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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Inference based on kernel estimates of the relative risk function in geographical epidemiology
Martin L Hazelton1, Tilman M Davies
1Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand. m.hazelton@massey.ac.nz
Biometrical Journal. Biometrische Zeitschrift
|February 7, 2009
Summary
This study introduces a faster method for calculating tolerance contours in geographical epidemiology, using asymptotic theory instead of Monte Carlo tests. This improves the analysis of relative risk surfaces and disease mapping.
Area of Science:
- Geographical Epidemiology
- Spatial Statistics
Background:
- Kernel smoothing is widely used for estimating relative risk surfaces in geographical epidemiology.
- Tolerance contours aid in interpreting these surfaces by identifying areas of significantly elevated risk.
Purpose of the Study:
- To evaluate a computationally efficient alternative to Monte Carlo randomization tests for calculating tolerance intervals.
- To assess the performance of global tests for heterogeneous risk using kernel-based statistics.
Main Methods:
- Derivation of tolerance intervals from asymptotic theory as a computationally cheaper alternative.
- Examination of global tests for heterogeneous risk, focusing on the impact of smoothing parameters on statistical power.
Main Results:
- Asymptotic theory provides a computationally inexpensive method for deriving tolerance intervals.
- The choice of smoothing parameters significantly influences the power of global tests for heterogeneous risk.
Conclusions:
- The proposed asymptotic method offers a practical and efficient approach for analyzing relative risk surfaces.
- Careful selection of smoothing parameters is crucial for robust statistical inference in disease mapping studies.
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