Generate synthetic data from a real dataset

Creates a synthetic version of the input data that preserves marginal distributions and pairwise correlations while adding controlled noise for privacy protection.

Usage

synthesize(
  data,
  method = c("parametric", "bootstrap", "noise"),
  n = nrow(data),
  noise_level = 0.1,
  seed = NULL
)

Arguments

data

A data frame of real clinical data.

method

Synthesis method:

• "parametric" (default): fits Gaussian copula to continuous variables, multinomial to categorical. Fast, interpretable.

• "bootstrap": nonparametric resampling with optional noise.

• "noise": adds calibrated Laplace noise to each variable (differential privacy inspired).

n

Number of synthetic records. Default: same as input.

noise_level

For method = "noise": scale of Laplace noise relative to variable SD. Default 0.1.

seed

Random seed for reproducibility. If non-NULL, the global RNG state is saved before and restored after synthesis so that calling code is not affected.

Value

A synthetic_data object (list) with components: $synthetic (tibble of synthetic records), $real (tibble of the original data, retained for downstream validation), $method, $n_original, $n_synthetic, $variables.

Details

The parametric method uses a Gaussian copula approach: marginal distributions are estimated empirically and the joint dependence structure is captured via the correlation matrix of normal scores. This preserves both marginal shapes and pairwise associations while generating genuinely new observations.

References

Jordon J, et al. (2022). Synthetic Data – what, why and how? arXiv preprint arXiv:2205.03257. doi:10.48550/arXiv.2205.03257

Examples

set.seed(42)
real <- data.frame(
  age = rnorm(200, 65, 10),
  sbp = rnorm(200, 130, 20),
  sex = sample(c("M", "F"), 200, replace = TRUE),
  outcome = rbinom(200, 1, 0.3)
)
syn <- synthesize(real, method = "parametric", seed = 42)
syn
#> 
#> ── Synthetic data (parametric) 
#> 200 real -> 200 synthetic records
#> Variables: age, sbp, sex, outcome