Introduction to rdborrow

Overview

rdborrow implements causal inference methods for incorporating external controls in randomized controlled trials (RCTs) with longitudinal outcomes. The package provides tools for both analysis and simulation, enabling researchers to evaluate different borrowing strategies and design trials that leverage external data.

The methods are motivated by the SUNFISH trial for spinal muscular atrophy (SMA), where external controls from the olesoxime trial augment the randomized data to improve statistical efficiency.

Methods

Primary analysis (placebo-controlled phase)

These methods estimate the average treatment effect (ATE) during the placebo-controlled phase by borrowing from external controls.

Method	Function	Reference	Description
EC-IPW	`ec_ipw()`	Zhou et al. (2024b)	Inverse probability weighting with external control borrowing
EC-AIPW	`ec_aipw()`	Zhou et al. (2024b)	Augmented IPW (doubly robust)

Both methods support:

No borrowing (weight = 0): uses only RCT data
Optimal weight (weight = NULL): data-adaptive weight minimizing variance
Fixed weight (weight = 0.3): user-specified borrowing amount
Sandwich variance or bootstrap inference

See vignette("primary_analysis_workflow") for usage.

OLE phase analysis (open-label extension)

After the placebo-controlled phase, control subjects cross over to treatment. These methods estimate the long-term treatment effect using external controls who remain untreated.

Method	Function	Reference	Description
DID-EC-IPW	`did_ec_ipw()`	Zhou et al. (2024a)	Difference-in-differences with IPW
DID-EC-AIPW	`did_ec_aipw()`	Zhou et al. (2024a)	DID with augmented IPW
DID-EC-OR	`did_ec_or()`	Zhou et al. (2024a)	DID with outcome regression
SCM	`scm()`	Zhou et al. (2024a)	Synthetic control method

All OLE methods use bootstrap for inference.

See vignette("OLE_analysis_workflow") for usage.

Simulation

The simulation module evaluates estimator performance via Monte Carlo:

simulate_trial() generates synthetic trial + external control data
setup_simulation_primary() / setup_simulation_OLE() configure simulations
run_simulation() runs Monte Carlo experiments and reports bias, variance, MSE, coverage, power

See vignette("primary_simulation_workflow") and vignette("OLE_simulation_workflow") for usage.

Getting help

The help pages for run_analysis() and run_simulation() list all available methods and link to their documentation:

?run_analysis
?run_simulation

Quick start

# create an EC-IPW method with optimal borrowing weight
method <- ec_ipw(ps_formula = "S ~ x1 + x2 + x3 + x4 + x5")

# set up the analysis
analysis <- setup_analysis_primary(
  data = SyntheticData,
  trial_status_col_name = "S",
  treatment_col_name = "A",
  outcome_col_name = c("y1", "y2"),
  covariates_col_name = c("x1", "x2", "x3", "x4", "x5"),
  method_weighting_obj = method
)

# run
results <- run_analysis(analysis)
results

## $results
##      point_estimates standard_deviation lower_CI_normal upper_CI_normal
## tau1      -0.1971969          0.5134018       -1.203446        0.809052
## tau2       0.4697209          0.5410007       -0.590621        1.530063
## 
## $borrow_weight
## [1] 0.1475196

References

Zhou X, Zhu J, Drake C, Pang H (2024). “Causal estimators for incorporating external controls in randomized trials with longitudinal outcomes.” Journal of the Royal Statistical Society Series A: Statistics in Society. doi: 10.1093/jrsssa/qnae075.
Zhou X, Pang H, Drake C, Burger HU, Zhu J (2024). “Estimating treatment effect in randomized trial after control to treatment crossover using external controls.” Journal of Biopharmaceutical Statistics. doi: 10.1080/10543406.2024.2444222.
Shi L, Pang H, Chen C, Zhu J (2025). “rdborrow: an R package for causal inference incorporating external controls in randomized controlled trials with longitudinal outcomes.” Journal of Biopharmaceutical Statistics, 35(6), 1043-1066. doi: 10.1080/10543406.2025.2489283.