Calculate cost-effectiveness probabilities for two strategies.

Description

This function calculates the probabilities that each strategy is the cost effective at different willingness to pay thresholds.

Usage

calculate_ceac(
  df,
  e_int,
  e_comp,
  c_int,
  c_comp,
  v_wtp = seq(from = 0, to = 1e+05, by = 1000)
)

Arguments

Value

A dataframe with three columns:

• WTP_threshold = The willingness-to-pay thresholds at which the probability of cost effectiveness has been calculated for both strategies

• Prob_int = The probability that the intervention strategy is cost effective at a given willingness-to-pay threshold

• Prob_comp = The probability that the comparator strategy is cost effective at a given willingness-to-pay threshold

Examples

# Calculate probabilities of cost effectiveness using the example dataframe,
# for willlingness-to-pay thresholds of 0 to 50,0000 euros.
data("df_pa")
calculate_ceac(df = df_pa,
               e_int = "t_qaly_d_int",
               e_comp = "t_qaly_d_comp",
               c_int = "t_costs_d_int",
               c_comp = "t_costs_d_comp",
               v_wtp = seq(from = 0, to = 50000, by = 1000))

Calculate cost-effectiveness probabilities.

Description

This function calculates the probabilities that each strategy is the cost effective at different willingness to pay thresholds, for an infinite amount of strategies.

Usage

calculate_ceac_mult(
  df,
  outcomes,
  costs,
  v_wtp = seq(from = 0, to = 1e+05, by = 1000)
)

Arguments

Value

A dataframe with three columns:

• WTP_threshold = The willingness-to-pay thresholds at which the probability of cost effectiveness has been calculated for both strategies

• Prob_int = The probability that the intervention strategy is cost effective at a given willingness-to-pay threshold

• Prob_comp = The probability that the comparator strategy is cost effective at a given willingness-to-pay threshold

Examples

# Calculate probabilities of cost effectiveness using the example dataframe,
data("df_pa")
df_pa$t_qaly_d_int2 <- df_pa$t_qaly_d_int * 1.5 # creating additional outcome variable
df_pa$t_costs_d_int2 <- df_pa$t_costs_d_int * 1.5 # creating additional cost variable
calculate_ceac_mult(df = df_pa,
             outcomes = c("t_qaly_d_int", "t_qaly_d_comp", "t_qaly_d_int2"),
             costs = c("t_costs_d_int","t_costs_d_comp", "t_costs_d_int2")
             )

Calculate NMB and NHB for two strategies.

Description

This function calculates the Net Monetary Benefits (NMB) and Net Health Benefits (NHB) for each strategy and the incremental NMB and NHB.

Usage

calculate_nb(df, e_int, e_comp, c_int, c_comp, wtp)

Arguments

Value

A dataframe containing the original data and the following variables:

• NMB_int = Net monetary benefit of the intervention

• NMB_comp = Net monetary benefit of the comparator

• iNMB = Incremental net monetary benefit of the intervention versus the comparator

• NHB_int = Net health benefit of the intervention

• NHB_comp = Net health benefit of the comparator

• iNHB = Net health benefit of the intervention versus the comparator

Examples

# Calculate NB's at a willingness-to-pay threshold of 80000 per unit of effects
data("df_pa")
calculate_nb(df = df_pa,
             e_int = "t_qaly_d_int",
             e_comp = "t_qaly_d_comp",
             c_int = "t_costs_d_int",
             c_comp = "t_costs_d_comp",
             wtp = 80000)

Calculate NMB and NHB.

Description

This function calculates the Net Monetary Benefits (NMB) and Net Health Benefits (NHB) for each strategy and the incremental NMB and NHB.

Usage

calculate_nb_mult(df, outcomes, costs, wtp)

Arguments

Value

A dataframe containing the original data and the following variables containing the NMB and NHB for each strategy. The name of these new variables are structured as 'NMB_strategyname' and 'NHB_strategyname'

Examples

# Calculate NB's at a willingness-to-pay threshold of 80000 per unit of effects
data("df_pa")
df_pa$t_costs_d_comp2 <- df_pa$t_costs_d_comp * 1.09
df_pa$t_qaly_d_comp2 <- df_pa$t_qaly_d_comp * 1.01
calculate_nb_mult(df = df_pa,
                  outcomes = c("t_qaly_d_comp2", "t_qaly_d_int", "t_qaly_d_comp"),
                  costs = c("t_costs_d_int", "t_costs_d_comp2", "t_costs_d_comp"),
                  wtp = 50000
                  )

Check binary

Description

This function tests whether the value of variables remain between 0 and 1 (for instance for utility and probability inputs)

Usage

check_binary(..., df, max_view = 50)

Arguments

Value

A dataframe.

Examples

# Checking whether a variable is strictly positive
data(df_pa)
check_binary("u_pfs", df = df_pa)
# Checking whether two variables are strictly positive
# Decreasing the number of iterations to display to 20.
check_binary("u_pfs", "p_pfspd", df = df_pa)

Check mean quality of life

Description

This function checks whether the mean quality of life outcome of each iteration remain between the maximum and minimum utility values of the specific iteration.

Usage

check_mean_qol(df, t_qaly, t_ly, u_values, max_view = 100)

Arguments

Value

A matrix.

