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

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 squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.

You might also read

Related Articles

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

Sort by
Same author

Bayesian Estimation of Hierarchical Linear Models From Incomplete Data: Cluster-Level Interaction Effects and Small Sample Sizes.

Statistics in medicine·2025
Same author

Variability in Causal Effects and Noncompliance in a Multisite Trial: A Bivariate Hierarchical Generalized Random Coefficients Model for a Binary Outcome.

Statistics in medicine·2024
Same author

Racial disparities in cancer genetic counseling encounters: study protocol for investigating patient-genetic counselor communication in the naturalistic clinical setting using a convergent mixed methods design.

BMC cancer·2023
Same author

An exploration of cultural competency training and genetic counselors' racial biases.

Journal of genetic counseling·2023
Same author

Opportunities and Challenges When Using the Electronic Health Record for Practice-Integrated Patient-Facing Interventions: The e-Assist Colon Health Randomized Trial.

Medical decision making : an international journal of the Society for Medical Decision Making·2022
Same author

Predictors of smell recovery in a nationwide prospective cohort of patients with COVID-19.

American journal of otolaryngology·2021

Related Experiment Video

Updated: Jul 14, 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

Just-identified versus overidentified two-level hierarchical linear models with missing data.

Yongyun Shin1, Stephen W Raudenbush

  • 1University of Michigan, 439 West Hall, 1085 South University, Ann Arbor, Michigan 48109-1107, USA. choil@umich.edu

Biometrics
|May 16, 2007
PubMed
Summary

This study introduces a method for efficiently estimating hierarchical linear models (HLMs) with incomplete data. The approach ensures accurate parameter estimation even when data is missing at multiple levels, improving statistical practice for complex datasets.

More Related Videos

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

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

Related Experiment Videos

Last Updated: Jul 14, 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

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

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

Area of Science:

  • Statistics
  • Statistical Modeling
  • Data Analysis

Background:

  • Model-based methods are crucial for handling incomplete data in statistical practice.
  • Estimating the joint distribution of complete data (Theta) from observed data (y(obs)) is a common approach.
  • Hierarchical linear models (HLMs) are frequently used for clustered data, but their parameters often lie in a subspace of Theta, leading to overidentification.

Purpose of the Study:

  • To develop an efficient method for estimating hierarchical linear models (HLMs) with incomplete data.
  • To address the challenge of parameter overidentification in HLMs when dealing with incomplete data.
  • To enable robust statistical inference for HLMs with missing outcomes and covariates at any level.

Main Methods:

  • Characterizing the joint distribution such that its parameters are a one-to-one transformation of the HLM parameters.
  • Utilizing the transformation method for efficient estimation.
  • Applying the method of multiple imputation for handling missing data.

Main Results:

  • The proposed method allows for efficient estimation of HLMs from incomplete data.
  • The approach effectively handles missing outcomes and covariates at both levels of the two-level data structure.
  • It provides a framework for regression analysis within HLMs involving subsets of variables.

Conclusions:

  • The developed method offers an efficient and robust way to estimate HLMs with incomplete data.
  • This technique improves statistical inference for complex hierarchical data structures with missing values.
  • The approach is versatile, accommodating missing data at various levels and supporting flexible regression specifications.