cemrun: Run the Markov model with input generated from 'cemtool()',...

In StanWijn/cemtool: Step By Step Guide to Build a Markov Model

Description

The cemtool() package provides a step-by-step tool to guide users in building a default Markov model. The tool guides the user though the steps of the development of a Markov model and will present the final result using graphs and tables. The only prerequisite is that the user knows the structure of the model and the transition probabilities, no calculations or coding is required.

Usage

cemrun(HS = cemtool.env$HS, HS1 = cemtool.env$HS1,
  HS2 = cemtool.env$HS2, HS3 = cemtool.env$HS3,
  HS4 = cemtool.env$HS4, HS5 = cemtool.env$HS5,
  dead = cemtool.env$dead, n.t = cemtool.env$n.t,
  control = cemtool.env$control,
  intervention = cemtool.env$intervention, d.rc = cemtool.env$d.rc,
  d.re = cemtool.env$d.rc, m.M = cemtool.env$m.M,
  m.M_treatment = cemtool.env$m.M_treatment, m.P = cemtool.env$m.P,
  m.P_treatment = cemtool.env$m.P_treatment,
  modelinput = cemtool.env$modelinput)

Arguments

HS	Number of healthstates
HS1	String with name of healthstate 1
HS2	String with name of healthstate 2
HS3	String with name of healthstate 3
HS4	String with name of healthstate 4
HS5	String with name of healthstate 5
dead	String with name of absorption / death state
n.t	Number of cycles
control	String with name of the usual care strategy
intervention	String with name of the intervention strategy
d.rc	Discount rate for costs
d.re	Discount rate for effects
m.M	Matrix showing the Markov trace of usual care, nrow = n.t + 1, ncol = HS
m.M_treatment	Matrix showing the Markov trace of intervention strategy, nrow = n.t + 1, ncol = HS
m.P	Matrix showing the transition probability matrix of the usual care, nrow = HS, ncol = HS
m.P_treatment	Matrix showing the transition probability matrix of the intervention strategy, nrow = HS, ncol = HS
modelinput	Matrix with 2 rows that include all the transition probabilities, costs and effects.

Details

All input arguments can be generated and saved with the cemtool() function. (cemtool.env <- cemtool()) There are multiple software systems that can be used to build Markov models for cost-effectiveness analyses like TreeAge, Excel or R. Although there are numerous advantages to use R over the others, the biggest downside is the steep learning curve from R. The cemtool() package aims to close this gap by introducing a step-by-step tool to guide users in building a default Markov models. The tool guides the user though the steps of the development of a Markov model and will present the final result using graphs and tables. The only prerequisite is that the user knows the structure of the model and the transition probabilities, no calculations or coding is required.

Value

The following variables will be created in the Global Enviroment:

— Full Markov trace (m.M and m.M_treatment)

— Calculate the costs and effects for both strategies.

— Results are saved (table_output) and shown in the console.

— The model structure and markov trace are both plotted and saved (plot1 and plot2)

Author(s)

S.R.W. Wijn MSc <[e-mail]>

Examples

## Not run: 
cemtool() # Start from stratch (clear the cemtool environment from the Global Environment)
cemprob() # Start from the second phase (definding the parameters) 
cemtpm()  # Start from the third phase (modify the transition probability matrix)
cemrun()  # Run the model with the current m.M markov trace and m.P transition probability matrix

cemtool.env <- cemtool() # To save all input for further modification

## End(Not run)

StanWijn/cemtool documentation built on April 8, 2020, 1:42 p.m.