Examples

# Check whether mean quality of life is within min-max utility values
check_mean_qol(df = df_pa,
               t_ly = "t_ly_comp",
               t_qaly = "t_qaly_comp",
               u_values = c("u_pfs", "u_pd")
               )

Check whether variables are strictly positive

Description

This function tests whether variables are strictly positive (for instance for costs and relative risks inputs)

Usage

check_positive(..., df, max_view = 50)

Arguments

Value

A dataframe.

Examples

# Checking whether a variable is strictly positive
check_positive("c_pfs", df = df_pa)

# Checking whether two variables are strictly positive
# Descreasing the number of iterations to display to 20.
check_positive("c_pfs", "c_pd", df = df_pa)

Check PSA inputs & outputs

Description

This function checks whether the value of variables remain between 0 and 1 for utility and probability inputs, and are strictly positive for costs, hazard ratios, odds ratios, relative risks, and total outcomes of each strategy.

Usage

check_psa_darth(
  l_psa_darth,
  utility = "u_",
  costs = "c_",
  probs = "p_",
  rr = "rr_",
  hr = "hr_",
  or = "or_",
  exclude = NULL,
  v_outcome = c("effectiveness", "cost")
)

Arguments

Value

A matrix containing the input and output variables that have been checked and the iterations wherein an erroneous value has been identified.

Check range

Description

This function tests whether an input or output value falls within a user-defined range and return the proportion of iteration in which this is not the case.

Usage

check_range(df, param, min_val = NULL, max_val = NULL)

Arguments

Details

If only 'min_val' is specified, the proportion of iteration above this value will be computed. If only 'max_val' is specified, the proportion of iteration below this value will be computed.

Value

A numeric.

Examples

# Checking how often the "u_pfs" values falls within 0.55 and 0.72.
data(df_pa)
check_range(df = df_pa,
            param = "u_pfs",
            min_val = 0.55,
            max_val = 0.72
                 )

Check sum probabilities

Description

This function checks whether the sum of user-defined variables representing probabilities is below or equal to 1 for each iteration of the probabilistic inputs.

Usage

check_sum_probs(..., df, digits = NULL, check = "lower", max_view = 100)

Arguments

Value

A text indicating whether the sum of the probabilities is belor and/or eual to one or indicating in which iteration that is not the case.

Examples

# Checking whether the sum of the two probabilities is lower than or equal to 1
check_sum_probs("p_pfspd", "p_pfsd", df = df_pa, check = "lower")

# Checking the sum of the two probabilities equals 1 using a vector to select them,
# Rounding off to two digits, and extending the number of iterations to display to 250.
check_sum_probs(c("p_pfspd", "p_pfsd"), df = df_pa, digits = 2, check = "equal", max_view = 250)

Check sum variables

Description

This function tests whether the sum of selected variables are equal to another.

Usage

check_sum_vars(..., df, outcome, digits = 3)

Arguments

Value

A string.

Examples

# Checking whether health state and adverse event costs equal the total discounted costs
check_sum_vars("t_costs_pfs_d_int", "t_costs_pd_d_int", "t_costs_ae_int",
               df = head(df_pa),
               outcome = "t_costs_d_int",
               digits = 0)

Check parametric survival models

Description

This function tests whether the first of two parametric survival model is lower than a second parametric survival model.

Usage

check_surv_mod(
  df,
  surv_mod_1,
  surv_mod_2,
  v_names_param_mod_1,
  v_names_param_mod_2,
  time = seq(0, 5, 0.1),
  label_surv_1 = "first survival",
  label_surv_2 = "second survival",
  n_view = 10
)

Arguments

Details

The parametric models that can be used are the following: exponential (exp), Weibull (weibull), gamma (gamma), loglogistic (logis), and lognormal (lnorm). All these functions are implemented following their distribution function as documented in the stats package.

Value

A list. The first element is a message, the second element contains the number of the iterations in which the the first curve is higher than the second curve.

A dataframe containing probabilistic inputs for testing

Description

A dataframe containing 1,000 sets of probabilistic inputs from a health state transition model developed in Python.

Usage

df_ckd_inputs

Format

The dataframe contains the 1,000 probabilistic inputs (indexes 1 to 1000) from the PSA.xlsx file available on the github repository of the model.

Source

Marika M. Cusick, Rebecca L. Tisdale, Glenn M. Chertow, et al. Population-Wide Screening for Chronic Kidney Disease: A Cost-Effectiveness Analysis. Ann Intern Med.2023;176:788-797. [Epub 23 May 2023]. doi:10.7326/M22-3228.

Link to Github repository: https://github.com/marikamaecusick/CKDScreeningCEA

A dataframe containing probabilistic outputs for testing

Description

A dataframe containing 1,000 sets of probabilistic outputs from a health state transition model developed in Python for individuals aged 35, screened every 10 years, versus no screening.

Usage

df_ckd_results

Format

The dataframe contains the 1,000 probabilistic outputs from the CKD Screening CEA health economic model.

Source

Marika M. Cusick, Rebecca L. Tisdale, Glenn M. Chertow, et al. Population-Wide Screening for Chronic Kidney Disease: A Cost-Effectiveness Analysis. Ann Intern Med.2023;176:788-797. [Epub 23 May 2023]. doi:10.7326/M22-3228.

