sparrpowR

sparrpowR estimates statistical power for detecting spatial clustering between two groups (e.g., cancer cases versus controls or different exposure groups) using kernel-based spatial relative risk and simulation of point-level data to inform epidemiological and environmental health study design.

Key Features:

• Kernel-Based Spatial Relative Risk Function: Implements the kernel-based spatial relative risk function to evaluate spatial relative risk surfaces and identify clusters in point-level data.
• Simulation-Based Power Estimation: Generates simulated point-level datasets from user-defined parameters, including sample size and location data, to estimate statistical power for detecting spatial clusters.
• Local Power Calculation: Computes local power for cluster detection to assess spatially varying sensitivity across the study region.
• Study Design Support: Provides simulation outputs and power estimates tailored to designing studies of cancer incidence and environmental exposure-related clustering.

Scientific Applications:

• Cancer Epidemiology Studies: Planning and power assessment for studies of spatial distributions of cancer incidence in relation to potential risk factors.
• Environmental Health Research: Designing studies to detect spatial patterns associated with environmental emissions and exposures.
• Two-Group Spatial Cluster Detection: Assessing the ability to detect spatial clustering between two groups such as cases versus controls or differing exposure groups.

Methodology:

Uses the kernel-based spatial relative risk function on point-level data and simulates datasets based on user-defined parameters (sample size and location data) to estimate statistical power and local power for spatial cluster detection.