
  • Existing proportional hazards models may lack flexibility for nonlinear covariate effects.
  • Accurate modeling is crucial for understanding time-to-event data in cohort studies.

Purpose of the Study:

  • To propose a flexible partially linear proportional hazards model for current status data.
  • To approximate the baseline cumulative hazard function and accommodate nonlinear covariate effects.
  • To develop an efficient expectation-maximization algorithm for model fitting.

Main Methods:

  • Utilizing monotone splines for the baseline cumulative hazard function.
  • Employing B-splines to model nonlinear covariate effects.
  • Implementing a two-stage data augmentation expectation-maximization algorithm with latent Poisson variables.

Main Results:

  • The proposed spline estimator demonstrates asymptotic normality and efficiency for regression coefficients.
  • Nonparametric components achieve optimal rates of convergence under regularity conditions.
  • Monte Carlo simulations and uterine fibroid data analysis validate the approach's performance.

Conclusions:

  • The flexible partially linear proportional hazards model provides a robust framework for current status data.
  • The developed algorithm is computationally efficient and easy to implement.
  • The model effectively handles nonlinearities and improves survival data analysis.