Link to Github repository: https://github.com/marikamaecusick/CKDScreeningCEA

Dataframe of inputs and outputs of a health economic model developed and evaluated with the iviRA R package for testing

Description

A dataframe containing 1,000 sets of probabilistic inputs and outputs of a individual-level health state transition model developed using iviRA package available on Github.

Usage

df_iviRA_pa

Format

See the documentation of the 'sample_pars' function of the iviRA package for details.

Source

Incerti D, Curtis JR, Shafrin J, Lakdawalla DN, Jansen JP (2019). “ A Flexible Open-Source Decision Model for Value Assessment of Biologic Treatment for Rheumatoid Arthritis.” PharmacoEconomics. doi: 10.1007/s40273-018-00765-2.

Link to Github repository: https://innovationvalueinitiative.github.io/IVI-RA/index.html

Link to iviRA website: https://innovationvalueinitiative.github.io/IVI-RA/

Dataframe for testing

Description

A dataframe containing 1,000 iterations of a probabilistic analysis of a health state transition model. To access the original dataframe used in the scientific publication of PACBOARD (Pouwels et al. 2024), follow the link below.

Usage

df_pa

Format

A dataframe with 1,000 rows, each row being the inputs and (intermediate) outputs of a single probabilistic iteration, and 44 variables:

p_pfspd

Probability to transit from the progression-free survival (PFS) to progressed disease (PD) health state

p_pfsd

Probability to transit from the PFS to Death (D) health state

p_pdd

Probability to transit from the PD to D health state

p_dd

Probability to transit from the D to D health state

p_ae

Probability of occurence of an adverse event in the intervention stategy

rr

Relative effectiveness of the treatment (_int)

u_pfs

Utility value (per cycle) associated with PFS health state

u_pd

Utility value (per cycle) associated with PD health state

u_d

Utility value (per cycle) associated with D health state

u_ae

Utility decrement associated with the occurrence of an adverse event

c_pfs

Costs (per cycle) associated with PFS health state

c_pd

Costs (per cycle) associated with PD health state

c_d

Costs (per cycle) associated with D health state

c_thx

Costs (per cycle) associated with receiving treatment, in the PFS health state

c_ae

Costs associated with experiencing an adverse event

t_qaly_comp

Total undiscounted QALY obtained with the comparator, i.e. no treatment administered

t_qaly_int

Total undiscounted QALY obtained with the intervention, i.e. treatment administered

t_qaly_d_comp

Total discounted QALY obtained with the comparator, i.e. no treatment administered

t_qaly_d_int

Total discounted QALY obtained with the intervention, i.e. treatment administered

t_costs_comp

Total undiscounted costs obtained with the comparator, i.e. no treatment administered

t_costs_int

Total undiscounted costs obtained with the intervention, i.e. treatment administered

t_costs_d_comp

Total discounted costs obtained with the comparator, i.e. no treatment administered

t_costs_d_int

Total discounted costs obtained with the intervention, i.e. treatment administered

t_ly_comp

Total undiscounted LY obtained with the comparator, i.e. no treatment administered

t_ly_int

Total undiscounted LY obtained with the intervention, i.e. treatment administered

t_ly_d_comp

Total discounted LY obtained with the comparator, i.e. no treatment administered

t_ly_d_int

Total discounted LY obtained with the intervention, i.e. treatment administered

t_ly_pfs_d_comp

Total discounted life years accrued in PFS health state, comparator strategy

t_ly_pfs_d_int

Total discounted life years accrued in PFS health state, intervention strategy

t_ly_pd_d_comp

Total discounted life years accrued in PD health state, comparator strategy

t_ly_pd_d_int

Total discounted life years accrued in PD health state, intervention strategy

t_qaly_pfs_d_comp

Total discounted quality-adjusted life years accrued in PFS health state, comparator strategy

t_qaly_pfs_d_int

Total discounted quality-adjusted life years accrued in PFS health state, intervention strategy

t_qaly_pd_d_comp

Total discounted quality-adjusted life years accrued in PD health state, comparator strategy

t_qaly_pd_d_int

Total discounted quality-adjusted life years accrued in PD health state, intervention strategy

t_costs_pfs_d_comp

Total discounted costs accrued in PFS health state, comparator strategy

t_costs_pfs_d_int

Total discounted costs accrued in PFS health state, intervention strategy

t_costs_pd_d_comp

Total discounted costs accrued in PD health state, comparator strategy

t_costs_pd_d_int

Total discounted costs accrued in PD health state, intervention strategy

t_qaly_ae_int

Quality-adjusted life year decrement associated with the occurence of adverse events, intervention strategy

t_costs_ae_int

Costs associated with the occurence of adverse events, intervention strategy

inc_ly

Incremental QALYs obtained with the intervention versus the comparator

inc_qaly

Incremental QALYs obtained with the intervention versus the comparator

inc_costs

Incremental costs obtained with the intervention versus the comparator

Source

Pouwels XGLV, Kroeze K, van der Linden N, Kip MMA, Koffijberg H. Validating Health Economic Models With the Probabilistic Analysis Check dashBOARD. Value Health. 2024 Aug;27(8):1073-1084. doi: 10.1016/j.jval.2024.04.008.

Link to the original data ("df_pa") used in the PACBOARD publication: https://github.com/Xa4P/pacheck/tree/master/data-raw

Dataframe for testing

Description

A dataframe containing 10,000 iterations of a probabilistic analysis of a partitioned survival model. To access the original dataframe used in the scientific publication of PACBOARD (Pouwels et al. 2024), follow the link below.

