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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Longitudinal Studies01:26

Longitudinal Studies

701
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...
701
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

725
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
725
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.6K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.6K
Longitudinal Research02:20

Longitudinal Research

11.7K
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...
11.7K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

333
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
333

You might also read

Related Articles

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

Sort by
Same author

Plum-blossom needling enhanced the effect of photodynamic therapy on basal cell carcinoma.

Photodiagnosis and photodynamic therapy·2018
Same author

A novel exonuclease-assisted isothermal nucleic acid amplification with ultrahigh specificity mediated by full-length Bst DNA polymerase.

Chemical communications (Cambridge, England)·2018
Same author

SREBP1, targeted by miR-18a-5p, modulates epithelial-mesenchymal transition in breast cancer via forming a co-repressor complex with Snail and HDAC1/2.

Cell death and differentiation·2018
Same author

In situ fluorescence monitoring of diagnosis and treatment: a versatile nanoprobe combining tumor targeting based on MUC1 and controllable DOX release by telomerase.

Chemical communications (Cambridge, England)·2018
Same author

DNA barcoding of marine fish species from Rongcheng Bay, China.

PeerJ·2018
Same author

Characterization of the Populus Rab family genes and the function of PtRabE1b in salt tolerance.

BMC plant biology·2018

Related Experiment Video

Updated: Apr 26, 2026

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

Bias in longitudinal data analysis with missing data using typical linear mixed-effects modelling and pattern-mixture

Manshu Yang1, Lijuan Wang, Scott E Maxwell

  • 1Health and Social Development Program, American Institute for Research, Chapel Hill, North Carolina, USA.

The British Journal of Mathematical and Statistical Psychology
|August 8, 2014
PubMed
Summary

Typical linear mixed-effects modeling (TLME) shows biased estimates in longitudinal data with missing not at random (MNAR) mechanisms. A pattern-mixture (PM) approach offers unbiased estimates for specific MNAR data, unlike TLME.

More Related Videos

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

5.8K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K

Related Experiment Videos

Last Updated: Apr 26, 2026

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
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

5.8K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Linear growth curve models are essential for analyzing longitudinal data.
  • Missing data in longitudinal studies can lead to biased estimates.
  • Typical linear mixed-effects modeling (TLME) and pattern-mixture (PM) models are common approaches for handling missing data.

Purpose of the Study:

  • To analytically derive fixed-effects estimates in unconditional linear growth curve models.
  • To compare the performance of TLME and PM approaches under random-slope-dependent missing not at random (MNAR) data.
  • To investigate the conditions under which PM models provide unbiased estimates.

Main Methods:

  • Analytical derivation of fixed-effects estimates using TLME and PM.
  • Simulation study to illustrate and compare TLME and PM results.
  • Empirical data analysis to assess generalizability of findings.

Main Results:

  • TLME estimates are biased under random-slope-dependent MNAR mechanisms due to incorrect weighting.
  • The mean slope estimate in TLME is biased towards completers' slopes; the intercept estimate is biased in the opposite direction.
  • PM provides unbiased estimates for random-coefficients-dependent MNAR data but not for missing at random or outcome-dependent MNAR data.

Conclusions:

  • TLME is inadequate for longitudinal data with random-slope-dependent MNAR missingness.
  • The PM approach is a viable alternative for specific MNAR scenarios in longitudinal data analysis.
  • Sensitivity analysis is recommended for longitudinal data with missing values to ensure robust findings.