Package 'BCEAweb'

BCEAweb

Description

Launches the web-app.

Usage

BCEAweb(e = NULL, c = NULL, parameters = NULL, ...)

Arguments

e	A matrix containing the simulations for the effectiveness variable (with number of simulation rows and number of interventions columns). Defaults at NULL, which means the user has to load their own values using the web-interface
c	A matrix containing the simulations for the cost variable (with number of simulation rows and number of interventions columns). Defaults at NULL, which means the user has to load their own values using the web-interface
parameters	A matrix with the simulations for all the relevant model parameters. Defaults at NULL, which means the user has to load their own values using the web-interface. Columns must be named
...	Additional parameters passed to shiny::runApp

Author(s)

Gianluca Baio

References

Baio, G., Dawid, A. P. (2011). Probabilistic Sensitivity Analysis in Health Economics. Statistical Methods in Medical Research doi:10.1177/0962280211419832.

Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London.

See Also

bcea

Examples

## Not run: 
data(Vaccine)
BCEAweb(eff, cost, vaccine_mat)

data(Smoking)
colnames(pi_post) = paste0("p",1:ncol(pi_post))
BCEAweb(eff, cost, pi_post)

## End(Not run)

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Internal launch function

Description

Internal launch function

Usage

launch(e, c, parameters, ...)

Arguments

e	effects
c	costs
parameters	input parameters
...	additional arguments passed to shiny::runApp

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Data set for the Bayesian model for the cost-effectiveness of smoking cessation interventions

Description

This data set contains the results of the Bayesian analysis used to model the clinical output and the costs associated with the health economic evaluation of four different smoking cessation interventions.

Format

The Smoking example is included using 3 object:

Smoking.RData

A data list including the variables needed for the smoking cessation cost-effectiveness analysis. The variables are as follows:

• costa matrix of 500 simulations from the posterior distribution of the overall costs associated with the four strategies

• dataa dataset containing the characteristics of the smokers in the UK population

• effa matrix of 500 simulations from the posterior distribution of the clinical benefits associated with the four strategies

• life.yearsa matrix of 500 simulations from the posterior distribution of the life years gained with each strategy

• pi_posta matrix of 500 simulations from the posterior distribution of the event of smoking cessation with each strategy

• smokinga data frame containing the inputs needed for the network meta-analysis model. The data.frame object contains: nobs: the record ID number, s: the study ID number, i: the intervention ID number, r_i: the number of patients who quit smoking, n_i: the total number of patients for the row-specific arm and b_i: the reference intervention for each study

• smoking_mata matrix obtained by running the network meta-analysis model based on the data contained in the smoking object

• treatsa vector of labels associated with the four strategies

smoking_parameters.csv

A csv file including the (named) pi_post variable describing above in a format that can be to the web-app using the Spreadsheet option

smoking_results.csv

A csv file including the (named) effectiveness and costs results in a format that can be uploaded to the web-app using the Spreadsheet option

Source

Effectiveness data adapted from Hasselblad V. (1998). Meta-analysis of Multitreatment Studies. Medical Decision Making 1998;18:37-43. Cost and population characteristics data adapted from various sources:

• Taylor, D.H. Jr, et al. (2002). Benefits of smoking cessation on longevity. American Journal of Public Health 2002;92(6)

• ASH: Action on Smoking and Health (2013). ASH fact sheet on smoking statistics,
  ⁠https://ash.org.uk/files/documents/ASH_106.pdf⁠

• Flack, S., et al. (2007). Cost-effectiveness of interventions for smoking cessation. York Health Economics Consortium, January 2007

• McGhan, W.F.D., and Smith, M. (1996). Pharmacoeconomic analysis of smoking-cessation interventions. American Journal of Health-System Pharmacy 1996;53:45-52

References

Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London

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Data set for the Bayesian model for the cost-effectiveness of influenza vaccination

Description

This data set contains the results of the Bayesian analysis used to model the clinical output and the costs associated with an influenza vaccination.

Format

The influenza vaccination example is included using 3 objects:

Vaccine.Rdata

A data list including the variables needed for the influenza vaccination. The variables are as follows:

cost

a matrix of simulations from the posterior distribution of the overall costs associated with the two treatments

c.pts

cost.GP

a matrix of simulations from the posterior distribution of the costs for GP visits associated with the two treatments

cost.hosp

a matrix of simulations from the posterior distribution of the costs for hospitalisations associated with the two treatments

cost.otc

a matrix of simulations from the posterior distribution of the costs for over-the-counter medications associated with the two treatments

cost.time.off

a matrix of simulations from the posterior distribution of the costs for time off work associated with the two treatments

cost.time.vac

a matrix of simulations from the posterior distribution of the costs for time needed to get the vaccination associated with the two treatments

cost.travel

a matrix of simulations from the posterior distribution of the costs for travel to get vaccination associated with the two treatments

cost.trt1

a matrix of simulations from the posterior distribution of the overall costs for first line of treatment associated with the two interventions

cost.trt2

a matrix of simulations from the posterior distribution of the overall costs for second line of treatment associated with the two interventions

cost.vac

a matrix of simulations from the posterior distribution of the costs for vaccination

eff

a matrix of simulations from the posterior distribution of the clinical benefits associated with the two treatments

e.pts

N

the number of subjects in the reference population

N.outcomes

the number of clinical outcomes analysed

N.resources

the number of health-care resources under study

QALYs.adv

a vector from the posterior distribution of the QALYs associated with advert events

QALYs.death

a vector from the posterior distribution of the QALYs associated with death

QALYs.hosp

a vector from the posterior distribution of the QALYs associated with hospitalisation

QALYs.inf

a vector from the posterior distribution of the QALYs associated with influenza infection

QALYs.pne

a vector from the posterior distribution of the QALYs associated with pneumonia

treats

a vector of labels associated with the two treatments

vaccine_mat

a matrix containing the simulations for the parameters used in the original model

vaccine_parameters.csv

A csv file including the (named) input parameters for the vaccination example in a format that can be to the web-app using the Spreadsheet option

vaccine_results.csv

A csv file including the (named) effectiveness and costs results in a format that can be uploaded to the web-app using the Spreadsheet option

Source

Adapted from Turner D, Wailoo A, Cooper N, Sutton A, Abrams K, Nicholson K. The cost-effectiveness of influenza vaccination of healthy adults 50-64 years of age. Vaccine. 2006;24:1035-1043.

References

Baio, G., Dawid, A. P. (2011). Probabilistic Sensitivity Analysis in Health Economics. Statistical Methods in Medical Research doi:10.1177/0962280211419832.

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