Usage

df_pa_psm

Format

A dataframe with 10,000 rows, each row being the inputs and (intermediate) outputs of a single probabilistic iteration, and 46 variables:

p_ae

Probability of occurence of an adverse event in the intervention stategy

r_exp_pfs_comp

Rate of the exponential survival model used to estimate PFS of the comparator

rr_thx_pfs

Relative risk of the occurrence of progression of the intervention versus the comparator, used to estimate PFS of the intervention

r_exp_pfs_int

Rate of the exponential survival model used to estimate PFS of the intervention

shape_weib_os

Shape of the Weibull survival model used to estimate OS of the comparator and intervention

scale_weib_os_comp

Scale of the Weibull survival model used to estimate OS of the comparator

rr_thx_os

Relative risk of the occurrence of death of the intervention versus the comparator, used to estimate PFS of the intervention

scale_weib_os_int

Scale of the Weibull survival model used to estimate OS of the intervention

u_pfs

Utility value (per cycle) associated with PFS health state

u_pd

Utility value (per cycle) associated with PD health state

u_d

Utility value (per cycle) associated with D health state

u_ae

Utility decrement associated with the occurrence of an adverse event

c_pfs

Costs (per cycle) associated with PFS health state

c_pd

Costs (per cycle) associated with PD health state

c_d

Costs (per cycle) associated with D health state

c_thx

Costs (per cycle) associated with receiving treatment, in the PFS health state

c_ae

Costs associated with experiencing an adverse event

t_qaly_comp

Total undiscounted QALY obtained with the comparator, i.e. no treatment administered

t_qaly_int

Total undiscounted QALY obtained with the intervention, i.e. treatment administered

t_qaly_d_comp

Total discounted QALY obtained with the comparator, i.e. no treatment administered

t_qaly_d_int

Total discounted QALY obtained with the intervention, i.e. treatment administered

t_costs_comp

Total undiscounted costs obtained with the comparator, i.e. no treatment administered

t_costs_int

Total undiscounted costs obtained with the intervention, i.e. treatment administered

t_costs_d_comp

Total discounted costs obtained with the comparator, i.e. no treatment administered

t_costs_d_int

Total discounted costs obtained with the intervention, i.e. treatment administered

t_ly_comp

Total undiscounted LY obtained with the comparator, i.e. no treatment administered

t_ly_int

Total undiscounted LY obtained with the intervention, i.e. treatment administered

t_ly_d_comp

Total discounted LY obtained with the comparator, i.e. no treatment administered

t_ly_d_int

Total discounted LY obtained with the intervention, i.e. treatment administered

t_ly_pfs_d_comp

Total discounted life years accrued in PFS health state, comparator strategy

t_ly_pfs_d_int

Total discounted life years accrued in PFS health state, intervention strategy

t_ly_pd_d_comp

Total discounted life years accrued in PD health state, comparator strategy

t_ly_pd_d_int

Total discounted life years accrued in PD health state, intervention strategy

t_qaly_pfs_d_comp

Total discounted quality-adjusted life years accrued in PFS health state, comparator strategy

t_qaly_pfs_d_int

Total discounted quality-adjusted life years accrued in PFS health state, intervention strategy

t_qaly_pd_d_comp

Total discounted quality-adjusted life years accrued in PD health state, comparator strategy

t_qaly_pd_d_int

Total discounted quality-adjusted life years accrued in PD health state, intervention strategy

t_costs_pfs_d_comp

Total discounted costs accrued in PFS health state, comparator strategy

t_costs_pfs_d_int

Total discounted costs accrued in PFS health state, intervention strategy

t_costs_pd_d_comp

Total discounted costs accrued in PD health state, comparator strategy

t_costs_pd_d_int

Total discounted costs accrued in PD health state, intervention strategy

t_qaly_ae_int

Quality-adjusted life year decrement associated with the occurence of adverse events, intervention strategy

t_costs_ae_int

Costs associated with the occurence of adverse events, intervention strategy

inc_ly

Incremental QALYs obtained with the intervention versus the comparator

inc_qaly

Incremental QALYs obtained with the intervention versus the comparator

inc_costs

Incremental costs obtained with the intervention versus the comparator

Source

Pouwels XGLV, Kroeze K, van der Linden N, Kip MMA, Koffijberg H. Validating Health Economic Models With the Probabilistic Analysis Check dashBOARD. Value Health. 2024 Aug;27(8):1073-1084. doi: 10.1016/j.jval.2024.04.008.

Link to the original data ("df_pa_psm") used in the PACBOARD publication: https://github.com/Xa4P/pacheck/tree/master/data-raw

Perform a check

Description

Checks whether variables fulfill a specific test.

Usage

do_check(
  df,
  v_vars,
  check,
  label_check,
  template_ok = "all variables are {label_check}",
  template_fail = "{var} is not {label_check}"
)

Arguments

Value

List containing the results of the check (checks), and a tibble of status and message for each test (messages). The list of messages in the result contains a single line if the test passed, or if a test failed for one or more variables, a line for each failure.

Examples

data(df_pa)
do_check(df = df_pa,
         v_vars = c("u_pfs", "u_pd"),
         check = ~ .x >= 0,
         label_check = "positive"
         )

Perform discounted and undiscounted results check

Description

This function performs multiple checks on user-defined columns.

