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

Censoring Survival Data01:09

Censoring Survival Data

671
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...
671
Randomized Experiments01:13

Randomized Experiments

9.4K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.4K
Odds Ratio01:09

Odds Ratio

2.4K
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
2.4K
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
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

601
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...
601
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.9K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
8.9K

You might also read

Related Articles

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

Sort by
Same author

Precision prebiotics: Engineering food-derived polysaccharides to target specific SCFA-producing taxa for neuroprotection via the microbiota-gut-brain axis.

Current research in food science·2026
Same author

Ethical Challenges in the Delivery of Family-Centered Nursing Care.

Journal of family nursing·2026
Same author

A Multi-Task Segmentation and Classification Network Based on Ultrasound Images for Predicting the Grading of Ascites in the Abdominal Cavity.

Ultrasonic imaging·2026
Same author

Targeting the Myocardial Microenvironment: Novel Antiviral Strategies and Therapeutic Perspectives for Coxsackievirus B-Induced Myocarditis.

Journal of the American Heart Association·2026
Same author

Developing High Performance N-Oxygenase for Azomycin Synthesis through Ancestral Sequence Reconstruction.

ACS synthetic biology·2026
Same author

Application of single-cell transcriptomic technology in cell fate determination of embryonic liver.

Histology and histopathology·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Apr 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

3.8K

A random pattern mixture model for ordinal outcomes with informative dropouts.

Chengcheng Liu1, Sarah J Ratcliffe2, Wensheng Guo2

  • 1Allergan, Inc., 200 Somerset Corporate Blvd, Suite 6001, Bridgewater, NJ, 08807, U.S.A.

Statistics in Medicine
|April 21, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing health data with dropouts. The model accurately captures how patient dropouts affect longitudinal health outcomes, improving analysis accuracy.

Keywords:
EM algorithmNewton-Raphson algorithmPattern mixture modeladaptive gaussian quadratureordinal outcome

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K
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

Related Experiment Videos

Last Updated: Apr 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

3.8K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.1K
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

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Health Outcomes Research

Background:

  • Longitudinal studies often face challenges with informative dropouts, where patient withdrawal is related to the outcome.
  • Existing statistical models may not adequately account for the complex relationship between dropout processes and longitudinal outcomes.
  • Accurate modeling is crucial for reliable interpretation of treatment effects and disease progression.

Purpose of the Study:

  • To extend the random pattern mixture joint model to accommodate longitudinal ordinal outcomes and informative dropouts.
  • To develop a robust statistical framework for analyzing patient data where dropouts are not random.
  • To improve the understanding of factors influencing health outcomes in the presence of non-random patient attrition.

Main Methods:

  • Developed a random pattern mixture joint model incorporating latent 'pattern' effects based on covariates.
  • Defined random pattern effects as latent variables linking dropout and ordinal longitudinal outcomes.
  • Employed the Expectation-maximization (EM) algorithm for parameter estimation.
  • Utilized simulations to assess model performance under diverse assumptions.

Main Results:

  • The proposed model demonstrated that anemia in end-stage renal disease patients was significantly affected by baseline iron treatment when dropout information was adjusted.
  • This finding contrasted with results from independent or shared parameter models, highlighting the importance of accounting for informative dropouts.
  • The model successfully linked patient covariates to patterns of dropout and outcome trajectories.

Conclusions:

  • The random pattern mixture joint model provides a more accurate approach for analyzing longitudinal ordinal data with informative dropouts compared to traditional methods.
  • Accounting for informative dropouts is essential for correctly assessing treatment effects and understanding disease progression, as shown in the end-stage renal disease example.
  • The developed model offers a valuable tool for researchers in health sciences and biostatistics dealing with complex patient attrition.