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

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 situations...
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)...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

You might also read

Related Articles

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

Sort by
Same author

Regression Discontinuity Design: Simulation and Application in Two Cardiovascular Trials with Continuous Outcomes.

Epidemiology (Cambridge, Mass.)·2016
Same author

Development of a Prognostic Nomogram for Patients with Peritoneally Metastasized Colorectal Cancer Treated with Cytoreductive Surgery and HIPEC.

Annals of surgical oncology·2016
Same author

Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury.

Journal of clinical epidemiology·2016
Same author

Calibrating Parameters for Microsimulation Disease Models: A Review and Comparison of Different Goodness-of-Fit Criteria.

Medical decision making : an international journal of the Society for Medical Decision Making·2016
Same author

Feature selection and validated predictive performance in the domain of Legionella pneumophila: a comparative study.

BMC research notes·2016
Same author

Disease-specific survival of patients with invasive cribriform and intraductal prostate cancer at diagnostic biopsy.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2016

Related Experiment Videos

Developing and evaluating prediction models in rehabilitation populations.

Ronald T Seel1, Ewout W Steyerberg, James F Malec

  • 1Crawford Research Institute and Brain Injury Program, Shepherd Center, Atlanta, GA 30309, USA. ron_seel@shepherd.org

Archives of Physical Medicine and Rehabilitation
|July 31, 2012
PubMed
Summary

This article offers a framework for creating and assessing prediction models in rehabilitation. It covers developing research questions, building and validating models, and evaluating study quality for better patient outcomes.

Related Experiment Videos

Area of Science:

  • Rehabilitation Medicine
  • Biostatistics
  • Clinical Epidemiology

Background:

  • Developing accurate prediction models is crucial for effective rehabilitation.
  • Existing frameworks for prediction model development and evaluation in rehabilitation are limited.
  • Standardized approaches are needed to ensure the reliability and validity of rehabilitation prediction models.

Purpose of the Study:

  • To present a comprehensive 3-part framework for developing and evaluating prediction models in rehabilitation populations.
  • To guide researchers in refining prognostic research questions and the scientific approach to prediction modeling.
  • To provide criteria for evaluating the quality of prediction model studies in rehabilitation.

Main Methods:

  • The framework outlines the scientific approach, including study design, sampling, outcome measurement, predictor selection, bias minimization, sample size determination, and statistical model selection.
  • It details a 7-step statistical process for building and validating multivariable prediction models.
  • Quality indicators are proposed for evaluating prediction model studies.

Main Results:

  • The article presents a structured process for developing prediction models, emphasizing key methodological and statistical considerations.
  • A clear pathway for statistically building and validating multivariable models is provided.
  • Specific quality indicators are suggested for assessing the rigor of prediction model studies.

Conclusions:

  • The proposed framework enhances the development and evaluation of prediction models in rehabilitation.
  • Adherence to this framework can improve the quality and applicability of rehabilitation prediction models.
  • This work facilitates the future development and clinical use of robust rehabilitation prediction models.