Usage

do_discount_check(df, v_outcomes = NULL, v_outcomes_d = NULL)

Arguments

Details

The variables contained in 'v_outcomes' and 'v_outcomes_d' should be in the same order.

Value

A matrix.

Examples

# Checking whether discounted QALYs are lower than undiscounted QALYs using the example data
do_discount_check(df = df_pa,
                  v_outcomes = "t_qaly_comp",
                  v_outcomes_d = "t_qaly_d_comp")
                  

Perform quick checks of inputs and outputs

Description

This function performs multiple checks on user-defined columns.

Usage

do_quick_check(
  df,
  v_probs = NULL,
  v_utilities = NULL,
  v_costs = NULL,
  v_hr = NULL,
  v_rr = NULL,
  v_r = NULL,
  v_outcomes = NULL
)

Arguments

Value

A matrix.

Examples

# Checking costs and utility values of the example data

do_quick_check(df = df_pa,
               v_utilities = c("u_pfs", "u_pd"),
               v_costs = c("c_pfs", "c_pd")
               )
               

Estimate decision sensitivy DSA using linear metamodel

Description

This function performs a logistic regression analysis and determines the decision sensitivity to parameter value using the logistic regression. (STILL IN DEVELOPMENT)

Usage

estimate_decision_sensitivity(df, y, x, y_binomial = FALSE, limit = 0)

Arguments

Details

The method for these analyses is described in [Merz et al. 1992](https://doi.org/10.1177

Value

A dataframe with the parameter values of the fitted logistic regression and the decision sensitivity associated with each parameter included in the logistic regression model.

Examples

# Determining decision sensitivity using a non-binomial outcome
data(df_pa)
df_pa$inmb <- df_pa$inc_qaly * 100000 - df_pa$inc_costs
estimate_decision_sensitivity(df = df_pa,
                              y = "inmb",
                              x = c("p_pfsd", "p_pdd"),
                              y_binomial = FALSE
                              )

Fit distribution to parameter

Description

This function fits statistical distributions to a user-defined parameter.

Usage

fit_dist(df, param, dist = c("norm", "beta", "gamma", "lnorm"))

Arguments

Details

The available distributions are: "norm" (normal), "beta", "gamma", "lnorm" (lognormal). The arguments of the lists are "AIC" which contains the Akaike Information Criteria for each fitted distribution and "Dist_parameters" which contains the parameters of the fitted distributions. The distributions are fitted using the fitdistrplus::fitdist()

Value

A list with two objects:

• Statistical_fit: a dataframe containing the statistical fit criteria of the fitted distributions.

• Dist_parameters: a dataframe containing the parameter value of the fitted distributions.

Examples

# Fitting normal and beta distribution to the "u_pfs" variable of the example dataframe.
data(df_pa)
fit_dist(df = df_pa,
         param = "u_pfs",
         dist = c("norm", "beta"))

Fit LASSO metamodel

Description

This function fits a lasso metamodel using the glmnet package.

Usage

fit_lasso_metamodel(
  df,
  y_var = NULL,
  x_vars = NULL,
  seed_num = 1,
  standardise = FALSE,
  tune_plot = TRUE,
  x_poly_2 = NULL,
  x_poly_3 = NULL,
  x_exp = NULL,
  x_log = NULL,
  x_inter = NULL
)

Arguments

Value

A list contaning the following elements:

• An object of the fitted metamodel,

• The coefficient of the fitted metamodel,

• information on the data used to fit the metamodel and its form.

Examples

#Fit lasso metamodel with two variables using the probabilistic data
data(df_pa)
fit_lasso_metamodel(df = df_pa,
                 y_var = "inc_qaly",
                 x_vars = c("p_pfsd", "p_pdd"),
                 tune_plot = TRUE
                 )

Fit linear metamodel

Description

This function fits and provides summary statistics of a linear regression model fitted on the input and output values of a probabilistic analysis.

Usage

fit_lm_metamodel(
  df,
  y_var = NULL,
  x_vars = NULL,
  standardise = FALSE,
  partition = 1,
  seed_num = 1,
  validation = FALSE,
  folds = 5,
  show_intercept = FALSE,
  x_poly_2 = NULL,
  x_poly_3 = NULL,
  x_exp = NULL,
  x_log = NULL,
  x_inter = NULL
)

Arguments

Details

Standardisation of the parameters is obtained by

(x - u(x)) / sd(x)

where x is the variable value, u(x) the mean over the variable and sd(x) the standard deviation of x.

For more details, see Jalal H, Dowd B, Sainfort F, Kuntz KM. Linear Regression Metamodeling as a Tool to Summarize and Present Simulation Model Results. Medical Decision Making. 2013;33(7):880-890. doi:10.1177/0272989X13492014

Value

A list containing the fit of the model and validation estimates and plots when selected.

Examples

# Fitting linear meta model with two variables using the probabilistic data
data(df_pa)
fit_lm_metamodel(df = df_pa,
                 y_var = "inc_qaly",
                 x_vars = c("p_pfsd", "p_pdd")
                 )

Fit random forest metamodel

Description

This function fits a random forest metamodel using the randomForestSRC package.

