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

Variance01:15

Variance

12.6K
The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the data....
12.6K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

512
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...
512
Margin of Error01:27

Margin of Error

7.7K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.7K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

296
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
296
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

598
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
598
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

267
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
267

You might also read

Related Articles

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

Sort by
Same author

Analysis of Stepped-Wedge Cluster Randomized Trials: A Tutorial Using Marginal Models.

Statistics in medicine·2026
Same author

Identifying Bariatric Surgery Patients With the Most Favorable Cost Outcomes.

Health services research·2026
Same author

A Pharmacist Consultant Service for Deprescribing Opioids and Benzodiazepines in Older Adults: A Cluster Randomized Trial.

JAMA network open·2026
Same author

Inflammasome targeting for periodontitis prevention is sex dependent.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Quality-of-life factors at home and in health care settings for people living with dementia.

The Gerontologist·2025
Same author

Mouth Care Without a Battle: Change in Assisted Living Staff Self-Efficacy and Attitudes.

Journal of the American Medical Directors Association·2025

Related Experiment Video

Updated: Feb 14, 2026

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions
09:54

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

Published on: July 22, 2022

3.7K

A marginalized two-part model with heterogeneous variance for semicontinuous data.

Valerie A Smith1,2, John S Preisser3

  • 11 Center for Health Services Research in Primary Care, Durham VAMC, Durham, NC, USA.

Statistical Methods in Medical Research
|February 17, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced marginalized two-part model for analyzing semicontinuous medical data, improving interpretation of covariate effects. The enhanced model accounts for non-constant variance, offering more accurate health care expenditure insights.

Keywords:
Health care expendituresexcess zerosgeneralized gammaheterogeneous variancelog skew normalmarginalized modelssemicontinuous datatwo-part models

More Related Videos

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.3K
Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry
09:12

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Published on: January 12, 2018

15.5K

Related Experiment Videos

Last Updated: Feb 14, 2026

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions
09:54

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

Published on: July 22, 2022

3.7K
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.3K
Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry
09:12

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Published on: January 12, 2018

15.5K

Area of Science:

  • Biostatistics
  • Health Services Research
  • Econometrics

Background:

  • Semicontinuous data, common in medical research, present analysis challenges due to a point mass at zero and a continuous positive distribution.
  • Standard two-part mixture models offer conditional interpretations, limiting understanding of covariate effects on the overall population.
  • Previous work introduced a marginalized two-part model for interpretable effect estimates based on the marginal mean.

Purpose of the Study:

  • To extend the marginalized two-part model to accommodate non-constant variance in the positive component.
  • To incorporate flexible distributional assumptions, specifically log-skew-normal and generalized gamma distributions, into the extended model.
  • To evaluate the performance of the proposed models using simulation studies and illustrate their application in analyzing health care expenditures.

Main Methods:

  • Developed an extended marginalized two-part model allowing variance to be a function of covariates.
  • Integrated log-skew-normal and generalized gamma distributions, which encompass the log-normal distribution.
  • Conducted simulation studies to assess bias, coverage, and efficiency of the proposed models.
  • Applied the framework to analyze the impact of a weight loss intervention on healthcare costs.

Main Results:

  • The extended model successfully incorporates non-constant variance, providing a more comprehensive analysis of semicontinuous data.
  • Simulation results indicate the performance characteristics (bias, coverage, efficiency) of the different distributional assumptions under various scenarios.
  • The application demonstrated the model's utility in evaluating intervention effects on health care expenditures.

Conclusions:

  • The enhanced marginalized two-part model provides a flexible and interpretable framework for analyzing semicontinuous medical data with non-constant variance.
  • The inclusion of log-skew-normal and generalized gamma distributions broadens the applicability of the model.
  • This approach offers improved insights into covariate effects, crucial for health economics and intervention studies.