Help for package mSimCC

Type:

Package

Title:

Micro Simulation Model for Cervical Cancer Prevention

Version:

0.0.3

Date:

2023-08-21

Author:

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

Maintainer:

David Moriña Soler <dmorina@ub.edu>

Description:

Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Encoding:

UTF-8

LazyData:

true

Depends:

R (≥ 3.1.1), parallel, doParallel, foreach

NeedsCompilation:

yes

Packaged:

2023-08-21 07:37:58 UTC; dmorina

Repository:

CRAN

Date/Publication:

2023-08-22 12:10:08 UTC

Microsimulation model for cervical cancer prevention

Description

Microsimulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies.

Details

Package:	mSimCC
Type:	Package
Version:	0.0.3
Date:	2023-08-21
License:	GPL version 2 or newer
LazyLoad:	yes

Author(s)

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

Mantainer: David Moriña Soler <dmorina@ub.edu>

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.

Aggregate data from several microsimulated cohorts

Description

This function aggregates data from several microsimulated cohorts.

Usage

  bCohort(ind)

Arguments

ind

microsimulated cohort obtained using microsim.

Value

Data frame with health states as columns and ages as rows.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level

hn_c     <- bCohort(hn) ### Aggregated level

Calculate the costs of a prevention strategy.

Description

Calculate the costs of a prevention strategy.

Usage

  costs(scenario, disc=FALSE)

Arguments

scenario

microsimulated cohort.

disc

discount rate to be applied. Defaults to FALSE (undiscounted).

Value

Global and per-person costs of the considered prevention strategy.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level

costs(hn)

Calculates life expectancy for a prevention strategy

Description

Aggregates data from a microsimulated cohort.

Usage

  le(scenario, disc=FALSE)

Arguments

scenario

microsimulated cohort.

disc

discount rate to be applied. Defaults to FALSE (undiscounted).

Value

Global and per-person life expectancy of the considered prevention strategy.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level

le(hn) ### Aggregated level

Internal mSimCC functions

Description

Internal mSimCC functions

Usage

byAgeGroup2(data)
byAgeGroup3(data)

Details

These functions are not to be called by the user

Generate microsimulated cohorts

Description

Generates several microsimulated cohorts with desired specifications.

Usage

  microsim(seed=1234, nsim, transition, abs_states, sympt_states, prob_sympt, size, 
           p_men, min_age, max_age, utilityCoefs, costCoefs.md, costCoefs.nmd, 
           costCoefs.i, disc=3, vacc=FALSE, vacc.age=NULL, ndoses=NULL, vacc.cov=NULL, 
           vacc.eff=NULL, vacc.type=NULL, vacc.prop=NULL, vaccprice.md=NULL, 
           vaccprice.nmd=NULL, vaccprice.i=NULL, screening=FALSE, screenType=0, 
           scrSchema=0, screenPeriod=NULL, cytoType=NULL, screenPrice.md=NULL, 
           screenPrice.nmd=NULL, screenPrice.i=NULL, colpoPrice.md=NULL, 
           colpoPrice.nmd=NULL, colpoPrice.i=NULL, hpvTestPrice.md=NULL, 
           hpvTestPrice.nmd=NULL, hpvTestPrice.i=NULL, cytoHpvPrice.md=NULL, 
           cytoHpvPrice.nmd=NULL, cytoHpvPrice.i=NULL, biopsPrice.md=NULL, 
           biopsPrice.nmd=NULL, biopsPrice.i=NULL, screenCoverage=NULL, screenSensi=NULL,
           screenSensi2=NULL, screenSensi3=NULL, colpoSensi=NULL, biopSensi=NULL, 
           hpvTestSensi=NULL, treatProbs, nAnnualVisits=0, nAnnualVisitsLSIL=0, 
           nAnnualVisitsHSIL=0, cytoHPVPeriod=0, cytoHPVPostColpo=0, 
           cytoHPVPostBiop=NULL, cytoLSILperiod=0, cytoHSILperiod=0, switchAge=0, 
           C_period=NULL, hpvPeriod=0, nCores=1)

Arguments

seed

seed to be used in the simulation. Default value is 1234.

nsim

number of cohorts to be simulated.

transition

transition probabilities matrix.

abs_states

vector with the absorbing states.

sympt_states

vector with the health states that might present symptoms.

prob_sympt

vector with the probability of presenting symptoms for each health state that might present symptoms. Should have the same length of sympt_states.

size

number of individuals on each simulated cohort.

p_men

proportion of men in the simulated cohorts.

min_age

lowest age in the cohort.

max_age

largest age in the cohort.

utilityCoefs

vector with the utilities for each health state.

costCoefs.md

vector with the direct medical costs for each health state.

costCoefs.nmd

vector with the direct non medical costs for each health state.

