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

Percentile01:18

Percentile

9.5K
A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
9.5K
Quartile01:15

Quartile

9.9K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
9.9K
Review and Preview01:10

Review and Preview

8.7K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
8.7K
Review and Preview01:13

Review and Preview

12.0K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
12.0K
Outliers and Influential Points01:08

Outliers and Influential Points

6.5K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.5K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

314
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
314

You might also read

Related Articles

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

Sort by
Same author

Diagnostic Deserts: National Gaps in Screening and Care Infrastructure for Metabolic Dysfunction-Associated Steatotic Liver Disease.

The American journal of gastroenterology·2026
Same author

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.

Biometrics·2026
Same author

Mitigating CT number variability between scanners, tube potentials, and patient sizes using spectral CT virtual monoenergetic imaging.

Physics in medicine and biology·2026
Same author

Atezolizumab plus FOLFOX for Stage III Mismatch Repair-Deficient Colon Cancer.

The New England journal of medicine·2026
Same author

SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT.

The annals of applied statistics·2026
Same author

DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS.

The annals of applied statistics·2026
Same journal

Comparing Adaptive Interventions under a General Sequential Multiple Assignment Randomized Trial Design via Multiple Comparisons with the Best.

Journal of statistical planning and inference·2026
Same journal

Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data.

Journal of statistical planning and inference·2026
Same journal

On semi-supervised estimation using exponential tilt mixture models.

Journal of statistical planning and inference·2025
Same journal

Regression-Assisted Bayesian Record Linkage for Causal Inference in Observational Studies with Covariates Spread Over Two Files.

Journal of statistical planning and inference·2024
Same journal

Efficient inference of parent-of-origin effect using case-control mother-child genotype data.

Journal of statistical planning and inference·2024
Same journal

Distributed eQTL analysis with auxiliary information.

Journal of statistical planning and inference·2024
See all related articles

Related Experiment Video

Updated: Mar 9, 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

Quantile Regression Models for Current Status Data.

Fang-Shu Ou1, Donglin Zeng1, Jianwen Cai1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

Journal of Statistical Planning and Inference
|December 21, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new quantile regression model for analyzing current status data, offering direct interpretation of covariate effects on failure time distributions without needing distributional assumptions. The method shows promise in real-world applications like aging studies.

Keywords:
Concave-convex procedureCurrent status dataM-estimationQuantile regressionSubsampling

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
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

Related Experiment Videos

Last Updated: Mar 9, 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
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
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

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Current status data, common in demography and epidemiology, lack precise failure times, only indicating occurrence before or after observation.
  • Analyzing such data requires methods robust to missing exact failure times and distributional assumptions.

Purpose of the Study:

  • To propose a novel quantile regression model for analyzing current status data.
  • To provide a method for direct interpretation of covariate effects on the failure time distribution.
  • To develop a statistically sound estimation and inference procedure for this model.

Main Methods:

  • A quantile regression model is proposed, assuming a linear relationship between covariates and conditional quantiles of failure time.
  • An M-estimator is developed for parameter estimation, utilizing the concave-convex procedure.
  • Confidence intervals are constructed using a subsampling method, with asymptotic properties derived via empirical process theory.

Main Results:

  • The proposed quantile regression model effectively analyzes current status data without requiring distributional assumptions.
  • The M-estimator provides reliable parameter estimation, and the subsampling method yields valid confidence intervals.
  • Simulation studies demonstrate good small sample performance of the developed method.

Conclusions:

  • The new quantile regression approach offers a flexible and interpretable tool for current status data analysis.
  • The method is applicable to various fields, including aging research, as shown by its application to the Mayo Clinic Study of Aging data.