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Simulated Transportability Data

Source: R/data.R
transport_sim.Rd

A simulated dataset for demonstrating transportability analysis of prediction model performance. The data includes source (RCT) and target populations, where treatment is randomized in the source but confounded in the target.

Usage

transport_sim

Format

A data frame with 1500 rows and 7 variables:

age

Standardized age (mean 0, SD 1)

biomarker

Continuous biomarker value

smoking

Binary smoking status (1 = smoker, 0 = non-smoker)

source

Population indicator (1 = source/RCT, 0 = target). RCT patients tend to be younger and less likely to smoke.

treatment

Binary treatment indicator (1 = treated, 0 = untreated). Randomized (50/50) in source, confounded by age and biomarker in target.

event

Binary outcome indicating event (1 = event, 0 = no event). Risk depends on age, biomarker, smoking, and is reduced by treatment.

risk_score

Predicted probability of event from a model trained on the source population, using age, biomarker, and smoking as predictors.

Source

Simulated data based on the framework in Voter et al. (2025). "Transportability of machine learning-based counterfactual prediction models." Diagnostic and Prognostic Research, 9(4). doi:10.1186/s41512-025-00201-y

Details

The data generating process creates realistic heterogeneity between source (RCT) and target populations:

  • Selection into RCT: RCT patients are younger on average and less likely to be smokers, reflecting typical trial enrollment patterns.

  • Treatment assignment:

    • Source: Randomized with P(A=1) = 0.5

    • Target: Confounded with P(A=1|X) = plogis(-0.3 + 0.015*age + 0.2*biomarker)

  • Outcome model: P(Y=1|X,A) = plogis(-2.5 + 0.03*age + 0.4*biomarker + 0.3*smoking - 0.5*A)

This creates a scenario where naive performance estimates from the RCT will not generalize to the target population due to covariate shift.

Examples

data(transport_sim)
head(transport_sim)
#>           age   biomarker smoking source treatment event risk_score
#> 1  0.63916439 -0.06780891       1      1         0     0  0.3388230
#> 2  0.06014812  0.81999552       0      1         0     0  0.3309058
#> 3  0.78997359 -1.00222911       1      0         1     0  0.2773807
#> 4  1.28410328  0.78155960       1      0         1     0  0.4481046
#> 5  0.39198673  1.24382106       0      1         1     0  0.3841622
#> 6 -0.13627856  0.61176294       1      1         1     0  0.3482827

# Population sizes
table(transport_sim$source)  # 0=target, 1=source
#> 
#>    0    1 
#> 1224 1276 

# Estimate transportable MSE under no treatment
result <- tr_mse(
  predictions = transport_sim$risk_score,
  outcomes = transport_sim$event,
  treatment = transport_sim$treatment,
  source = transport_sim$source,
  covariates = transport_sim[, c("age", "biomarker", "smoking")],
  treatment_level = 0,
  analysis = "transport",
  estimator = "dr"
)
result
#> 
#> Counterfactual Transportable MSE Estimation
#> --------------------------------------------- 
#> Analysis: transport 
#> Estimator: dr 
#> Treatment level: 0 
#> N target: 1224  | N source: 1276 
#> 
#> Estimate: 0.2219 (SE: 0.0081 )
#> 95% CI: [0.2055, 0.2392]
#> 
#> Naive estimate: 0.2178 
#>