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Related Experiment Video

Updated: Jun 12, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Multiple testing on standardized mortality ratios: a Bayesian hierarchical model for FDR estimation.

Massimo Ventrucci¹, E Marian Scott, Daniela Cocchi

¹Department of Statistics, Glasgow University, Glasgow G12 8QQ, UK. mventrucci@stats.gla.ac.uk

Biostatistics (Oxford, England)

|June 26, 2010

Summary

This summary is machine-generated.

This study introduces a Bayesian approach to improve the detection of environmental risk factors using standardized mortality ratios (SMRs). The method enhances sensitivity in small areas, outperforming traditional p-value methods for false discovery rate (FDR) control.

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Published on: July 3, 2020

Area of Science:

Epidemiology
Biostatistics
Spatial Analysis

Background:

Descriptive epidemiology uses standardized mortality ratios (SMRs) to detect environmental risk factors.
Small areas with low expected counts and spatially correlated risks pose challenges for traditional methods.
Poisson p-value-based false discovery rate (FDR) control methods may lack sensitivity in small areas.

Purpose of the Study:

To propose a Bayesian approach for evaluating the null hypothesis of no risk in SMRs.
To control the posterior false discovery rate (FDR) for improved risk detection.
To enhance sensitivity in identifying risks in small geographical areas.

Main Methods:

A Bayesian hierarchical model incorporating spatial random effects was implemented.
The model allows for extra-Poisson variability.
Posterior probabilities of no risk were estimated to compute the posterior FDR.

Main Results:

The proposed Bayesian method provides an estimate of the posterior FDR.
This estimate enables non-arbitrary FDR-based selection rules for high-risk areas.
Simulations demonstrated improved sensitivity and specificity compared to p-value-based rules.

Conclusions:

The Bayesian approach offers a more sensitive method for detecting environmental risk factors in small areas.
It provides a robust framework for controlling the posterior FDR.
The method was validated through simulation and real-data analysis.