yls: Aggregate data from a microsimulated cohort

In mSimCC: Micro Simulation Model for Cervical Cancer Prevention

Aggregate data from a microsimulated cohort

Description

Aggregates data from a microsimulated cohort.

Usage

  yls(scenario1, scenario2, disc = FALSE)

Arguments

scenario1	microsimulated cohort.
scenario2	microsimulated cohort.
disc	discount rate to be applied. Defaults to FALSE (undiscounted).

Value

Years of life saved due to strategy scenario1 compared to scenario2.

Author(s)

David Moriña (Universitat de Barcelona), Pedro Puig (Universitat Autònoma de Barcelona) and Mireia Diaz (Institut Català d'Oncologia)

References

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.

See Also

mSimCC-package, microsim, costs, le, plotCIN1Incidence, plotCIN2Incidence, plotCIN3Incidence, plotIncidence, plotMortality, plotPrevalence, qalys, bCohort

Examples

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)

mSimCC documentation built on Aug. 22, 2023, 5:07 p.m.