Fit multiple models to data from two-arm trials with an exponentially distributed time-to-event endpoint and no predictor of the intercurrent event

Fit multiple models to data from two-arm trials with an exponentially distributed time-to-event endpoint and no predictor of the intercurrent event

Usage

fit_mult_exp_nocovar(dat_mult_trials, params, seed = 23)

Arguments

dat_mult_trials

List generated by sim_dat_mult_trials_exp_nocovar.

params

List of model parameters as supplied to fit_single_exp_nocovar.

seed

Numeric value, seed for reproducibility.

Value

A list of objects generated by fit_single_exp_nocovar.

See also

sim_dat_mult_trials_exp_nocovar(), fit_single_exp_nocovar(), fit_mult_exp_covar()

Examples

d_params_nocovar <- list(
  n = 500L,
  nt = 250L,
  prob_ice = 0.5,
  fu_max = 336L,
  prop_cens = 0.15,  
  T0T_rate = 0.2,
  T0N_rate = 0.2,
  T1T_rate = 0.15,
  T1N_rate = 0.1
)
dat_mult_trials <- sim_dat_mult_trials_exp_nocovar(
  n_iter = 2,
  params = d_params_nocovar 
)
m_params_nocovar <- list(
  tg = 48L,
  prior_piT = c(0.5, 0.5),
  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 = 2L,
  n_iter = 3000L,
  warmup = 1500L,
  cores = 2L,
  open_progress = FALSE,
  show_messages = TRUE
)
# \donttest{
fit_multiple <- fit_mult_exp_nocovar(
  dat_mult_trials = dat_mult_trials,
  params = m_params_nocovar,
  seed = 12
)
lapply(fit_multiple, dim)
head(fit_multiple[[1]])
# }