costCoefs.i

vector with the indirect costs for each health state.

disc

discount rate in percentage. Default value is 3.

vacc

boolean value specifying if the considered scenario includes vaccination. Default value is FALSE.

vacc.age

vector with ages at vaccination if the considered scenario includes vaccination. Default value is NULL.

ndoses

number of doses of vaccine if the considered scenario includes vaccination. Default value is NULL.

vacc.cov

vaccine coverage if the considered scenario includes vaccination. Default value is NULL.

vacc.eff

vaccine effectivity if the considered scenario includes vaccination. Default value is NULL.

vacc.type

type of vaccine if the considered scenario includes vaccination, character with values biv for bivalent, quad for quadrivalent and nona for nonavalent vaccines. Default value is NULL.

vacc.prop

proportion of vaccinated women on each age group if the considered scenario includes vaccination. Default value is NULL.

vaccprice.md

vaccine direct medical costs if the considered scenario includes vaccination. Default value is NULL.

vaccprice.nmd

vaccine direct non medical costs if the considered scenario includes vaccination. Default value is NULL.

vaccprice.i

vaccine indirect if the considered scenario includes vaccination. Default value is NULL.

screening

boolean specifying if the considered scenario includes screening of any type. Default value is FALSE.

screenType

type of screening. 1 stands for organized screening, 2 stands for opportunistic screening. Default value is 0 (no screening).

scrSchema

screening schema. 1 stands for cytology alone with repeat cytology for triage, 2 stands for cytology with HPV triage, 3 stands for HPV with cytology triage and 4 stands for HPV genotyping with cytology triage. Default value is 0 (no screening).

screenPeriod

screening period (in years). Default value is NULL (no screening).

cytoType

type of cytology. 0 stands for conventional cytology, 1 stands for Liquid Based Cytology (LBC). Default value is NULL (no cytology).

screenPrice.md

medical direct cost of cytology. Default value is NULL.

screenPrice.nmd

non-medical direct cost of cytology. Default value is NULL.

screenPrice.i

indirect cost of cytology. Default value is NULL.

colpoPrice.md

medical direct cost of colposcopy. Default value is NULL.

colpoPrice.nmd

non-medical direct cost of colposcopy. Default value is NULL.

colpoPrice.i

indirect cost of colposcopy. Default value is NULL.

hpvTestPrice.md

medical direct cost of HPV test. Default value is NULL.

hpvTestPrice.nmd

non-medical direct cost of HPV test. Default value is NULL.

hpvTestPrice.i

indirect cost of HPV test. Default value is NULL.

cytoHpvPrice.md

medical direct cost of HPV reflex test, in case cytoType=1. Default value is NULL.

cytoHpvPrice.nmd

non-medical direct cost of HPV reflex test, in case cytoType=1. Default value is NULL.

cytoHpvPrice.i

indirect cost of HPV reflex test, in case cytoType=1. Default value is NULL.

biopsPrice.md

medical direct cost of biopsy. Default value is NULL.

biopsPrice.nmd

non-medical direct cost of biopsy. Default value is NULL.

biopsPrice.i

indirect cost of biopsy. Default value is NULL.

screenCoverage

cytology coverage for each age group. Default value is NULL.

screenSensi

cytology sensitivity for each age group. Default value is NULL.

screenSensi2

cytology sensitivity after cytology for each age group. Default value is NULL.

screenSensi3

cytology sensitivity after HPV test for each age group. Default value is NULL.

colpoSensi

colposcopy sensitivity for each age group. Default value is NULL.

biopSensi

biopsy sensitivity for each age group. Default value is NULL.

hpvTestSensi

HPV test sensitivity for each age group. Default value is NULL.

treatProbs

probability of recuperation after treatment for each FIGO I - FIGO IV states.

nAnnualVisits

number of annual visits after colposcopy for screening schema 1. Default value is 0.

nAnnualVisitsLSIL

number of annual visits after LSIL for screening schema 2. Default value is 0.

nAnnualVisitsHSIL

number of annual visits after HSIL for screening schema 2. Default value is 0.

cytoHPVPeriod

cytology and HPV test protocol period for screening schemas 3 and 4. Default value is 0.

cytoHPVPostColpo

cytology and HPV test protocol period after colposcopy protocol for screening schemas 3 and 4. Default value is 0.

cytoHPVPostBiop

cytology and HPV test protocol period after biopsy protocol for screening schemas 2. Default value is NULL.

cytoLSILperiod

period for cytology after LSIL detection for screening schame 2. Default value is 0.

cytoHSILperiod

period for cytology after HSIL detection for screening schame 2. Default value is 0.

switchAge

age at which screening protocol changes for screening schemas 3 and 4. Default value is 0.

