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 Experiment Videos

Statistical methods in diagnosis.

D J Hand1

  • 1Department of Statistics, Faculty of Mathematics, Open University, Milton Keynes, UK.

Statistical Methods in Medical Research
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Interaction of light and temperature on seed germination of Rumex obtusifolius L.

Planta·2013
Same author

Mining medical data.

Statistical methods in medical research·2000
Same author

An improved measure for comparing diagnostic tests.

Computers in biology and medicine·2000
Same author

Improving the practice of classifier performance assessment.

Neural computation·2000
Same author

Clinical impact versus factor analysis for quality of life questionnaire construction.

Journal of clinical epidemiology·1998
Same author

Generalisation from phase III clinical trials: survival, quality of life, and health economics.

Lancet (London, England)·1997
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
See all related articles

This study explores formal statistical methods for medical diagnosis, discussing their pros and cons. It reviews various techniques and provides criteria for selection and performance assessment.

Area of Science:

  • Medical Statistics
  • Diagnostic Methods
  • Computational Biology

Background:

  • Formal statistical methods offer rigorous approaches to medical diagnosis.
  • Understanding the advantages and disadvantages of various statistical techniques is crucial for effective implementation.
  • The complexity of medical data necessitates a review of diverse analytical tools.

Purpose of the Study:

  • To motivate the exploration of formal statistical methods in medical diagnosis.
  • To discuss the benefits and drawbacks associated with different statistical approaches.
  • To provide a comprehensive overview of available statistical techniques for diagnostic applications.

Main Methods:

  • Review of classical linear discriminant analysis, quadratic discriminant analysis, logistic regression, nearest neighbor, and kernel methods.

Related Experiment Videos

  • Exploration of recursive partitioning, independence models, regularized discriminant analysis, and structured conditional probability distributions.
  • Inclusion of methods for categorical data and other relevant statistical techniques.

    • Main Results:

    • Presentation of criteria for selecting appropriate statistical methods for medical diagnosis.
    • Outline of methods for assessing the performance of diagnostic tools.
    • Illustrative applications in screening and chromosome analysis.

    Conclusions:

    • Formal statistical methods provide a robust framework for medical diagnosis.
    • A systematic approach to method selection and performance evaluation enhances diagnostic accuracy.
    • Software availability facilitates the practical application of these statistical techniques.