Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric 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...

Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

Longitudinal Studies

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

Assumptions of Survival Analysis

Assumptions 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.

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

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

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...

You might also read

Related Articles

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

Sort by

Same author

Metabolic-epigenetic crosstalk in lung cancer: Glycolytic reprogramming and lactylation-driven macrophage polarization.

Pathology, research and practice·2026

Same author

Split-Dose Versus Standard Single-Dose Bowel Preparation Regimens for Colonoscopy: A Systematic Review and Meta-Analysis.

Cureus·2026

Same author

Integrative genomic analysis identifies key target genes and candidate drugs for spinal stenosis.

Frontiers in molecular neuroscience·2026

Same author

Comparative Efficacy and Safety of Remimazolam Versus Propofol for Sedation in Gastrointestinal Endoscopy: A Systematic Review and Meta-Analysis.

Cureus·2026

Same author

Pollution Characteristics and Risk Assessment of Chlorinated Paraffins in Seawater and Kelp from Kelp Mariculture Areas of the Shandong Peninsula.

Toxics·2026

Same author

FA-2-b-β modulates HMGB1/NF-κB/NLRP3 signaling to alleviate neuroinflammation in Alzheimer's disease.

Journal of Alzheimer's disease : JAD·2026

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Estimation of semiparametric regression model with longitudinal data.

¹Department of Mathematics and Statistics, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223, USA. yasun@uncc.edu

Lifetime Data Analysis

|November 6, 2009

Summary

This study introduces a robust statistical method for analyzing longitudinal data with irregular sampling times. The new procedure improves estimation efficiency and accuracy for proportional mean models, outperforming existing methods.

More Related Videos

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

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

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jun 19, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

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

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

Published on: July 3, 2020

Area of Science:

Biostatistics
Longitudinal Data Analysis
Statistical Modeling

Background:

Longitudinal studies involve repeated measurements over time, often with irregular and covariate-dependent sampling.
Accurate statistical modeling is crucial for analyzing such complex data structures.

Purpose of the Study:

To propose a novel sampling-adjusted procedure for estimating proportional mean models.
To develop a method robust to misspecification of sampling times in longitudinal studies.

Main Methods:

Developed a sampling-adjusted estimation procedure for proportional mean models.
Investigated large sample properties of regression coefficient and baseline function estimators.
Constructed large sample confidence intervals for the baseline function.

Main Results:

The proposed method is robust to sampling time model misspecification.
Demonstrated improved efficiency of the new estimation procedure compared to existing methods.
Simulation studies confirmed the finite sample properties of the proposed estimators.

Conclusions:

The novel sampling-adjusted procedure offers a more efficient and robust approach for analyzing longitudinal data with irregular sampling.
The method provides reliable estimation and confidence intervals for key model parameters.