Randomly draw parameters from their (joint) probability distribution.
sample_pars(n = 100, input_data, tx_names = iviRA::treatments$sname, nma_acr_mean = iviRA::nma.acr.naive$mean, nma_acr_vcov = iviRA::nma.acr.naive$vcov, nma_acr_k_lower = 0.75, nma_acr_k_upper = 0.92, nma_das28_mean = iviRA::nma.das28.naive$mean, nma_das28_vcov = iviRA::nma.das28.naive$vcov, nma_das28_k_lower = 0.75, nma_das28_k_upper = 0.92, nma_haq_mean = iviRA::nma.haq.naive$mean, nma_haq_vcov = iviRA::nma.haq.naive$vcov, nma_haq_k_lower = 0.75, nma_haq_k_upper = 0.92, acr2haq_mean = iviRA::acr2haq$mean, acr2haq_se = iviRA::acr2haq$se, acr2das28_lower = iviRA::acr2das28$inception$lower, acr2das28_upper = iviRA::acr2das28$inception$upper, acr2sdai_lower = iviRA::acr2sdai$inception$lower, acr2sdai_upper = iviRA::acr2sdai$inception$upper, acr2cdai_lower = iviRA::acr2cdai$inception$lower, acr2cdai_upper = iviRA::acr2cdai$inception$upper, acr2eular_mat = iviRA::acr2eular, eular2haq_mean = iviRA::eular2haq$mean, eular2haq_se = iviRA::eular2haq$se, rebound_lower = 0.7, rebound_upper = 1, haq_lprog_tx_mean = iviRA::haq.lprog$tx$est, haq_lprog_tx_se = iviRA::haq.lprog$tx$se, haq_lprog_age_mean = iviRA::haq.lprog$diff.age$est, haq_lprog_age_se = iviRA::haq.lprog$diff.age$se, haq_lcgm_pars = iviRA::haq.lcgm, ltfemale = iviRA::lifetable.female, ltmale = iviRA::lifetable.male, mort_logor = iviRA::mort.or$logor, mort_logor_se = iviRA::mort.or$logor_se, mort_loghr_haqdif = iviRA::mort.hr.haqdif$loghr, mort_loghr_se_haqdif = iviRA::mort.hr.haqdif$loghr_se, ttd_all = iviRA::ttd.all, ttd_da = iviRA::ttd.da, ttd_eular = iviRA::ttd.eular, ttsi = iviRA::ttsi, tx_cost = iviRA::tx.cost, hosp_days_mean = iviRA::hosp.cost$days_mean, hosp_days_se = iviRA::hosp.cost$days_se, hosp_cost_mean = iviRA::hosp.cost$cost_pday_mean, hosp_cost_se = iviRA::hosp.cost$cost_pday_se, mgmt_cost_mean = iviRA::mgmt.cost$est, mgmt_cost_se = iviRA::mgmt.cost$se, si_cost = 5873, si_cost_range = 0.2, si_ul = 0.156, si_ul_range = 0.2, tx_attr_utilcoef_lower = iviRA::utility.tx.attr$coef$lower, tx_attr_utilcoef_upper = iviRA::utility.tx.attr$coef$upper, tx_attr_utilcoef_names = iviRA::utility.tx.attr$coef$var, utility_mixture_pain = iviRA::pain, pl_mean = iviRA::prod.loss$est, pl_se = iviRA::prod.loss$se)
| n | Size of the posterior sample. |
|---|---|
| input_data | An object of class 'input_data' returned from get_input_data. |
| tx_names | Vector of treatment names. Length should be equal to the number of treatments
included in the NMA for ACR response ( |
| nma_acr_mean | Posterior means for ACR response NMA parameters on probit scale for biologic naive patients (i.e., 1st line). ACR response is modeled using an ordered probit model. |
| nma_acr_vcov | Variance-covariance matrix for ACR response NMA parameters on probit scale for biologic naive patients (i.e., 1st line). ACR response is modeled using an ordered probit model. |
| nma_acr_k_lower | Treatment effects for bDMARD experienced patients are reduced by multiplying the parameters of the statistical model of ACR response for bDMARD naive patients by a constant \(k\). This is the lower bound for that constant \(k\). |
| nma_acr_k_upper | Upper bound for the constant \(k\). |
| nma_das28_mean | Posterior means for DAS28 NMA parameters for biologic naive patients (i.e., 1st line). Change in DAS28 from baseline is modeled using a linear model. |
| nma_das28_vcov | Variance-covariance matrix for DAS28 NMA paramters for biologic naive patients (i.e., 1st line). Change in DAS28 from baseline is modeled using a linear model. |
| nma_das28_k_lower | Treatment effects for bDMARD experienced patients are reduced by
multiplying the parameters of the statistical model of the change in DAS28 at 6 months for
bDMARD naive patients by a constant \(k\). This is the lower bound for that constant
|
| nma_das28_k_upper | Upper bound for constant \(k\). |
