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

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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...

You might also read

Related Articles

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

Sort by
Same author

Retrospective longitudinal analysis of blood microRNA-7-5p as a possible progression biomarker in people with Parkinson's disease.

Frontiers in neuroscience·2026
Same author

Glucose-6-Phosphate Dehydrogenase Modifies the Impact of Glucose on Arterial Aging in A Sex-Specific Manner.

Journal of the American Heart Association·2026
Same author

Rehabilitative Interventions for Flight-Related Musculoskeletal Injuries in the Neck, Shoulder, and Back among Military Pilots and Aircrew: A Systematic Review With Meta-Analysis.

Military medicine·2026
Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Angiotensin II, miR-34a, and AGTRAP crosstalk in arterial smooth muscle cells.

GeroScience·2025
Same author

Chronic kidney disease and cardiac remodeling potentiate cognitive impairment progression: disentangling the sex-specific cross talk of kidney-heart-brain axis.

American journal of physiology. Heart and circulatory physiology·2025
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: May 21, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Screening for prostate cancer using multivariate mixed-effects models.

Christopher H Morrell1, Larry J Brant, Shan Sheng

  • 1Mathematics and Statistics Department, Loyola University Maryland, 4501 North Charles St., Baltimore, MD 21210-2699 USA.

Journal of Applied Statistics
|June 9, 2012
PubMed
Summary
This summary is machine-generated.

This study developed a multivariate classification method to predict prostate cancer onset using prostate specific antigen (PSA), free testosterone index (FTI), and body mass index (BMI). Combining these variables improves prediction accuracy for high-risk or low-risk prostate cancer.

Related Experiment Videos

Last Updated: May 21, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

Area of Science:

  • Biostatistics
  • Oncology
  • Epidemiology

Background:

  • Prostate cancer prediction remains a challenge.
  • Early detection of prostate cancer is crucial for effective treatment.
  • Identifying predictive biomarkers for prostate cancer is an active area of research.

Purpose of the Study:

  • To develop and evaluate a multivariate classification method for predicting clinical prostate cancer onset.
  • To assess the predictive performance of prostate specific antigen (PSA), free testosterone index (FTI), and body mass index (BMI) individually and in combination.
  • To differentiate between high-risk and low-risk prostate cancer cases.

Main Methods:

  • A multivariate mixed-effects model was employed to analyze longitudinal changes in PSA, FTI, and BMI.
  • Bayes' theorem was applied to calculate posterior probabilities for cancer prediction.
  • A sequential classification approach was used, analyzing data one observation at a time.
  • Analyses were conducted using individual variables, pairs, and all three variables together.

Main Results:

  • The study demonstrated that a multivariate approach using PSA, FTI, and BMI can predict prostate cancer development.
  • Combining variables generally improved classification accuracy compared to individual variable analyses.
  • The model showed potential in distinguishing between high-risk and low-risk prostate cancer.

Conclusions:

  • Multivariate classification models incorporating longitudinal changes in PSA, FTI, and BMI show promise for early prostate cancer prediction.
  • The predictive sensitivity is influenced by the number and type of variables included.
  • This approach may aid in identifying individuals at risk for prostate cancer and stratifying risk levels.