Usage

fit_rf_metamodel(
  df,
  y_var = NULL,
  x_vars = NULL,
  ntree = 500,
  seed_num = 1,
  tune = FALSE,
  var_importance = FALSE,
  pm_plot = FALSE,
  pm_vars = x_vars[1],
  validation = FALSE,
  folds = 5,
  show_intercept = FALSE,
  partition = 1,
  fit_complete_model = TRUE
)

Arguments

Value

A list containing the following elements:

• fit: a list, see randomForestSRC::rfsrc() for a description of the outputs contained in this list.

• model_info: a list containing the following elements:

  • x_vars: vector of names of parameters included in the metamodel;

  • y_var: name outcome variable;

  • form: formula of the metamodel based on 'x_vars' and 'y_var';

  • data: dataframe containing the inputs and output values used to fit (and fit) the metamodel;

  • type: "rf" for "random forest".

• (if 'tune' = TRUE) tune_fit: a list containing the results of the tuning process, see randomForestSRC::tune() for a description of the elements containd in this list.

• (if ‘tune' = TRUE) tune_plot: plot showing the out-of-bag error for each tested combination of ’mtry' and 'nodesize'.

• (if validation != FALSE) stats_validation: data frame containing the R-squared, Mean absolute error, Mean relative error, Mean squared error in the test validation set.

• (if validation = "test_train_split") calibration_plot: plot showing the rf-predicted versus observed output values in the test validation set.

If 'var_importance' is set to TRUE, the variable importance plot is printed in the console. If 'pm_plot'is used, the marginal/ partial importance plot(s) - drawn using randomForestSRC::plot.variable.rfsrc() - is (are) printed in the console.

Examples

# Fitting and tuning a random forest meta model with two variables using the example data
data(df_pa)

fit_rf_metamodel(df = df_pa,
                 y_var = "inc_qaly",
                 x_vars = c("p_pfsd", "p_pdd"),
                 tune = FALSE
                 )

Generate correlation matrix

Description

This function generates the correlation matrix of input and output values of a probabilistic analysis.

Usage

generate_cor(df, vars = NULL, figure = FALSE, digits = 3)

Arguments

Value

If figure == FALSE: a matrix with summary statistics for the selected inputs and outputs. If figure == TRUE: a tile ggplot2 of the correlation matrix.

Examples

# Generating summary data of all inputs using the example dataframe
data(df_pa)
generate_cor(df_pa)

Generate deterministic model inputs.

Description

This function generates the deterministic model inputs for the example health economic model developed to test the functionalities of the package.

Usage

generate_det_inputs()

Value

A list. A description of the inputs parameters is available in the documentation of the df_pa dataframe.

Examples

# Generating deterministic model inputs and storing them in an object.
l_inputs_det <- generate_det_inputs()

Generate probabilistic model inputs.

Description

This function generates the probabilistic model inputs for the example health economic model developed to test the functionalities of the package.

Usage

generate_pa_inputs(n_sim = 10000, sd_var = 0.2, seed_num = 452)

Arguments

Value

A dataframe. A description of the variables of the returned dataframe is available in the documentation of the df_pa dataframe.

Examples

# Generating deterministic model inputs and storing them in an object.
df_inputs_prob <- generate_pa_inputs()

Generate probabilistic model inputs for partitioned survival model.

Description

This function generates the probabilistic model inputs for the example health economic model developed to test the functionalities of the package.

Usage

generate_pa_inputs_psm(n_sim = 10000, sd_var = 0.2, seed_num = 452)

Arguments

Value

A dataframe. A description of the variables of the returned dataframe is available in the documentation of the df_pa_psm dataframe.

Examples

# Generating probabilistic model inputs and storing them in an object.

df_inputs_prob <- generate_pa_inputs_psm(n_sim = 10)

Generate summary statistics

Description

This function generates summary statistics of input and output values of a probabilistic analysis.

Usage

generate_sum_stats(df, vars = NULL)

Arguments

Value

A dataframe with summary statistics for the selected variables. The returned summary statistics are:

• Mean

• Standard deviation

• 2.5th percentile

• 97.5th percentile

• Minimum

• Maximum

• Median

• Skewness

• Kurtosis

Examples

# Generating summary data of all inputs
data(df_pa)
df_summary <- generate_sum_stats(df_pa)

A dataframe containing probabilistic inputs for testing

Description

A list containing 1,000 sets of probabilistic inputs and outputs from a discrete event simulation developed in R.

Usage

l_psa_aaa

Format

The dataframe contains the 1,000 probabilistic inputs and outputs obtained by executing the Main file_men 30years_FullModel.R' script (for 1,000 individuals) under the 'models' folder on the github repository of the model.

Source

Sweeting MJ, Masconi KL, Jones E, Ulug P, Glover MJ, Michaels JA, Bown MJ, Powell JT, Thompson SG. Analysis of clinical benefit, harms, and cost-effectiveness of screening women for abdominal aortic aneurysm. Lancet. 2018 Aug 11;392(10146):487-495. doi: 10.1016/S0140-6736(18)31222-4.

Link to Github repository: https://github.com/mikesweeting/AAA_DES_model

Perform deterministic one-way sensitivity analyses using probabilistic inputs and outputs.

Description

This function performs the deterministic one-way sensitivity analyses (DOWSA) using probabilistic inputs and outputs for the health economic model developed to test the package. The outcome of the DOWSA is the incremental net monetary benefit.

