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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

495
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.
495
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

342
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...
342
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

405
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
405
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

415
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
415
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

725
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...
725
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

352
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
352

You might also read

Related Articles

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

Sort by
Same author

Transfer between Reading Comprehension and Word-Problem Solving among Children with Learning Difficulty in Both Domains.

Journal of educational psychology·2025
Same author

Determining the power of a 1-sided z-test given only the power of the corresponding 2-sided test.

Journal of behavioral medicine·2025
Same author

Power analysis to detect misfit in SEMs with many items: Resolving unrecognized problems, relating old and new approaches, and "matching" power analysis approach to data analysis approach.

Psychological methods·2024
Same author

On the Common but Problematic Specification of Conflated Random Slopes in Multilevel Models.

Multivariate behavioral research·2023
Same author

Latent profile analyses of disordered eating behaviors and nonsuicidal self-injury among Vietnamese adolescents.

The International journal of eating disorders·2022
Same author

R-squared Measures for Multilevel Models with Three or More Levels.

Multivariate behavioral research·2022

Related Experiment Video

Updated: Apr 17, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

10.0K

A Latent Transition Analysis Model for Latent-State-Dependent Nonignorable Missingness.

Sonya K Sterba1

  • 1Quantitative Methods Program, Department of Psychology and Human Development, Vanderbilt University, Peabody #552, 230 Appleton Place, Nashville, TN, 37203 , USA. sonya.sterba@vanderbilt.edu.

Psychometrika
|February 21, 2015
PubMed
Summary
This summary is machine-generated.

Latent transition analysis (LTA) can be biased by missing data. A new parallel-process LTA (MNAR-PP LTA) method corrects this bias, providing more accurate insights into behavioral changes.

Keywords:
latent transition analysismissing not at randommixture modelnonignorable missing datashared parameter model

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
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Related Experiment Videos

Last Updated: Apr 17, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

10.0K
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
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Area of Science:

  • Psychology
  • Statistics
  • Quantitative Psychology

Background:

  • Latent transition analysis (LTA) is commonly used to study changes in discrete latent states, particularly for delinquent or risky behaviors.
  • Nonignorable missing data, where missingness depends on unobserved states, is a significant concern in LTA.
  • Existing methods for handling nonignorable missingness are well-developed for some longitudinal models but not for LTA.

Purpose of the Study:

  • To introduce a novel shared parameter latent transition analysis (LTA) model to address latent-state-dependent nonignorable missingness.
  • To develop a parallel-process missing-not-at-random (MNAR-PP) LTA that reduces bias caused by nonignorable missing data.
  • To facilitate sensitivity analyses by maintaining interpretable outcome process parameters.

Main Methods:

  • Development of a parallel-process missing-not-at-random (MNAR-PP) latent transition analysis (LTA) model.
  • Implementation of a shared parameter approach within the LTA framework.
  • Conducting a sensitivity analysis using an empirical example with adolescent delinquency data.

Main Results:

  • The proposed MNAR-PP LTA effectively reduces bias stemming from latent-state-dependent nonignorable missingness.
  • In the empirical example, previous and current high-delinquency states predicted missingness.
  • A conventional LTA overestimated the proportion of adolescents in low-delinquency states compared to the MNAR-PP LTA.

Conclusions:

  • The MNAR-PP LTA offers a robust method for analyzing longitudinal data with nonignorable missingness in psychological research.
  • This approach provides more accurate estimates of state transitions compared to conventional LTA when missing data are present.
  • Researchers should consider employing the MNAR-PP LTA for studies involving sensitive behaviors and potential data missingness.