Harmonizing Various Comorbidity, Multimorbidity, and Frailty
Measures
person(given = "Wyatt",
family = "Bensken",
role = c("aut", "cre"),
email = "wpb27@case.edu",
comment = c(ORCID = "0000-0002-2597-9732"))
Identifying comorbidities, frailty, and multimorbidity in claims
and administrative data is often a duplicative process.
The functions contained in this package are meant to first prepare the data to a format
acceptable by all other packages, then provide a uniform and simple approach to
generate comorbidity and multimorbidity metrics based on these claims data. The package
is ever evolving to include new metrics, and is always looking for new measures to include.
The citations used in this package include the following publications:
Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) <doi:10.1097/00005650-199801000-00004>,
Brian J Moore, Susan White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>,
Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) <doi:10.1016/0021-9681(87)90171-8>,
Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>,
Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>,
Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>,
Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>,
Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>,
Martin Fortin, José Almirall, and Kathryn Nicholson (2017)<doi:10.15256/joc.2017.7.122>.
tidyverse, dplyr, lubridate, magrittr, stringr, rlang,
tidyselect, tidyr, devtools, sqldf, stats
Wyatt Bensken [aut, cre] (<https://orcid.org/0000-0002-2597-9732>)

Regression analysis of overdispersed correlated count data
This is a likelihood approach for the regression analysis of overdispersed correlated count data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.