costs | R Documentation |
Calculate the costs of a prevention strategy.
costs(scenario, disc=FALSE)
scenario |
microsimulated cohort. |
disc |
discount rate to be applied. Defaults to |
Global and per-person costs of the considered prevention strategy.
David Moriña (Universitat de Barcelona), Pedro Puig (Universitat Autònoma de Barcelona) and Mireia Diaz (Institut Català d'Oncologia)
Georgalis L, de Sanjosé S, Esnaola M, Bosch F X, Diaz M. Present and future of cervical cancer prevention in Spain: a cost-effectiveness analysis. European Journal of Cancer Prevention 2016;25(5):430-439.
Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.
mSimCC-package
, microsim
, bCohort
, le
,
plotCIN1Incidence
, plotCIN2Incidence
, plotCIN3Incidence
,
plotIncidence
, plotMortality
, plotPrevalence
,
qalys
, yls
data(probs)
nsim <- 3
p.men <- 0
size <- 20
min.age <- 10
max.age <- 84
#### Natural history
hn <- microsim(seed=1234, nsim, probs, abs_states=c(10, 11), sympt_states=c(5, 6, 7, 8),
prob_sympt=c(0.11, 0.23, 0.66, 0.9),
size, p.men, min.age, max.age,
utilityCoefs = c(1, 1, 0.987, 0.87, 0.87, 0.76, 0.67, 0.67, 0.67, 0.938, 0, 0),
costCoefs.md = c(0, 0, 254.1, 1495.9, 1495.9, 5546.8, 12426.4, 23123.4,
34016.6, 0, 0, 0),
costCoefs.nmd = c(0, 0, 81.4, 194.1, 194.1, 219.1, 219.1, 219.1, 219.1, 0, 0, 0),
costCoefs.i = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), disc=3,
treatProbs=c(0,0,1,1,1,0.9894,0.9422,0.8262,0.5507,0,0,0),
nCores=1) ### individual level
costs(hn)
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