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

Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Conformity01:20

Conformity

Conformity is the change in a person’s behavior to go along with the group, even if that person does not agree with the group.
Deindividuation00:57

Deindividuation

Deindividuation is a form of social influence on an individual’s behavior such that the individual engages in unusual or non-normal behavior while in a group setting. Why? Because in these group settings, the individual no longer sees themselves as an individual anymore, disinhibiting their behavior and personal restraint.
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Longitudinal Research02:20

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...
Conservation of Declining Populations02:07

Conservation of Declining Populations

Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.

You might also read

Related Articles

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

Sort by
Same author

A randomised comparison of management strategies for drug-induced liver injury associated with tuberculous meningitis treatment.

The Journal of infection·2026
Same author

Genotype-stratified adjunctive dexamethasone for tuberculous meningitis in HIV-negative adults: a randomized controlled phase 3 trial.

Nature medicine·2026
Same author

Safety and tolerability of metformin in overweight and obese patients with dengue: An open-label clinical trial (MeDO).

PLoS neglected tropical diseases·2025
Same author

The latency time of SARS-CoV- 2 Delta variant in infection- and vaccine-naive individuals from Vietnam.

BMC infectious diseases·2025
Same author

Statistical analysis plan for the LAST ACT clinical trial; a Leukotriene A4 hydrolase Stratified non-inferiority Trial of Adjunctive Corticosteroids for HIV-negative adults with Tuberculous meningitis.

Wellcome open research·2025
Same author

Correction: The effect of M. tuberculosis lineage on clinical phenotype.

PLOS global public health·2024
Same journal

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.

Statistical methods in medical research·2026
Same journal

Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.

Statistical methods in medical research·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: May 22, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Which individuals make dropout informative?

Ronald B Geskus1

  • 1Academic Medical Center, Amsterdam, The Netherlands.

Statistical Methods in Medical Research
|April 27, 2012
PubMed
Summary
This summary is machine-generated.

Missing marker data in disease progression studies can cause bias. Jointly modeling marker development and dropout risk, like with a random effects selection model, can mitigate this bias, especially with frequent measurements.

Keywords:
AIDS markersLongitudinal datamissing not at randomrandom effects selection model

More Related Videos

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
12:55

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Related Experiment Videos

Last Updated: May 22, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
12:55

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Research Methodology

Background:

  • Marker data are crucial for assessing disease status and recovery.
  • Missing marker data due to patient dropout (e.g., death) is common in longitudinal studies.
  • Missingness is often not completely at random, potentially biasing standard statistical models.

Purpose of the Study:

  • To investigate methods for handling missing marker data in longitudinal studies.
  • To evaluate bias introduced by standard mixed-effects models when missingness is not at random.
  • To compare joint modeling approaches with traditional models for marker data with dropout.

Main Methods:

  • Utilized a random effects selection model to jointly model marker development and dropout risk.
  • Compared results from a random effects model and a random effects selection model using a real-world dataset.
  • Conducted a simulation study to assess the impact of measurement frequency and time-to-dropout on parameter estimate bias.

Main Results:

  • Results from the real data analysis showed similar outcomes between the random effects model and the random effects selection model.
  • Simulation results indicated that bias in parameter estimates from a random effects model is minimal when follow-up measurements are frequent.
  • The frequency of measurements significantly influences the bias associated with missing marker data.

Conclusions:

  • Joint modeling approaches, such as random effects selection models, offer a robust strategy for handling non-randomly missing marker data.
  • Frequent patient follow-up is critical in longitudinal studies to minimize bias in statistical analyses of marker data.
  • While joint models are theoretically sound, practical bias may be small with high-frequency data collection.