ameras-package: Analyze multiple exposure realizations in association studies

In ameras: Analyze Multiple Exposure Realizations in Association Studies

Analyze multiple exposure realizations in association studies

Description

Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 <doi:10.1667/RR13729.1>) to characterize exposure uncertainty. Methods include regression calibration (Carroll et al. 2006 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/9781420010138")}), extended regression calibration (Little et al. 2023 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41598-023-42283-y")}), Monte Carlo maximum likelihood (Stayner et al. 2007 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1667/RR0677.1")}), frequentist model averaging (Kwon et al. 2023 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0290498")}), and Bayesian model averaging (Kwon et al. 2016 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.6635")}). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic.

Details

The main function is ameras.

Author(s)

Sander Roberti <[e-mail]>, William Wheeler <[e-mail]>, Ruth Pfeiffer <[e-mail]>, and Deukwoo Kwon <[e-mail]

References

Roberti, S., Kwon D., Wheeler W., Pfeiffer R. (in preparation). ameras: An R Package to Analyze Multiple Exposure Realizations in Association Studies

ameras documentation built on March 29, 2026, 5:07 p.m.