Etiologic heterogeneity refers to the concept of subtypes of disease that are influenced by different risk factors. Likely the best known example of this is in breast cancer research, where subtypes are often formed based on immunohistochemical staining of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). Risk factors including patient characteristics such as age and BMI as well as hormonal risk factors such as age at menarche, parity, and menopausal status have been shown to have different relative risks for these disease subtypes.

riskclustr is a collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study or case-only study, and provide p-values to test for etiologic heterogeneity with respect to either disease subtypes or individual disease markers in the context of a case-control study.

Installation

You can install the development version of riskclustr by running:

devtools::install_github("zabore/riskclustr")
library(riskclustr)

Or the prodcution version from CRAN by running:

Documentation

This package is documented using pkgdown, and the resulting website is available at https://www.emilyzabor.com/riskclustr/, where detailed Tutorials can be found covering all of the package functionality.

See https://www.emilyzabor.com/riskclustr/reference/ for detailed function documentation.

References

Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE. A conceptual and methodological framework for investigating etiologic heterogeneity. Stat Med. 2013;32(29):5039-52. doi: 10.1002/sim.5902

Begg CB, Seshan VE, Zabor EC, et al. Genomic investigation of etiologic heterogeneity: methodologic challenges. BMC Med Res Methodol. 2014;14:138. doi: 10.1186/1471-2288-14-138

Zabor, EC. riskclustr: Functions to Study Etiologic Heterogeneity. Journal of Open Source Software. 2019;4(35):1269. doi:10.21105/joss.01269