Skip to contents

Simulate Longitudinal PROM Data and a Binary Anchor

Source: R/simdat.R
simdat.Rd

simdat() simulates Time 1 and Time 2 item responses for a 10-item patient-reported outcome measure (PROM) and a binary anchor / transition rating from an item response theory context. Each item has four ordered response categories scored 0, 1, 2, and 3, so the total PROM score ranges from 0 to 30 at each time point.

Usage

simdat(
  N = 2000,
  mn_imic = 0.37425,
  sd_imic = 0.05,
  cor_t1_change = -0.5,
  mean_tetch = 0.3,
  sd_tetch = 1,
  rel_trt = 0.7,
  return_latent = TRUE,
  add_change = FALSE
)

Arguments

N

Integer. Sample size for simulation.

mn_imic

Numeric. Mean individual minimal important change (MIC) on the latent theta-change scale. For context, mn_imic = 0.5 corresponds approximately to a raw-score MIC of about 2.8 points, while mn_imic = 0.37425 corresponds approximately to a raw-score MIC of about 2.5 points on the 0-30 PROM scale.

sd_imic

Numeric. Standard deviation of individual MICs on the latent theta-change scale.

cor_t1_change

Numeric. Correlation between baseline theta and latent change.

mean_tetch

Numeric. Mean latent change.

sd_tetch

Numeric. Standard deviation of latent change.

rel_trt

Numeric. Target reliability of perceived change used to generate the binary anchor / transition rating.

return_latent

Logical. If TRUE, returns latent variables and item parameters in the output object.

add_change

Logical. If TRUE, adds change = score_t2 - score_t1 to the returned data frame. Defaults to FALSE.

Value

A list containing:

settings

Simulation settings.

item_names

Names of Time 1 items, Time 2 items, and anchor.

truth

Truth / diagnostic quantities, including target_rel_trt and observed_rel_trt.

datw

The simulated wide-format data frame.

If return_latent = TRUE, the output also includes item parameters, latent variables, perceived change, and individual MICs.

Details

The returned data frame includes item-level responses, the binary anchor trat, the Time 1 summed PROM score score_t1, and the Time 2 summed PROM score score_t2. If add_change = TRUE, the observed change score change = score_t2 - score_t1 is also added.

For reproducible simulations, call set.seed() before calling simdat().

The R code is adapted from supplementary materials of Terluin et al. Qual Life Res. 2024;33:963-973.

Examples

set.seed(123)
sim <- simdat(N = 200, add_change = TRUE)

names(sim$datw)
#>  [1] "item1"    "item2"    "item3"    "item4"    "item5"    "item6"   
#>  [7] "item7"    "item8"    "item9"    "item10"   "item1.1"  "item2.1" 
#> [13] "item3.1"  "item4.1"  "item5.1"  "item6.1"  "item7.1"  "item8.1" 
#> [19] "item9.1"  "item10.1" "trat"     "score_t1" "score_t2" "change"  
sim$truth
#> $target_rel_trt
#> [1] 0.7
#> 
#> $observed_rel_trt
#> [1] 0.7617496
#> 
#> $latent_mic
#> [1] 0.37425
#> 
#> $mean_individual_mic
#> [1] 0.3706512
#> 
#> $sd_individual_mic
#> [1] 0.05012383
#> 
#> $mean_theta_change
#> [1] 0.3
#> 
#> $sd_theta_change
#> [1] 1
#> 
#> $prop_improved
#> [1] 0.48
#> 
#> $cor_change_anchor
#> [1] 0.5584512
#> 
#> $cor_theta_t1_theta_t2
#> [1] 0.5164919
#> 
#> $cor_theta_change_anchor
#> [1] 0.6508751
#> 
#> $raw_score_range
#> [1]  0 30
#>