Usage

perform_dowsa(df, vars, wtp = 120000)

Arguments

Value

A dataframe. The outcome of the deterministic one-way sensitivity analyses is the iNMB by default.

Examples

# Perform the deterministic one-way sensitivity analyses for a selection of parameters

data(df_pa)
df_res_dowsa <- perform_dowsa(df = df_pa,
                              vars = c("rr", "c_pfs"))
                              

Perform the health economic simulation.

Description

This function performs the simulation of the health economic model developed to test the functionalities of the package.

Usage

perform_simulation(l_params)

Arguments

Value

A vector. This vector contains the (un)discounted intermediate and final outcomes of the health economic model.

Examples

# Perform the simulation using the deterministic model inputs
l_inputs_det <- generate_det_inputs()
v_results_det <- perform_simulation(l_inputs_det)

Perform the health economic simulation using partitioned survival model.

Description

This function performs the simulation of the partitioned survival health economic model developed to test the functionalities of the package.

Usage

perform_simulation_psm(l_params, min_fct = TRUE)

Arguments

Value

A vector. This vector contains the (un)discounted intermediate and final outcomes of the health economic model.

Examples

# Perform the simulation using one iteration of the probabilistic model inputs
l_inputs_det <- as.list(generate_pa_inputs_psm(n_sim = 1))
v_results_det <- perform_simulation_psm(l_inputs_det)

Plotting cost-effectiveness plane for two strategies.

Description

This function plots the cost-effectiveness plane for two strategies.

Usage

plot_ce(df, e_int, e_comp, c_int, c_comp, currency = "euro", axes = TRUE)

Arguments

Value

A ggplot2 graph.

Examples

# Plot cost effectiveness plane
data("df_pa")
plot_ce(df = df_pa,
        e_int = "t_qaly_d_int",
        e_comp = "t_qaly_d_comp",
        c_int = "t_costs_d_int",
        c_comp = "t_costs_d_comp",
        currency = "none"
        )

Plotting cost-effectiveness plane.

Description

This function plots the cost-effectiveness plane for an infinite amount of strategies .

Usage

plot_ce_mult(df, outcomes, costs, ellipse = FALSE, currency = "euro")

Arguments

Value

A ggplot2 graph. # Plot cost effectiveness plane as ellipses data("df_pa") df_pa$t_qaly_d_int2 <- df_pa$t_qaly_d_int * 1.5 # creating additional outcome variable df_pa$t_costs_d_int2 <- df_pa$t_costs_d_int * 1.5 # creating additional cost variable plot_ce_mult(df = df_pa, outcomes = c("t_qaly_d_int", "t_qaly_d_comp", "t_qaly_d_int2"), costs = c("t_costs_d_int","t_costs_d_comp", "t_costs_d_int2"), ellipse = TRUE, currency = "none")

Plotting the cost-effectiveness acceptability curves.

Description

This function plots cost-effectiveness acceptability curves.

Usage

plot_ceac(df, name_wtp, currency = "euro")

Arguments

Value

A ggplot2 graph.

Examples

# Plot CEAC based on results from calculate_ceac()
data("df_pa")
df_ceac_p <- calculate_ceac(df = df_pa,
                            e_int = "t_qaly_d_int",
                            e_comp = "t_qaly_d_comp",
                            c_int = "t_costs_d_int",
                            c_comp = "t_costs_d_comp")
plot_ceac(df = df_ceac_p,
          name_wtp = "WTP_threshold",
          currency = "none")

Plot moving average

Description

This function plots the moving average of a user-defined variable.

Usage

plot_convergence(
  df,
  param,
  block_size = 500,
  conv_limit = 0,
  y_min = NULL,
  y_max = NULL,
  breaks = NULL,
  variance = FALSE
)

Arguments

Value

A ggplot graph.

Examples

# Checking the moving average of the incremental QALYs using the example data.
data(df_pa)
plot_convergence(df = df_pa,
                 param = "inc_qaly"
                 )

Plotting the incremental cost-effectiveness plane.

Description

This function plots the incremental cost-effectiveness plane for two strategies.

Usage

plot_ice(
  df,
  e_int,
  e_comp,
  c_int,
  c_comp,
  col = NULL,
  n_it = NULL,
  wtp = NULL,
  currency = "euro",
  axes = TRUE
)

Arguments

Value

A ggplot2 graph.

Examples

# Generating plot using the example dataframe, and a willlingness-to-pay threshold of 80,0000 euros.
data(df_pa)
plot_ice(df = df_pa,
         e_int = "t_qaly_d_int",
         e_comp = "t_qaly_d_comp",
         c_int = "t_costs_d_int",
         c_comp = "t_costs_d_comp",
         wtp = 8000,
         currency = "none")

Plot (i)NMB or (i)NHB.

Description

This function plots the Net Monetary Benefits (NMB) and Net Health Benefits (NHB) for each strategy and the incremental NMB and NHB (only pairwise comparison).

Usage

plot_nb(df, NMB = TRUE, comparators = TRUE, incremental = FALSE)

Arguments

Details

The use this function, the dataframe 'df' should contain the variables 'NMB_int', 'NMB_comp', 'iNMB', 'NHB_int', 'NHB_comp', and 'iNHB'. For instance, use the calculate_nb function to calculate these outcomes.

