Simulate Longitudinal PRO Data with anchor and validator variables included

This version 2 was updated based on MWPC MMRM simulations

sim_pro_dat_v2(
  N = 1000,
  number.timepoints = 4,
  number.anchor.categories = 4,
  reg.formula = formula(~Time + Group + Time * Group),
  Beta.anchor = 0,
  thresholds = c(0.2, 0.4, 0.6, 0.8),
  polychor.struc = "ar1",
  polychor.value = 0.4,
  number.of.anchor.groups = 5,
  Beta.PRO = NULL,
  corr = "ar1",
  cor.value = 0.8,
  var.values = 2,
  n.val = 5,
  n.cat = c(3, NA, NA, NA, 5),
  cor.val.ref = c(0.5, 0.6, 0.8, 0.2, 0.3)
)

Arguments

number.anchor.categories	anchor generation - Pass to sim_dat_ord(): the number of PGIS anchor categories to be generated, this is passed to the number.groups in the sim_dat_ord function
reg.formula	anchor generation - Pass to sim_dat_ord(): this is a regression formula to pass to the anchor data generation; null is formula(~ Group + Time + Time*Group),
Beta.anchor	anchor generation - Pass to sim_dat_ord(): this is the Beta for the regression equation; numeric matrix of Beta values OR a scalar value != 0 that will be the final value of the interaction parameters, default is all(Beta == 0) for Type I error simulations
thresholds	anchor generation - Pass to sim_dat_ord(): the ordinal generation uses a probit approach, so these are the thresholds
polychor.struc	anchor generation - Pass to sim_dat_ord(): options for correlation structure c('ind', 'ar1', 'cs'), default is 'ar1'
polychor.value	anchor generation - Pass to sim_dat_ord(): numeric, the first corr in ar1, the corr in cs option
corr	PRO data generation, options for correlation structure c('ind', 'ar1', 'cs'), default is 'ar1'
cor.value	PRO data generation, numeric, the first corr in ar1, the corr in cs option, default is 0.8
var.values	PRO data generation, numeric, if numeric vector, the var values in the var/covar matrix, if scalar, the final var value in the var/covar matrix, others filled in automatically
n.val	Validator variable (Reference Measure) generation - Pass to sim_val_var(): numeric, number of validators
n.cat	Validator variable (Reference Measure) generation - Pass to sim_val_var(): numeric vector, number of categories in each validator, NA for continuous validator variables
cor.val.ref	Validator variable (Reference Measure) generation - Pass to sim_val_var(): numeric vector, the correlation between the PRO score and each of the n.val validators

Value

returns a dataframe containing the simulated data