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

Non-parametric maximum likelihood estimators for disease mapping.

A Biggeri1, M Marchi, C Lagazio

  • 1Department of Statistics G. Parenti, University of Florence, Italy. abiggeri@ds.unifi.it

Statistics in Medicine
|August 29, 2000
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

Sinonasal cancer incidence in Lombardy, Italy, 2008-20.

Occupational medicine (Oxford, England)·2024
Same author

Assessing injury risk in male and female Royal Navy recruits: does the Functional Movement Screen provide understanding to inform effective injury mitigation?

BMJ military health·2023
Same author

Tracing the footprints of SARS-CoV-2 in oceanic waters.

The Science of the total environment·2023
Same author

Sensitivity of contact-tracing for COVID-19 in Thailand: a capture-recapture application.

BMC infectious diseases·2022
Same author

The middle cingulate cortex and dorso-central insula: A mirror circuit encoding observation and execution of vitality forms.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

Communicative And Affective Components in Processing Auditory Vitality Forms: An fMRI Study.

Cerebral cortex (New York, N.Y. : 1991)·2021
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Non-Parametric Maximum Likelihood (NPML) disease mapping estimates closely align with Bayesian models, offering a flexible alternative. The NPML autoregressive approach proved superior for relative risk estimation in real-world disease mapping scenarios.

Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Statistical Modeling

Background:

  • Disease mapping requires accurate estimation of relative risks.
  • Existing methods like Hierarchical Bayesian models and Maximum Likelihood via Monte Carlo Scoring have limitations.
  • Conditional autoregressive models are crucial for spatial analysis in disease mapping.

Purpose of the Study:

  • To introduce and evaluate a Non-Parametric Maximum Likelihood (NPML) approach for disease mapping.
  • To propose an NPML approximation for conditional autoregressive models.
  • To compare NPML estimates with existing methods using real disease data.

Main Methods:

  • Developed a Non-Parametric Maximum Likelihood (NPML) approximation for conditional autoregressive models.

Related Experiment Videos

  • Compared NPML estimates with Maximum Likelihood via Monte Carlo Scoring and Hierarchical Bayesian models.
  • Applied methods to real-world breast cancer and leukemia disease mapping examples.

    • Main Results:

    • NPML autoregressive estimates (with a weighted term) closely matched Hierarchical Bayesian estimates.
    • The exchangeable NPML model performed well, showing greater shrinkage.
    • The truncated auto-Poisson model was inadequate for disease mapping.
    • Analysis revealed distinct spatial correlation patterns for breast cancer in urban vs. rural areas.
    • The Poisson-Gamma model failed to detect high-risk areas for leukemia under specific conditions.

    Conclusions:

    • The NPML approach offers a general, simple, and flexible method for disease mapping and relative risk estimation.
    • NPML autoregressive models provide interpretable coefficients reflecting spatial disease patterns.
    • Users should be aware of potential local maxima and challenges in determining the optimal number of components with NPML.
    • Specialized software (CAMAN, DismapWin) is recommended for users, especially those less experienced with NPML methods.