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

Induced-fit Model01:13

Induced-fit Model

89.4K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
89.4K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.4K
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:  
1.4K
Confirmation Biases01:31

Confirmation Biases

8.3K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
8.3K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

277
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
277
Mixtures of Acids03:27

Mixtures of Acids

21.9K
The pH of a solution containing an acid can be determined using its acid dissociation constant and its initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending upon the relative strength of the acids and their dissociation constants.
A Mixture of a Strong Acid and a Weak Acid
In a mixture of a strong acid and a weak acid, the strong acid dissociates completely and becomes a source of almost all the hydronium ions...
21.9K
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

6.9K
When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
6.9K

You might also read

Related Articles

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

Sort by
Same author

Impact of abdominal obesity prevalence trends on dementia, cardiovascular disease, functional impairment, and mortality in older Chinese adults: A Markov scenario simulation, 2020-2050.

PLoS medicine·2026
Same author

Association between carotid-femoral pulse wave velocity and cardiovascular disease in individuals with moderate blood pressure: a systematic review and individual participant meta-analysis.

BMJ open·2025
Same author

Correction: Direct and indirect impacts of the COVID-19 pandemic on life expectancy and person-years of life lost with and without disability: A systematic analysis for 18 European countries, 2020-2022.

PLoS medicine·2025
Same author

Direct and indirect impacts of the COVID-19 pandemic on life expectancy and person-years of life lost with and without disability: A systematic analysis for 18 European countries, 2020-2022.

PLoS medicine·2025
Same author

Closing the gap in dementia research by community-based cohort studies in the Chinese population.

The Lancet regional health. Western Pacific·2025
Same author

Erratum: Many-Body Interference at the Onset of Chaos [Phys. Rev. Lett. 130, 080401 (2023)].

Physical review letters·2024

Related Experiment Video

Updated: Feb 6, 2026

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.2K

Does pattern mixture modelling reduce bias due to informative attrition compared to fitting a mixed effects model to

Catherine A Welch1, Séverine Sabia2,3, Eric Brunner2

  • 1Department of Epidemiology and Public Health, University College London, Gower Street, London, WC1E 7HB, UK. catherine.welch@ucl.ac.uk.

BMC Medical Research Methodology
|August 31, 2018
PubMed
Summary

Informative attrition in longitudinal studies can bias results. Pattern mixture modeling (PMM) may reduce bias from non-monotone missing data, especially with moderately correlated outcomes, though some underestimation may persist.

Keywords:
Informative attritionLongitudinal observational dataMultiple imputationPattern mixture modelling

More Related Videos

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.4K
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

3.8K

Related Experiment Videos

Last Updated: Feb 6, 2026

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.2K
Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.4K
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

3.8K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Informative attrition, where dropout reasons correlate with study outcomes, can bias longitudinal study inferences.
  • Existing methods to mitigate bias in longitudinal data primarily address monotone missingness, with less clarity on non-monotone patterns.
  • Non-monotone missingness, where participants may return after dropping out, presents unique challenges for bias reduction.

Purpose of the Study:

  • To compare different statistical approaches for reducing bias caused by informative attrition in longitudinal data with non-monotone missingness.
  • To evaluate the performance of available case analysis, multiple imputation (MI), and pattern mixture modeling (PMM) in simulated epidemiological data.

Main Methods:

  • Simulated data from the Whitehall II cohort study, incorporating informative attrition linked to cognitive decline.
  • Linear mixed-effects models with random intercepts and slopes were used to assess the association between smoking and cognitive decline.
  • Compared bias in slope coefficient estimates from: (1) available case analysis, (2) MI (two-fold fully conditional specification), and (3) PMM adjusted for known bias.

Main Results:

  • For highly correlated repeated measures, fitting mixed-effects models to available cases yielded the least biased slope coefficients.
  • With moderately correlated outcome measurements, PMM-adjusted data resulted in the least biased estimates, although they still underestimated true coefficients.
  • Multiple imputation (MI) did not adequately reduce bias under non-ignorable missingness assumptions.

Conclusions:

  • Pattern mixture modeling (PMM) shows potential for reducing bias in longitudinal studies with suspected informative attrition and moderately correlated outcomes.
  • Incorporating additional auxiliary variables into the imputation model may further mitigate residual bias.
  • Careful consideration of missing data mechanisms and analytical approaches is crucial for valid inferences in longitudinal research.