| nma_haq_mean | Posterior means for HAQ NMA parameters for biologic naive patients (i.e., 1st line). Change in HAQ from baseline is modeled using a linear model. |
| nma_haq_vcov | Variance-covariance matrix for HAQ NMA paramters for biologic naive patients (i.e., 1st line). Change in HAQ from baseline is modeled using a linear model. |
| nma_haq_k_lower | Treatment effects for bDMARD experienced patients are reduced by
multiplying the parameters of the statistical model of the change in HAQ at 6 months for
bDMARD naive patients by a constant \(k\). This is the lower bound for that constant
|
| nma_haq_k_upper | Upper bound for constant \(k\). |
| acr2haq_mean | Mean HAQ change by ACR response category. |
| acr2haq_se | Standard error of mean HAQ change by ACR response category. |
| acr2das28_lower | Lower bound for change in DAS28 by ACR response category. |
| acr2das28_upper | Upper bound for change in DAS28 by ACR response category. |
| acr2sdai_lower | Lower bound for change in SDAI by ACR response category. |
| acr2sdai_upper | Upper bound for change in SDAI by ACR response category. |
| acr2cdai_lower | Lower bound for change in CDAI by ACR response category. |
| acr2cdai_upper | Upper bound for change in CDAI by ACR response category. |
| acr2eular_mat | A two-way frequency matrix with columns denoting EULAR response (none, moderate, good) and rows denoting ACR response (<20, 20-50, 50-70, 70+). |
| eular2haq_mean | Mean HAQ change by Eular response category. |
| eular2haq_se | Standard error of mean HAQ change by Eular response category. |
| rebound_lower | The rebound is the increase in HAQ following treatment discontinuation.
It is defined as a proportion \(f\) times the size of the inititial treatment response.
|
| rebound_upper |
|
| haq_lprog_tx_mean | Point estimate of linear yearly HAQ progression rate by treatment. |
| haq_lprog_tx_se | Standard error of linear yearly HAQ progression rate by treatment. |
| haq_lprog_age_mean | Impact of age on annual linear HAQ progression rate. |
| haq_lprog_age_se | Standard error of impact of age on annual linear HAQ progression rate. |
| haq_lcgm_pars | Parameters of LCGM for HAQ progression. |
| ltfemale | Lifetable for women. Must contain column 'age' for single-year of age and 'qx' for the probability of death at a given age. Age must range from 0 to 100. |
| ltmale | Identical to |
| mort_logor | Log odds ratio of impact of baseline HAQ on probability of mortality. |
| mort_logor_se | Standard error of log odds ratio of impact of baseline HAQ on probability of mortality. |
| mort_loghr_haqdif | Log hazard ratio of impact of change in HAQ from baseline on mortality rate. A vector with each element denoting (in order) hazard ratio for months 0-6, >6 - 12, >12 - 24, >24 -36, >36. |
| mort_loghr_se_haqdif | Standard error of log hazard ratio of impact of change in HAQ from baseline on mortality rate. |
| ttd_all | A list containing time to treatment discontinuation parameters representative of all patients (i.e., unstratified). See 'Time to treatment discontinuation' for more details. |
| ttd_da | A list containing time to treatment discontinuation parameters with covariates for moderate and high disease activity. See 'Time to treatment discontinuation'. |
| ttd_eular | A list containing time to treatment discontinuation parameters stratified by EULAR response. See 'Time to treatment discontinuation'. |
| ttsi | Paramters of survival model used to estimate time to serious infection. |
| tx_cost | Treatment cost matrix and treatment lookup in format of iviRA::tx.cost. |
| hosp_days_mean | Vector denoting average number of hospital days for HAQ < 0.5, 0.5 <= HAQ < 1, 1 <= HAQ < 1.5, 1.5 <= HAQ < 2, 2 <= HAQ < 2.5, HAQ >= 2.5. |
| hosp_days_se | Vector denoting standard error of average number of hospital days for HAQ < 0.5, 0.5 <= HAQ < 1, 1 <= HAQ < 1.5, 1.5 <= HAQ < 2, 2 <= HAQ < 2.5, HAQ >= 2.5. |
