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Simulate non-proportional slowing model.

Source: R/pmrm_simulate_slowing_nonproportional.R
pmrm_simulate_slowing_nonproportional.Rd

Simulate a dataset from the non-proportional slowing model.

Usage

pmrm_simulate_slowing_nonproportional(
  patients = 300,
  visit_times = seq(from = 0, to = 4, by = 1),
  spline_knots = visit_times,
  spline_method = c("natural", "fmm"),
  tau = 0,
  alpha = log(spline_knots + 1),
  beta = cbind(0, rbind(0, rep(0.2, length(visit_times) - 1L), rep(0.3,
    length(visit_times) - 1L))),
  gamma = numeric(0L),
  sigma = rep(1, length(visit_times)),
  rho = rep(0, length(visit_times) * (length(visit_times) - 1L)/2L)
)

Arguments

patients

Positive integer scalar, total number of patients in the output dataset. Patients are allocated (roughly) uniformly across the study arms.

visit_times

Numeric vector, the continuous scheduled time after randomization of each study visit.

spline_knots

Numeric vector of spline knots on the continuous scale, including boundary knots.

spline_method

Character string, spline method to use for the base model. Must be "natural" or "fmm". See stats::splinefun() for details.

tau

Positive numeric scalar, standard deviation for jittering the simulated time points. Defaults to 0 so that the observed continuous times are just the scheduled visit times.

alpha

Numeric vector of spline coefficients for simulating the mean function f(t_{ij} | spline_knots, alpha). Must have length(spline_knots) elements.

beta

Numeric matrix with one row for each study arm (including the control arm) and one column for each study visit (including baseline). See vignette("models", package = "pmrm") for details on this parameter.

gamma

Numeric vector of model coefficients for covariate adjustment. The simulation functions in pmrm simulate length(gamma) columns for the covariate adjustment model matrix W. Set to numeric(0) to omit covariates.

sigma

A positive numeric vector of visit-level standard deviation parameters.

rho

A finite numeric vector of correlation parameters. Must have length J * (J - 1) / 2, where J is length(visit_times). The full covariance matrix Sigma is given by diag(sigma) %*% RTMB::unstructured(length(sigma))$corr(rho) %*% diag(sigma).

Value

A tibble of clinical data simulated from the slowing model. See the "Simulated data" section of this help file for details.

Details

See vignette("models", package = "pmrm") for details.

Simulated data

The datasets returned from the simulation functions have one row per patient visit and the following columns which conform to the notation from vignette("models", package = "pmrm"):

  • patient: Character vector of patient ID labels.

  • visit: Ordered factor of clinical visits with labels included. min(visit) indicates the baseline visit.

  • arm: Ordered factor of study arms with visits included. min(arm) indicates the control arm.

  • i: integer ID of each patient.

  • j: integer ID of each clinical visit. j == 1 at baseline.

  • k: integer ID of the study arm of patient i. k == 1 for the control arm.

  • y: clinical outcomes.

  • t: observed continuous time since baseline.

  • beta: the scalar component of the treatment effect parameter beta defined for patient i.

  • mu: expected clinical outcome at the given patient visit.

  • w_*: columns of the covariate adjustment model matrix W.

  • e: residuals.

See also

Other simulations: pmrm_simulate_decline_nonproportional(), pmrm_simulate_decline_proportional(), pmrm_simulate_slowing_proportional()

Examples

  pmrm_simulate_slowing_nonproportional()
#> # A tibble: 1,500 × 11
#>    patient   visit   arm       i     j     k      y     t  beta    mu       e
#>    <chr>     <ord>   <ord> <int> <int> <int>  <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 patient_1 visit_1 arm_1     1     1     1 -1.09      0   0   0     -1.09  
#>  2 patient_1 visit_2 arm_1     1     2     1  0.873     1   0   0.693  0.180 
#>  3 patient_1 visit_3 arm_1     1     3     1  1.06      2   0   1.10  -0.0359
#>  4 patient_1 visit_4 arm_1     1     4     1  2.01      3   0   1.39   0.620 
#>  5 patient_1 visit_5 arm_1     1     5     1  1.50      4   0   1.61  -0.114 
#>  6 patient_2 visit_1 arm_2     2     1     2  0.379     0   0   0      0.379 
#>  7 patient_2 visit_2 arm_2     2     2     2  2.36      1   0.2 0.575  1.78  
#>  8 patient_2 visit_3 arm_2     2     3     2  0.843     2   0.2 0.963 -0.120 
#>  9 patient_2 visit_4 arm_2     2     4     2  0.871     3   0.2 1.22  -0.351 
#> 10 patient_2 visit_5 arm_2     2     5     2 -0.325     4   0.2 1.43  -1.76  
#> # ℹ 1,490 more rows