Analyze multiple exposure realizations in association studies

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 doi:10.1201/9781420010138 ), extended regression calibration (Little et al. 2023 doi:10.1038/s41598-023-42283-y ), Monte Carlo maximum likelihood (Stayner et al. 2007 doi:10.1667/RR0677.1 ), frequentist model averaging (Kwon et al. 2023 doi:10.1371/journal.pone.0290498 ), and Bayesian model averaging (Kwon et al. 2016 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

Sander Roberti <sander.roberti@nih.gov>, William Wheeler <WheelerB@imsweb.com>, Ruth Pfeiffer <pfeiffer@mail.nih.gov>, and Deukwoo Kwon <DKwon@uams.edu

References

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