| hosp_cost_mean | Mean of daily hospital cost. |
| hosp_cost_se | Standard error of dail hospital cost. |
| mgmt_cost_mean | Mean of costs of services (in order: chest x-ray, x-ray visit, outpatient followup, Mantoux tuberculin skin test) for general management of RA. |
| mgmt_cost_se | Standard error of mean of costs of services for general management of RA for general management of RA. |
| si_cost | Cost of a serious infection. |
| si_cost_range | Range used to vary serious infection cost. Default is to calculate upper and lower bound by multiplying
|
| si_ul | One month loss in utility from a serious infection. |
| si_ul_range | Range used to vary serious infection utility loss. Default is to calculate upper and lower bound by multiplying
|
| tx_attr_utilcoef_lower | Lower bound for utility gain from treatment attributes. |
| tx_attr_utilcoef_upper | Upper bound for utility gain from treatment attributes. |
| tx_attr_utilcoef_names | Names of treatment attributes to be returned in sampled matrix
|
| utility_mixture_pain | Summary statistics for bivariate distribution of HAQ and pain. Format should be the same as iviRA::pain. Currently, each element of the list must be of length 1. |
| pl_mean | Mean annual productivity loss per 1-unit increase in HAQ. |
| pl_se | Standard error of mean annual productivity loss per 1-unit increase in HAQ. |
List containing samples for the following model parameters:
A list containing randomly sampled values of the parameters of the statistical model of ACR response at 6 months.
A list containing randomly sampled values of the parameters of the statistical model of change in DAS28 at 6 months.
Identical to DAS28 but for the HAQ score.
A matrix of sampled HAQ changes by ACR response category. The matrix has four columns for ACR < 20, ACR 20 - <50, ACR 50 - <70, and ACR 70+.
A matrix of sampled changes in DAS28 by ACR response category. The matrix has four columns for ACR < 20, ACR 20 - <50, ACR 50 - <70, and ACR 70+.
Same as acr2das28 but for SDAI.
Same as acr2das28 but for CDAI.
An array of matrices. Each matrix represents a random sample of the conditional probability of each EULAR response category for a given ACR response.
A matrix of sampled HAQ changes by Eular response category. The matrix has three columns for no response, moderate response, and good response.
Vector of the sampled values of the HAQ rebound (i.e., the increase in HAQ following treatment discontinuation.)
A matrix of sampled yearly linear change in HAQ by treatment.
The matrix has one column for each treatment in tx_names.
A matrix of sampled yearly linear change in HAQ by age. The matrix has three columns for age < 40, age 40-64, and age 65+.
A list of two elements containing parameters from the latent class growth
model. The first element is delta which is a an array of sampled matrices with
each matrix containing coefficients predicting class membership. Rows are classes and columns index
variables. beta is similar to delta, but each matrix contains coefficients
predicting HAQ as a function of time using a quadratic polynomial model.
A list with two elements for consisting of two matrics, one for males and
one for females. Each matrix contains three variables: age, qx
(probability of death) and logit_qx (the logit of the probability of death).
Importantly, there is a row for each single-year of age from 0 to 100, which is passed to
the sim_iviRA function.
Matrix of log odds ratio used to adjust mortality. One row for each sample and one column for each variable used to adjust mortality.
Matrix of the log hazard ratio of the impact of a change in HAQ from baseline on mortality. Columns denote hazard ratios at times < 6 months, months 6 - <12, months 12 - <24, months 24 - <36, and months 36+.
Sampled values of time to treatment discontinuation parameters representative of all patients. See 'Time to treatment discontinuation' for more details.