C_period

vector with screening periods (in years) before and after switch age for screening schemas 3 and 4. Default value is NULL.

hpvPeriod

period for HPV test in screening schema 2. Default value is 0.

nCores

number of cores of the computer. Default value is 1.

Value

Data frame containing the simulated cohorts and the individual history for each person in each simulated cohort.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level

Calculates and plots the CIN1 incidence.

Description

Calculates and plots the CIN1 incidence for one or several prevention strategies.

Usage

  plotCIN1Incidence(..., current=NULL, labels=NULL)

Arguments

...

one or several microsimulated cohort corresponding to one or several microsimulated cohorts.

current

real CIN 1 incidence in the population of interest.

labels

labels to be used in the plot.

Value

Returns a list with CIN 1 incidence for each age group.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level
hn_c <- bCohort(hn)
plotCIN1Incidence(hn_c) ### Aggregated level

Calculates and plots the CIN2 incidence.

Description

Calculates and plots the CIN2 incidence for one or several prevention strategies.

Usage

  plotCIN2Incidence(..., current=NULL, labels=NULL)

Arguments

...

one or several microsimulated cohort corresponding to one or several microsimulated cohorts.

current

real CIN 2 incidence in the population of interest.

labels

labels to be used in the plot.

Value

Returns a list with CIN 2 incidence for each age group.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level
hn_c <- bCohort(hn)
plotCIN2Incidence(hn_c) ### Aggregated level

Calculates and plots the CIN3 incidence.

Description

Calculates and plots the CIN3 incidence for one or several prevention strategies.

Usage

  plotCIN3Incidence(..., current=NULL, labels=NULL)

Arguments

...

one or several microsimulated cohort corresponding to one or several microsimulated cohorts.

current

real CIN 3 incidence in the population of interest.

labels

labels to be used in the plot.

Value

Returns a list with CIN 3 incidence for each age group.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level
hn_c <- bCohort(hn)
plotCIN3Incidence(hn_c) ### Aggregated level

Calculates and plots the cervical cancer incidence.

Description

Calculates and plots the cervical cancer incidence for one or several prevention strategies.

Usage

  plotIncidence(..., current=NULL, labels=NULL)

Arguments

...

one or several microsimulated cohort corresponding to one or several microsimulated cohorts.

current

real cervical cancer incidence in the population of interest.

labels

labels to be used in the plot.

Value

Returns a list with cervical cancer incidence for each age group.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level
hn_c <- bCohort(hn)
plotIncidence(hn_c) ### Aggregated level

Calculates and plots the cervical cancer mortality.

Description

Calculates and plots the cervical cancer mortality for one or several prevention strategies.

Usage

  plotMortality(..., current=NULL, labels=NULL)

Arguments

...

one or several microsimulated cohort corresponding to one or several microsimulated cohorts.

current

real cervical cancer mortality in the population of interest.

labels

labels to be used in the plot.

Value

Returns a list with cervical cancer mortality for each age group.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level
hn_c <- bCohort(hn)
plotMortality(hn_c) ### Aggregated level

Calculates and plots the HPV prevalence.

Description

Calculates and plots the HPV prevalence for one or several prevention strategies.

Usage

  plotPrevalence(..., current=NULL, labels=NULL)

Arguments

...

one or several microsimulated cohort corresponding to one or several microsimulated cohorts.

current

real HPV prevalence in the population of interest.

labels

labels to be used in the plot.

Value

Returns a list with HPV prevalence for each age group.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level
hn_c <- bCohort(hn)
plotPrevalence(hn_c) ### Aggregated level

Transition probabilities matrix for Spanish population

Description

This data corresponds to a transition probabilities matrix calibrated for the Spanish population.

Usage

probs

Format

A data frame with 180 rows and 13 columns.

Aggregate data from a microsimulated cohort

Description

Aggregates data from a microsimulated cohort.

Usage

  qalys(scenario, disc=FALSE)

Arguments

scenario

microsimulated cohort.

disc

discount rate to be applied. Defaults to FALSE (undiscounted).

Value

Global and per-person QALYs of the considered prevention strategy.

Author(s)

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

References

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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) ### individual level

qalys(hn)

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

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

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)

Microsimulation model for cervical cancer prevention

Description

Details

Author(s)

References

See Also

Aggregate data from several microsimulated cohorts

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Calculate the costs of a prevention strategy.

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Calculates life expectancy for a prevention strategy

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Internal mSimCC functions

Description

Usage

Details

See Also

Generate microsimulated cohorts

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Calculates and plots the CIN1 incidence.

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Calculates and plots the CIN2 incidence.

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Calculates and plots the CIN3 incidence.

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Calculates and plots the cervical cancer incidence.

Description

Usage

Arguments

Value

Author(s)