A modular and computationally efficient R package for
parameterizing, simulating, and analyzing health-economic simulation
models. The package supports cohort discrete time state transition models
(Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>,
N-state partitioned survival models (Glasziou et al. 1990)
<doi:10.1002/sim.4780091106>, and individual-level continuous
time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>,
encompassing both Markov (time-homogeneous and time-inhomogeneous) and
semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is
quantified with standard graphical and tabular summaries of a probabilistic
sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>,
<doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table
make individual-patient simulation, probabilistic sensitivity analysis,
and incorporation of patient heterogeneity fast.
covr, ggplot2, kableExtra, knitr, mstate, msm, nnet,
numDeriv, pracma, rmarkdown, scales, testthat, truncnorm

data.table, flexsurv, MASS, msm, Rcpp (>= 0.12.16), R6, stats,
survival
covr, ggplot2, kableExtra, knitr, mstate, nnet, numDeriv,
pracma, rmarkdown, scales, testthat, truncnorm

Health Economic Simulation Modeling and Decision Analysis
A modular and computationally efficient R package for
parameterizing, simulating, and analyzing health economic simulation
models. The package supports cohort discrete time state transition models
(Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>,
N-state partitioned survival models (Glasziou et al. 1990)
<doi:10.1002/sim.4780091106>, and individual-level continuous
time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>,
encompassing both Markov (time-homogeneous and time-inhomogeneous) and
semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is
quantified with standard graphical and tabular summaries of a probabilistic
sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>,
<doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table
make individual-patient simulation, probabilistic sensitivity analysis,
and incorporation of patient heterogeneity fast.
data.table, flexsurv, ggplot2, MASS, msm, Rcpp (>= 0.12.16),
R6, stats, survival
covr, kableExtra, knitr, magrittr, mstate, nnet, numDeriv,
pracma, rmarkdown, scales, testthat, truncnorm