Sampled values of time to treatment discontinuation parameters with covariates for moderate and high disease activity. See 'Time to treatment discontinuation' for more details.
Sampled values of time to treatment discontinuation parameters stratified by EULAR response. See 'Time to treatment discontinuation' for more details.
A matrix of sampled values of time to serious infection. The matrix
has one column for each treatment in tx_names.
Identical to argument tx_cost passed to sample_pars.
A list of two matrices hosp.days and cost.pday. hosp.days is sample of hospital days by HAQ category; the
columns of the matrix are the six HAQ categories (HAQ < 0.5, 0.5 <= HAQ < 1, 1 <= HAQ < 1.5, 1.5 <= HAQ < 2, 2 <= HAQ < 2.5, HAQ >= 2.5).
in hosp.days are HAQ. cost.pday is a sample of the costs per hospital day by HAQ category; the columns are the same six HAQ
categories as in hosp.days.
Matrix of sampled values of general management costs. Each column is a different category of costs ( chest x-ray, x-ray visit, outpatient follow-up, and Mantoux tuberculin skin test).
Vector of sampled values of the medical cost of a serious infection.
A list containing samples of all parameters in the Hernandez Alva (2013) mixture model. See 'Sampled mixture model parameters' for details.
A matrix of sampled regression coefficients from the model mapping HAQ to EQ5D utility in Wailoo (2006). Variables are (in order) "int" (intercept), "age" (patient age), "dis_dur" (disease duration), "haq0" (baseline HAQ), "male" (1 = male, 0 = female), "prev_dmards" (number of previous DMARDs), and "haq" (current HAQ).
Vector of the sampled values of the annualized utility loss from a serious infection.
Matrix of sampled values of utility gains. Each column is a different treatment attribute.
Vector of sampled values of decrease in wages (e.g. productivity loss) per unit increase in HAQ.
Time to treatment discontinuation parameters should be contained in a list of lists. The top-level
list identifies the name of the probability distribution; the possible distributions are the
exponential (exponential), Weibull (weibull), Gompertz (gompertz),
gamma (gamma), log-logistic (llogis), lognormal (lnorm), and generalized gamma (gengamma). Each distribution
should also contain a list with five elements:
A vector of the maximum likelihood estimates of the parameters.
The variance-covariance of the parameters.
A vector of the indices of the location parameters.
A vector of the indices of the first ancillary parameter.
A vector of indices of the second ancillary parameter.
The maximum likelihood estimates should be transformed to the real line. For example, if the model is fit using
flexsurvreg in the flexsurv package, the output should be returned from res.t.
The sampled mixture model parameters are contained in a list containing the following:
Coefficients for class 1 explanatory variables. A matrix of random draws where each column is an explanatory variable.
Coefficients for class 2 explanatory variables. A matrix of random draws where each column is an explanatory variable.
Coefficients for class 3 explanatory variables. A matrix of random draws where each column is an explanatory variable.
Coefficients for class 4 explanatory variables. A matrix of random draws where each column is an explanatory variable.
Random effects intecept term for class 1. A vector of random draws.
Random effects intecept term for class 2. A vector of random draws.
Random effects intecept term for class 3. A vector of random draws.
Random effects intecept term for class 4. A vector of random draws.
Random effects term for male indicator variable. A vector of random draws.
Variance for class 1. A vector of random draws.
Variance for class 2. A vector of random draws.
Variance for class 3. A vector of random draws.
Variance for class 4. A vector of random draws.
Random effects variance term. A vector of random draws.
Coefficients for explanatory variables explaining the probbaility of class membership. An array of matrices where each matrix is a random draw. There are three rows in each matrix (one for each class) and four columns (one for each explanatory variable).
The list also contains summary statistics for pain and HAQ, which is needed to simulate pain. In particular, the object 'pain' in the list is a list containing:
Mean of pain score in the population.
Mean of HAQ score in the population.
Variance of pain score in the population.
Variance of HAQ in the population.
Correlation between pain and HAQ in the population.
pop <- sample_pop(n = 10, type = "homog") input.dat <- get_input_data(pop = pop) parsamp <- sample_pars(n = 10, input_dat = input.dat)