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

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

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

701
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...
701
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
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

727
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
727
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

498
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.
498

You might also read

Related Articles

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

Sort by
Same author

Differential effects of κ-carrageenan and konjac glucomannan emulsion gels on the in vitro digestive behaviors of proteins in textured vegetable protein-based emulsions.

International journal of biological macromolecules·2026
Same author

A longitudinal investigation of aggression and social skills in autistic youth.

Research in autism·2026
Same author

Letter to the Editor Regarding "How Choice of Effect Measure Influences Minimally Sufficient Adjustment Sets for External Validity" by Webster-Clark and Keil (2023): The Covariate Class Taxonomy Should Be Strictly Defined With Respect To The Full Set Of Potential Outcome-Generating Covariates.

American journal of epidemiology·2026
Same author

Evolving roles of Data Coordinating Centers in multisite research: Challenges and adaptations from a rapid scoping review.

Journal of clinical and translational science·2026
Same author

Global and temporal trends in neonatal and under-five mortality due to birth asphyxia/trauma and prematurity (2000-2021) with projections up to 2040.

World journal of pediatrics : WJP·2026
Same author

Temporal trends and patterns in neonatal sepsis mortality across 194 countries, 2000-2021, with projections up to 2050.

Journal of perinatology : official journal of the California Perinatal Association·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Apr 18, 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

3.8K

Evaluating model-based imputation methods for missing covariates in regression models with interactions.

Soeun Kim1, Catherine A Sugar, Thomas R Belin

  • 1Department of Biostatistics, School of Public Health, University of Texas Health Science Center, Houston, Texas, 77030, U.S.A.

Statistics in Medicine
|January 30, 2015
PubMed
Summary
This summary is machine-generated.

Accurate imputation methods are crucial for incomplete data analysis, especially with interaction terms. Our study shows the correct conditional distribution performs best, but some multivariate normality approaches offer comparable results for regression models.

Keywords:
interactionmissing covariatemultiple imputationmultivariate normalregression

More Related Videos

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

6.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K

Related Experiment Videos

Last Updated: Apr 18, 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

3.8K
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

6.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K

Area of Science:

  • Statistics
  • Biostatistics
  • Psychological Research

Background:

  • Incomplete data is common in statistical analyses.
  • Imputation strategies are vital for handling missing data.
  • Assumptions in imputation methods can impact analysis outcomes.

Purpose of the Study:

  • To evaluate multiple imputation strategies for regression models with interaction terms and a partially observed covariate.
  • To compare the performance of imputation methods based on correct conditional distributions versus approximations using multivariate normality.
  • To assess the reliability of different imputation techniques in the presence of model assumption incompatibilities.

Main Methods:

  • Derivation of the conditional distribution for the missing covariate and interaction term.
  • Implementation and evaluation of a multiple imputation procedure based on the derived distribution.
  • Adaptation of multivariate normal multiple imputation software for alternative procedures.
  • Comparison of methods using bias, coverage, and confidence interval width.

Main Results:

  • The imputation procedure based on the correct conditional distribution demonstrated optimal performance across all scenarios.
  • Several multivariate normality-based approaches yielded results comparable to the correct method in many situations.
  • Methods attempting to preserve the multiplicative relationship of interaction terms proved less reliable.

Conclusions:

  • The choice of imputation strategy significantly affects regression analyses involving interaction terms.
  • While theoretically sound methods are preferred, practical approximations using multivariate normality can be effective.
  • Careful consideration of imputation method assumptions is essential for reliable statistical inference, particularly in psychological studies.