Generate sample data for propensity score examples

Produces a synthetic data frame that resembles a typical SAS export from a cardiac-surgery propensity score analysis. Propensity scores are generated from a logistic model driven by the supplied covariates, so the resulting prob_t column has realistic overlap and the SMD values are non-trivial.

Usage

sample_ps_data(n = 500L, seed = 42L, separation = 1)

Arguments

n

Number of patients per treatment arm. Total rows = 2 * n. Must be a positive integer. Default 500.

seed

Random seed for reproducibility. Default 42L. The fixed default gives the same demo dataset on every call. NULL draws from the caller's random-number stream, as ps_match() does: the data differ from call to call, and set.seed() before the call reproduces them. A negative seed gives the same data as its absolute value.

separation

Numeric scalar controlling how well the covariates separate the two groups. Higher values give less overlap. Default 1.0.

Value

A data frame with 2 * n rows and the following columns:

id

Integer patient identifier.

tavr

Binary treatment indicator: 0 = SAVR, 1 = TF-TAVR.

age

Continuous: age in years.

female

Binary: 1 = female.

ef

Continuous: ejection fraction (%).

diabetes

Binary: 1 = diabetic.

hypertension

Binary: 1 = hypertensive.

prob_t

Estimated propensity score (numeric, 0 to 1).

match

Match indicator, initialised to 0. Populated by ps_match().

See also

ps_match(), ps_weight()

Examples

dta <- sample_ps_data(n = 200, seed = 42)
head(dta)
#>   id tavr      age female       ef diabetes hypertension    prob_t match
#> 1  1    0 88.96767      0 51.95379        1            1 0.1112484     0
#> 2  2    0 73.48241      0 59.60242        0            1 0.5270896     0
#> 3  3    0 80.90503      1 52.38991        1            0 0.3951929     0
#> 4  4    0 83.06290      0 59.35072        0            1 0.2836606     0
#> 5  5    0 81.23415      1 50.53527        0            0 0.3751620     0
#> 6  6    0 77.15100      0 51.42113        0            0 0.3148051     0
table(dta$tavr)
#> 
#>   0   1 
#> 200 200 
hist(dta$prob_t, breaks = 20, main = "Propensity score distribution")