Health Economic Simulation Modeling and Decision Analysis
Absorbing states
Apply relative risks to transition probability matrices
Convert between 2D tabular objects and 3D arrays
Convert multi-state data to PFS and OS data
Coerce to data.table
Plot state probabilities
Plot survival curves
Bootstrap a statistical model
A cost-effectiveness object
Cost-effectiveness analysis
Check input data argument for create_input_mats
Input validation for class objects
Cohort discrete time state transition model
Transitions for a cohort discrete time state transition model
Costs object
Create CohortDtstm object
Create CohortDtstmTrans object
Create IndivCtstmTrans object
Create input matrices from formula
Create input matrices
Create a data table of treatment lines
Form a list from ...
Create a parameter object from a fitted model
Create PsmCurves object
Create a StateVals object
Create a data table of health state transitions
An R6 base class for continuous time state transition models
Define and evaluate model expression
Define and evaluate random number generation expressions
Define and evaluate transformed parameter expressions
Disease progression object
Expand hesim_data
Expand object
Matrix exponential
Random generation for generalized gamma distribution
List of flexsurvreg objects
List of formula objects
Get value labels
Data for health economic simulation modeling
List of survival distributions
hesim: Health Economic Simulation Modeling and Decision Analysis
Individualized cost-effectiveness analysis
ICER table
Incremental cost-effectiveness ratio
Attributes for ID variables
Incremental treatment effect
Individual-level continuous time state transition model
Transitions for an individual-level continuous time state transition m...
Input matrices for a statistical model
List of lm objects
Method of moments for beta distribution
Method of moments for gamma distribution
List of multinom objects
Draw parameters of statistical model from multivariate normal distribu...
Parameters of a linear model
Parameters of a list of multinomial logit models
Parameters of a multinomial logit model
Parameters of a list of survival models
Parameters of a survival model
Parameter object
Partitioned survival regression object
Plot cost-effectiveness acceptability curve
Plot cost-effectiveness acceptability frontier
Plot cost-effectiveness plane
Plot expected value of perfect information
Code to use the hesim package inline. Not directly called by the user.
N-state partitioned survival model
Partitioned survival curves
Quality-adjusted life-years object
Transition intensity matrix from tabular object
Transition intensity matrix from msm object
Transition intensity matrix
Random generation for categorical distribution
Random generation for multiple Dirichlet distributions
Objects exported from other packages
Random number generation distributions
Random generation for piecewise exponential distribution
Set value labels
Expected values from state probabilities
Simulated state probabilities
Simulate state probabilities from survival curves
State probability object
Table to store state value parameters
Model for state values
Summarize costs and effectiveness
Summary method for cost-effectiveness object
Summarize eval_rng object
Summarize parameter objects
Summarize tparams_mean object
Summarize tparams_transprobs object
Summarize transition probability matrix
Survival quantiles
Survival object
Time intervals
Predicted means
Transition probabilities
Transformed parameter object
Transition probability matrix IDs
Names for elements of a transition probability matrix
Transition probability matrix
Generate variates for univariate distributions
Parameterization of the Weibull distribution for network meta-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.
Useful links