yls {mSimCC} | R Documentation |
Aggregates data from a microsimulated cohort.
yls(scenario1, scenario2, disc = FALSE)
scenario1 |
microsimulated cohort. |
scenario2 |
microsimulated cohort. |
disc |
discount rate to be applied. Defaults to |
Years of life saved due to strategy scenario1
compared to scenario2
.
David Moriña, Pedro Puig and Mireia Diaz
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
, costs
, le
,
plotCIN1Incidence
, plotCIN2Incidence
, plotCIN3Incidence
,
plotIncidence
, plotMortality
, plotPrevalence
,
qalys
, bCohort
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) vacc12 <- 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, vacc=TRUE, vacc.age=12, vacc.prop=1, ndoses=3, vacc.cov=0.828, vacc.eff=1, vacc.type="biv", vaccprice.md=33.6, vaccprice.nmd=0, vaccprice.i=0, treatProbs=c(0,0,1,1,1,0.9894,0.9422,0.8262,0.5507,0,0,0), nCores=1) yls(hn, vacc12)