true_vals_exp_covar {BPrinStratTTE}

Adding true values to estimates for models with an exponential endpoint and consideration of predictors of the intercurrent event

Description

Adding true values to estimates for models with an exponential endpoint and consideration of predictors of the intercurrent event

Usage

true_vals_exp_covar(x, d_params, m_params)

Arguments

x

Model object as returned by fit_single_exp_covar().

d_params

List of data parameters as used in fit_single_exp_covar().

m_params

List of model parameters as used in fit_single_exp_covar().

Value

A summary table with parameter estimates, true values and differences.

See Also

true_vals_exp_nocovar()

Examples

d_params_covar <- list(
  n = 1000,        
  nt = 500,       
  prob_X1 = 0.4, 
  prob_ice_X1 = 0.5, 
  prob_ice_X0 = 0.2,
  fu_max = 48*7,       
  T0T_rate = 0.2,     
  T0N_rate = 0.2,     
  T1T_rate = 0.15,     
  T1N_rate = 0.1
 )
dat_single_trial <- sim_dat_one_trial_exp_covar(
  n = d_params_covar[["n"]], 
  nt = d_params_covar[["nt"]],
  prob_X1 = d_params_covar[["prob_X1"]],
  prob_ice_X1 = d_params_covar[["prob_ice_X1"]],
  prob_ice_X0 = d_params_covar[["prob_ice_X0"]],
  fu_max = d_params_covar[["fu_max"]],  
  T0T_rate = d_params_covar[["T0T_rate"]],
  T0N_rate = d_params_covar[["T0N_rate"]],
  T1T_rate = d_params_covar[["T1T_rate"]],
  T1N_rate = d_params_covar[["T1N_rate"]] 
)
m_params_covar <- list(
  tg = 48,
  p = 2, 
  prior_delta = matrix(
    c(0, 5, 0, 5),
    nrow = 2, byrow = TRUE),
  prior_0N = c(1.5, 5),
  prior_1N = c(1.5, 5),
  prior_0T = c(1.5, 5),
  prior_1T = c(1.5, 5),
  t_grid =  seq(7, 7 * 48, 7) / 30,
  chains = 2,
  n_iter = 3000,
  warmup = 1500,
  cores = 2,
  open_progress = FALSE,
  show_messages = TRUE   
)

fit_single <- fit_single_exp_covar(
  data = dat_single_trial,
  params = m_params_covar,
  summarize_fit = TRUE
)
print(fit_single)
tab_obs_truth <- true_vals_exp_covar(
  x = fit_single,
  d_params = d_params_covar,
  m_params = m_params_covar
)
print(tab_obs_truth)