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

Modeling in Therapy01:26

Modeling in Therapy

826
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
826
Steps in the Modeling Process01:14

Steps in the Modeling Process

875
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
875
Observational Studies01:11

Observational Studies

8.9K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
8.9K
Longitudinal Studies01:26

Longitudinal Studies

711
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
711
Longitudinal Research02:20

Longitudinal Research

11.7K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.7K
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

1.7K
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Association of Genomic Prostate Score at positive margin with recurrence after radical prostatectomy.

BJU international·2024
Same author

Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer.

European urology oncology·2023
Same author

ERG Status at the Margin Is Associated With Biochemical Recurrence After Radical Prostatectomy With Positive Surgical Margins.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2023
Same author

Urinary PSA and Serum PSA for Aggressive Prostate Cancer Detection.

Cancers·2023
Same author

Reply by Authors.

The Journal of urology·2022
Same author

Cell Cycle Progression Score, but Not Phosphatase and Tensin Homolog Loss, Is an Independent Prognostic Factor for Metastasis in Intermediate- and High-risk Prostate Cancer in Men Treated With and Without Salvage Radiotherapy.

The Journal of urology·2022

Related Experiment Video

Updated: May 7, 2026

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

10.3K

Modeling grade progression in an active surveillance study.

Lurdes Y T Inoue1, Bruce J Trock, Alan W Partin

  • 1Department of Biostatistics, University of Washington, Seattle, WA, U.S.A.

Statistics in Medicine
|October 15, 2013
PubMed
Summary
This summary is machine-generated.

Prostate cancer tumor grade can change over time, even if initial biopsies suggest otherwise. Accounting for biopsy misclassification reveals true grade progression rates in active surveillance patients.

Keywords:
Bayesian analysisprostate cancersimulation modeling

More Related Videos

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model
08:03

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

Published on: November 4, 2025

455
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

2.8K

Related Experiment Videos

Last Updated: May 7, 2026

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

10.3K
Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model
08:03

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

Published on: November 4, 2025

455
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

2.8K

Area of Science:

  • Oncology
  • Biostatistics

Background:

  • Prostate cancer grading using Gleason score is crucial for prognosis.
  • Active surveillance (AS) involves regular biopsies (BX) to monitor tumor grade.
  • Biopsy grade is prone to misclassification, complicating assessment of true tumor changes.

Purpose of the Study:

  • To develop a statistical model for estimating prostate cancer grade progression.
  • To account for biopsy misclassification errors in serial grade assessments.
  • To determine the true rate of grade change in men undergoing active surveillance.

Main Methods:

  • Developed a Bayesian statistical model to estimate time of true grade change.
  • Incorporated biopsy misclassification rates from prostate cancer studies.
  • Applied the model to serial biopsy data from 627 active surveillance patients.

Main Results:

  • Estimated the likelihood of grade progression within 10 years ranged from 12% to 24%, depending on prior assumptions.
  • Demonstrated that accounting for misclassification provides a more accurate rate of true grade progression.
  • Identified a non-trivial fraction of patients experiencing tumor grade progression.

Conclusions:

  • Accurate assessment of prostate cancer grade progression requires accounting for biopsy misclassification.
  • The developed model enables better estimation of true grade changes in active surveillance.
  • Findings highlight the importance of monitoring for true grade progression in AS cohorts.