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

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
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

567
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...
567
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.7K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.7K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

593
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
593
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

710
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...
710
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

311
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...
311

You might also read

Related Articles

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

Sort by
Same author

Piloting pond fencing for child drowning prevention: a process evaluation in rural India.

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention·2026
Same author

Association Between Celiac Disease and Uncontrolled Hemoglobin A1c Levels in Type 1 Diabetes Pediatric Patients.

Pediatric diabetes·2026
Same author

Racial and ethnic differences in COVID-19 infection and vaccine uptake across multiple waves of the pandemic in Southeast Michigan: a retrospective cohort study.

Frontiers in public health·2026
Same author

Drowning deaths in West Bengal, India: a statewide community-based survey.

BMJ global health·2025
Same author

Disparities in Lung Cancer Screening: Demographic and Socioeconomic Influences.

AJPM focus·2025
Same author

Association of health behaviors with healthcare workers' physical and psychological well-being: Learning from the COVID-19 pandemic.

PloS one·2025

Related Experiment Video

Updated: Apr 1, 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 Bayesian approach for inducing sparsity in generalized linear models with multi-category response.

Behrouz Madahian, Sujoy Roy, Dale Bowman

    BMC Bioinformatics
    |October 2, 2015
    PubMed
    Summary

    This study introduces a Sparse Bayesian Generalized Linear Model (GLM) with a Generalized Double Pareto (GDP) prior for analyzing gene expression data. The method accurately predicts prostate cancer subtypes and identifies early-stage metastatic cancer, offering valuable diagnostic insights.

    More Related Videos

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.1K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.3K

    Related Experiment Videos

    Last Updated: Apr 1, 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 Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.1K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.3K

    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • High-throughput gene expression data presents analysis challenges due to its high dimensionality and complexity.
    • Reducing variables is crucial for downstream analysis, especially with limited sample sizes.

    Purpose of the Study:

    • To develop and evaluate a Sparse Bayesian Generalized Linear Model (GLM) incorporating a Generalized Double Pareto (GDP) prior for enhanced gene expression data analysis.
    • To improve the accuracy of cancer subtype classification and early detection of metastatic stages.

    Main Methods:

    • Utilized a hierarchical Sparse Bayesian GLM with a GDP prior (SBGG) to model the progressive nature of cancer.
    • Applied the SBGG model to a publicly available microarray dataset of 99 samples across four prostate cancer subtypes.

    Main Results:

    • The SBGG model achieved classification accuracies between 82.5% and 94%, outperforming Support Vector Machine, Random Forest, and a Sparse Bayesian GLM with double exponential priors.
    • SBGG demonstrated superior performance in identifying pre-metastatic cancer stages, crucial for therapeutic and diagnostic applications.
    • Genes identified by SBGG showed higher functional cohesion (p-value 2.0E-4) compared to other methods.

    Conclusions:

    • The GDP prior within a Bayesian GLM framework enhances subclass prediction for cancer progression data.
    • The SBGG method significantly improves the accuracy of identifying pre-metastatic prostate cancer and generates more functionally relevant gene sets.