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

Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.5K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

9.1K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
9.1K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

11.1K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
11.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

159
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
159
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

955
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
955

You might also read

Related Articles

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

Sort by
Same author

Feasibility and Acceptability of a Mobile App and Wearable Device for Collecting Mental Health Survey and Passively Sensed Data Among Health Care Workers in Kenya: Mixed Methods Pilot Study.

JMIR mHealth and uHealth·2026
Same author

Pan-cancer molecular signatures connecting aspartate transaminase to cancer prognosis, metabolic and immune signatures.

Frontiers in oncology·2026
Same author

Endoscopy underuse for new-onset iron deficiency anemia in younger veterans.

The American journal of managed care·2026
Same author

Key predictors of postpartum depression and anxiety symptoms among mothers in Kilifi, Kenya: a machine learning approach.

Frontiers in psychiatry·2026
Same author

The Multiple Dimensions of Self-Management in Active Inflammatory Bowel Disease: A Qualitative Analysis of Patients' Daily Lived Experiences.

Digestive diseases and sciences·2026
Same author

Predicting off-track development in infants aged 0-6 months in low-resource settings using machine learning.

Pediatric research·2026
Same journal

Response to Jamalinia and Lonardo.

Clinical and translational gastroenterology·2026
Same journal

The impact of obesity on inflammatory cytokines and 90- and 180-day survival in patients with alcohol-associated hepatitis.

Clinical and translational gastroenterology·2026
Same journal

Serum IFABP level as an index of mucosal health in celiac disease: a small intestinal morphometry study.

Clinical and translational gastroenterology·2026
Same journal

Global, regional and national burden of liver cancer in middle-aged and older adults from 1990 to 2021: a comprehensive analysis with cross-country inequality, decomposition, and frontier analysis.

Clinical and translational gastroenterology·2026
Same journal

Correction to: Serum and Urinary Metabolomics Reflect the Early Stages of De Novo Metabolic Syndrome After Liver Transplant: A 2-Center Longitudinal Study.

Clinical and translational gastroenterology·2026
Same journal

A Novel Endoscopic Biopsy Technique for Translational Research in Patients with Inflammatory Bowel Disease.

Clinical and translational gastroenterology·2026
See all related articles

Related Experiment Video

Updated: May 4, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

A primer on predictive models.

Akbar K Waljee1, Peter D R Higgins2, Amit G Singal3

  • 11] Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA [2] Veterans Affairs Center for Clinical Management Research, Ann Arbor, Michigan, USA.

Clinical and Translational Gastroenterology
|January 4, 2014
PubMed
Summary
This summary is machine-generated.

Prediction research is gaining traction, but its methods differ from traditional explanatory research. This primer clarifies prediction research methods in gastroenterology to improve study quality.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K

Related Experiment Videos

Last Updated: May 4, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K

Area of Science:

  • Gastroenterology
  • Medical Research Methodology
  • Predictive Analytics

Background:

  • Prediction research is increasingly popular in gastroenterology.
  • Methodologic quality varies due to poor understanding of differences between explanatory and predictive research.
  • A clear distinction between research types is needed.

Purpose of the Study:

  • To describe basic methods for conducting prediction research in gastroenterology.
  • To highlight key differences between traditional explanatory research and predictive research.
  • To improve the methodologic quality of gastroenterology prediction studies.

Main Methods:

  • Review and synthesis of existing literature on prediction research.
  • Comparison of methodologies used in explanatory versus predictive studies.
  • Focus on practical application within gastroenterology.

Main Results:

  • Prediction research requires distinct methodologies compared to explanatory research.
  • Key differences lie in study design, data analysis, and outcome definition.
  • Best practices for gastroenterology prediction research are outlined.

Conclusions:

  • Understanding the unique methods of prediction research is crucial.
  • Implementing these methods can enhance the quality and reliability of gastroenterology prediction studies.
  • This primer serves as a guide for researchers in the field.