Value

A ggplot2 graph.

Examples

# Calculate NB's at a willingness-to-pay threshold of 80000 per unit of effects
data("df_pa")
df_nmb <- calculate_nb(df = df_pa,
             e_int = "t_qaly_d_int",
             e_comp = "t_qaly_d_comp",
             c_int = "t_costs_d_int",
             c_comp = "t_costs_d_comp",
             wtp = 80000)

# Plot NMB's for each comparator
plot_nb(df = df_nmb,
        NMB = TRUE,
        comparators = TRUE)

Plot NMB or NHB.

Description

This function plots the Net Monetary Benefits (NMB) and Net Health Benefits (NHB) for an infinite amount of strategies.

Usage

plot_nb_mult(df, outcomes, costs, wtp, NMB = TRUE)

Arguments

Value

A ggplot2 graph.

Examples

# Plot NMB's at a 50,0000 euro WTP threshold for three strategies
data("df_pa")
df_pa$t_qaly_d_int2 <- df_pa$t_qaly_d_int * 1.5
df_pa$t_costs_d_int2 <- df_pa$t_costs_d_int * 1.5
plot_nb_mult(df = df_pa,
             outcomes = c("t_qaly_d_int2", "t_qaly_d_int", "t_qaly_d_comp"),
             costs = c("t_costs_d_int", "t_costs_d_int2", "t_costs_d_comp"),
             wtp = 50000)

Plot parametric survival models

Description

This function plots two parametric survival models based on he functional form of the model and their parameters.

Usage

plot_surv_mod(
  df,
  surv_mod_1,
  surv_mod_2,
  v_names_param_mod_1,
  v_names_param_mod_2,
  label_surv_1 = "first survival",
  label_surv_2 = "second survival",
  iteration,
  time = seq(0, 5, 1)
)

Arguments

Details

The parametric models that can be used are the following: exponential (exp), Weibull (weibull), gamma (gamma), loglogistic (logis), and lognormal (lnorm). All these functions are implemented following their distribution function as documented in the stats package.

Value

A ggplot object.

Predict using a fitted metamodel

Description

Predict using a fitted metamodel

Usage

predict_metamodel(model = NULL, inputs = NULL, output_type = "vector")

Arguments

Value

returns a vector of the the predictions ('vector' output_type) or the parameter values used for the predictions and the predictions ('dataframe' or 'long_df' output_type).

Examples

#Making 3 predictions for a two-variable metamodel,
# using a vector as input, and yielding a dataframe as output.
data(df_pa)
lm_fit = fit_lm_metamodel(df = df_pa,
                 y_var = "inc_qaly",
                 x_vars = c("p_pfsd", "p_pdd")
                 )

vec = c(0.1,0.2,0.08,0.15,0.06,0.25)

predict_metamodel(model = lm_fit,
                 inputs = vec,
                 output_type = "dataframe"
                 )

Summary statistics of the incremental cost-effectiveness plane.

Description

This function computes the probability that the probabilistic outcome is in each of the quadrants.

Usage

summary_ice(df, e_int, e_comp, c_int, c_comp)

Arguments

Value

A dataframe.

Examples

# Generating statistics of the incremental cost-effectiveness plane using the example data.
data(df_pa)
summary_ice(df = df_pa,
            e_int = "t_qaly_d_int",
            e_comp = "t_qaly_d_comp",
            c_int = "t_costs_d_int",
            c_comp = "t_costs_d_comp"
            )

Validate metamodels

Description

Validate metamodels

Usage

validate_metamodel(
  model = NULL,
  method = NULL,
  partition = 1,
  folds = 1,
  show_intercept = FALSE,
  seed_num = 1,
  df_validate = NULL
)

Arguments

Value

.........................

Examples

#Validating meta model with two variables using the probabilistic data, using cross-validation.
data(df_pa)
lm_fit = fit_lm_metamodel(df = df_pa,
                 y_var = "inc_qaly",
                 x_vars = c("p_pfsd", "p_pdd")
                 )

validate_metamodel(model = lm_fit,
                 method = "cross_validation",
                 folds = 5
                 )

Visualise the distribution of a single parameter

Description

This function plots the distribution of a single parameter.

Usage

vis_1_param(
  df,
  param = NULL,
  binwidth = NULL,
  type = "histogram",
  dist = NULL,
  user_dist = NULL,
  user_param_1 = NULL,
  user_param_2 = NULL,
  user_mean = NULL
)

Arguments

Details

The available distributions are: "norm" (normal), "beta", "gamma", "lnorm" (lognormal). TO CHECK –> ask for mean and SD/SE for the user-defined distribution???

Value

A ggplot2 graph.

Examples

# Generating histogram for the costs of progression-free health state, bins of 50 euros
data(df_pa)
vis_1_param(df = df_pa, param = "c_pfs", binwidth = 50)

Visualise the distribution of two parameters

Description

This function plots the distribution of two parameters in a scatterplot.

Usage

vis_2_params(
  df,
  param_1,
  param_2,
  slope = NULL,
  intercept = 0,
  check = NULL,
  fit = NULL
)

Arguments

Value

A ggplot graph.

Examples

# Generating plot for the costs of progression-free health state versus incremental costs
data(df_pa)
vis_2_params(df = df_pa, param_1 = "c_pfs", "inc_costs")