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

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

6.4K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
6.4K
What are Estimates?01:06

What are Estimates?

7.6K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
7.6K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

792
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,...
792
Weighted Mean00:57

Weighted Mean

5.5K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.5K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

7.4K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
7.4K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K

You might also read

Related Articles

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

Sort by
Same author

One-year outcomes of infants discharged with a nasogastric feeding tube.

Journal of perinatology : official journal of the California Perinatal Association·2026
Same author

Implementing an adapted patient safety bundle for pregnancy-related severe hypertension to improve recognition, response, and respectful care in the outpatient setting.

Implementation science communications·2026
Same author

Allostatic Load in Endometrial Cancer Disparities.

medRxiv : the preprint server for health sciences·2026
Same author

Peripheral Positions and Reachability of Casual Sexual Partners in Egocentric Networks among Sexual and Gender Minorities.

Sexually transmitted diseases·2026
Same author

Building Equitable Linkages with Interprofessional Education Valuing Everyone (BELIEVE): research protocol for a multisite step-wedge cluster randomized trial.

BMC pregnancy and childbirth·2026
Same author

Functional characterization of furin-mediated lipoprotein lipase cleavage.

Disease models & mechanisms·2026

Related Experiment Video

Updated: May 4, 2026

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

10.3K

Nonparametric estimation of the mean function for recurrent event data with missing event category.

Feng-Chang Lin1, Jianwen Cai1, Jason P Fine1

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.

Biometrika
|January 14, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces novel nonparametric methods for analyzing recurrent event data with missing categories. These methods provide unbiased estimates of baseline event rates, outperforming traditional approaches in simulation studies.

Keywords:
Cystic fibrosisLocal polynomial regressionNelson–Aalen estimationPseudomonas aeruginosa infectionRate proportion

More Related Videos

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

9.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K

Related Experiment Videos

Last Updated: May 4, 2026

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

10.3K
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

9.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Recurrent event data are common in longitudinal studies.
  • Categorization of recurrent events can be incomplete due to missing data.
  • Complete case analysis may yield biased estimates for baseline event rates.

Purpose of the Study:

  • To develop fully nonparametric methods for analyzing recurrent event data with unspecified missingness mechanisms.
  • To provide consistent and asymptotically normal estimators for mean event functions and event category probabilities.
  • To offer a robust alternative to existing methods that rely on parametric missingness models.

Main Methods:

  • Development of fully nonparametric estimation techniques.
  • Analysis of consistency and asymptotic normality of proposed estimators.
  • Introduction of plug-in variance estimators.
  • Application to real-world data from a cystic fibrosis registry.

Main Results:

  • Nonparametric estimators for mean event functions are consistent and asymptotically normal.
  • Estimators accommodate slower convergence of nonparametric event category probabilities.
  • Proposed methods demonstrate superior performance compared to complete case and parametric methods in simulations.
  • Complete case estimators showed significant bias, and parametric estimators had larger mean squared errors when misspecified.

Conclusions:

  • The developed fully nonparametric methods effectively handle missing recurrent event categories without specifying the missingness mechanism.
  • These methods provide reliable estimates for baseline event rates, crucial for understanding disease progression and treatment efficacy.
  • The approach is validated through simulations and a practical application in cystic fibrosis research.