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Simulate longitudinal data (serodynamics trajectory)

Source: R/simulate_multi_b_long.R
simulate_multi_b_long.Rd

simulate_multi_b_long() simulates subject-level latent parameters with a Kronecker covariance \(\Sigma_B \otimes \Sigma_P\) (5 parameters per biomarker), then generates noisy observations on a time grid. The expected trajectory is computed directly using serodynamics::ab().

Usage

simulate_multi_b_long(
  n_id,
  n_blocks,
  time_grid,
  sigma_p,
  sigma_b,
  mu_latent_base = c(log(1), log(5), log(30), log(0.02), log(1.5)),
  meas_sd = rep(0.22, n_blocks)
)

Arguments

n_id

Integer. Number of individuals to simulate.

n_blocks

Integer. Number of biomarkers (blocks).

time_grid

Numeric vector of observation times (days).

sigma_p

5×5 covariance matrix for within-biomarker parameters.

sigma_b

n_blocks×n_blocks covariance across biomarkers.

mu_latent_base

Numeric length-5 vector of means for the latent parameters (on log scale) per biomarker, in the order (log y0, log(y1 - y0), log t1, log alpha, log(shape-1)).

meas_sd

Numeric. Measurement error SD(s) on the log scale; either a single value recycled to all biomarkers or a length-n_blocks vector.

Value

A list with:

  • data: tibble with columns Subject, visit_num, antigen_iso, time_days, value.

  • truth: list with m_true, sigma_p, sigma_b, sigma_total, meas_sd, and theta_latent.

Author

Kwan Ho Lee

Examples

set.seed(925)

# Define dimensions and covariances
n_blocks  <- 2
time_grid <- c(0, 7, 14, 30)

sd_p <- c(0.35, 0.45, 0.25, 0.30, 0.25)
R_p  <- matrix(0.25, 5, 5)
diag(R_p) <- 1
sigma_p <- diag(sd_p) %*% R_p %*% diag(sd_p)

R_b <- matrix(c(1, 0.5,
                0.5, 1), n_blocks, n_blocks, byrow = TRUE)
sd_b   <- rep(0.6, n_blocks)
sigma_b <- diag(sd_b) %*% R_b %*% diag(sd_b)

# Use default serodynamics trajectory 
sim <- simulate_multi_b_long(
  n_id      = 5,
  n_blocks  = n_blocks,
  time_grid = time_grid,
  sigma_p   = sigma_p,
  sigma_b   = sigma_b
)

head(sim$data)
#> # A tibble: 6 × 5
#>   Subject visit_num antigen_iso time_days value
#>   <chr>       <int> <chr>           <dbl> <dbl>
#> 1 1               1 bm1                 0 0.850
#> 2 1               2 bm1                 7 1.37 
#> 3 1               3 bm1                14 1.50 
#> 4 1               4 bm1                30 3.10 
#> 5 1               1 bm2                 0 0.692
#> 6 1               2 bm2                 7 1.36