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

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
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)...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

You might also read

Related Articles

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

Sort by
Same author

Comparing the sensory effects of menthol and synthetic coolants in e-liquids: A pilot investigation.

Experimental and clinical psychopharmacology·2026
Same author

Water addition to e-liquids to reduce flavour aldehyde acetal formation: chemistry and user sensory experience and appeal.

Tobacco control·2026
Same author

Cognitive behavioral telehealth treatment for adolescents with loss-of-control eating: A randomized controlled feasibility study.

Psychotherapy (Chicago, Ill.)·2026
Same author

Three-part random effect models for longitudinal skewed survey data with "not applicable" responses.

Journal of educational and behavioral statistics : a quarterly publication sponsored by the American Educational Research Association and the American Statistical Association·2026
Same author

Combining DNA methylation features and clinical characteristics predicts ketamine treatment response for PTSD.

iScience·2026
Same author

Multivariate mixed models accounting for don't know options in ordinal data.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)·2026

Related Experiment Video

Updated: May 27, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Dirichlet Component Regression and its Applications to Psychiatric Data.

Ralitza Gueorguieva1, Robert Rosenheck, Daniel Zelterman

  • 1Division of Biostatistics, Department of Epidemiology and Public Health Yale University, New Haven, CT 06520.

Computational Statistics & Data Analysis
|November 8, 2011
PubMed
Summary

This study introduces a Dirichlet multivariable regression for analyzing component percentages, like those in schizophrenia symptom scales. The method identifies factors influencing the relative contributions of different symptom clusters in patients.

More Related Videos

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

Related Experiment Videos

Last Updated: May 27, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

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

Area of Science:

  • Statistical modeling
  • Psychometrics
  • Clinical research methodology

Background:

  • Psychiatric assessments often comprise multiple components, each with relative contributions to a total score.
  • Existing statistical methods may not adequately model simultaneous covariate effects on these component proportions.
  • There is a need for methods to analyze how patient characteristics influence the relative importance of different symptom dimensions.

Purpose of the Study:

  • To present a novel Dirichlet multivariable regression method for analyzing compositional data.
  • To demonstrate the application of this method in understanding factors affecting symptom components in schizophrenia.
  • To provide a statistical framework for simultaneously assessing covariate impacts on multiple outcome proportions.

Main Methods:

  • Development and description of a Dirichlet multivariable regression model.
  • Application of the model to Positive and Negative Syndrome Scale (PANSS) data from a schizophrenia clinical trial.
  • Simultaneous examination of socio-demographic and co-morbid correlates on PANSS component scores.
  • Inclusion of diagnostic measures for overdispersion, Cook's distance, and local influence.

Main Results:

  • Identification of specific socio-demographic and co-morbid variables associated with changes in the relative contributions of PANSS components.
  • Demonstration of how the regression model can reveal nuanced relationships between patient characteristics and symptom profiles.
  • Quantification of the influence of various correlates on distinct aspects of schizophrenia symptomatology.

Conclusions:

  • The Dirichlet multivariable regression offers a robust approach for analyzing proportional data in psychiatry and related fields.
  • This method enhances the understanding of factors influencing complex symptom structures, such as those measured by the PANSS.
  • The findings highlight the utility of advanced statistical modeling for dissecting the multifactorial nature of psychiatric conditions.