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

43
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...
43
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

131
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.
131
Censoring Survival Data01:09

Censoring Survival Data

96
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
96
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
Survival Tree01:19

Survival Tree

86
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
86
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

101
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
101

You might also read

Related Articles

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

Sort by
Same author

Trajectories of child emotional and behavioural difficulties before and during the COVID-19 pandemic in a longitudinal UK cohort.

JCPP advances·2026
Same author

The reporting and handling of missing data in genetic epidemiological studies of mental health in childhood and adolescence: A systematic review.

JCPP advances·2026
Same author

Birth Weight Percentiles and Infant and Child Growth Dynamics.

JAMA network open·2026
Same author

Evaluating the Impact of Different Natural History Modeling Methods on Cost-Effectiveness Decisions: A Case Study in Duchenne Muscular Dystrophy.

MDM policy & practice·2026
Same author

Capturing infant and child growth dynamics with P-splines mixed effects models.

International journal of obesity (2005)·2026
Same author

Body Mass Index, Clinical Outcomes, and Mortality in Heart Failure: A Mendelian Randomization Study.

Journal of the American College of Cardiology·2026

Related Experiment Video

Updated: Jul 5, 2025

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

10.7K

Multiple imputation strategies for missing event times in a multi-state model analysis.

Elinor Curnow1,2,3, Rachael A Hughes2,3, Kate Birnie2,3

  • 1Department of Statistics and Clinical Research, NHS Blood and Transplant, Bristol, UK.

Statistics in Medicine
|January 23, 2024
PubMed
Summary

Multiple imputation (MI) using predictive mean matching (PMM) effectively handles missing event times in multi-state model (MSM) analyses. This method, especially when applied to subgroups, reduces bias and improves precision in understanding disease progression.

Keywords:
Markovmissing datamulti-state modelmultiple imputationpredictive mean matching

More Related Videos

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

3.4K
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

6.3K

Related Experiment Videos

Last Updated: Jul 5, 2025

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

10.7K
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

3.4K
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

6.3K

Area of Science:

  • Biostatistics
  • Clinical Research Methodology
  • Health Data Science

Background:

  • Multi-state models (MSM) are crucial for analyzing patient event sequences and disease progression.
  • Partially observed event times present a significant challenge in many MSM clinical studies.
  • Standard missing data methods may be inadequate when event times are missing at random and depend on event type.

Purpose of the Study:

  • To evaluate the performance of multiple imputation (MI) for handling missing event times in MSM analyses.
  • To assess the effectiveness of MI by predictive mean matching (PMM) under specific missing data conditions.
  • To provide recommendations for robust MSM analysis with partially observed event times.

Main Methods:

  • Utilized a real-world dataset of stem cell transplant patients with partially observed event times.
  • Conducted an extensive simulation study to compare MI with other missing data techniques.
  • Focused on MI by predictive mean matching (PMM), sampling from observed times without parametric assumptions.
  • Investigated subgroup-specific PMM application within the MSM framework.

Main Results:

  • Multiple imputation (MI) by predictive mean matching (PMM) demonstrated low bias for missing event times, even with complex missingness patterns.
  • Applying PMM separately for patient subgroups with distinct pathways through the MSM further reduced bias and enhanced precision.
  • Compared to maximum likelihood, complete case analysis, and inverse probability weighting, MI-PMM showed superior performance.

Conclusions:

  • Multiple imputation (MI) using predictive mean matching (PMM) is a recommended and effective method for MSM analyses with partially observed event times.
  • Subgroup-specific PMM application can optimize results in complex multi-state models.
  • This approach offers a flexible and robust solution for clinical studies facing missing event time data.