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Summary

This study introduces the rdborrow R package for clinical trials, enabling external control borrowing to enhance validity and power in randomized controlled trials (RCTs). It offers causal inference methods for analyzing longitudinal outcomes with limited sample sizes.

Keywords:
Causal inferenceRclinical trial designexternal controlslongitudinal outcome

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Causal Inference

Background:

  • Randomized controlled trials (RCTs) are crucial for treatment evaluation but face challenges like limited sample sizes and crossover effects.
  • Incorporating external data sources offers a potential solution to improve RCT design and analysis.
  • Causal inference frameworks provide robust methods for utilizing external control data.

Purpose of the Study:

  • To introduce the rdborrow R package for facilitating the design and analysis of clinical trials.
  • To implement external control borrowing methods within a causal inference framework for longitudinal outcomes.
  • To provide tools for simulation, performance evaluation, and power analysis in RCTs.

Main Methods:

  • The rdborrow package implements weighting, difference-in-differences, and synthetic control methods for external control borrowing.
  • A Simulation module allows for data simulation, study design, estimator performance evaluation, and power analysis.
  • The package utilizes reproducible code examples with simulated data mimicking real-world scenarios.

Main Results:

  • The rdborrow package offers practical implementation of advanced external control borrowing techniques.
  • The simulation module aids in optimizing study design and assessing the performance of different analytical approaches.
  • The causal inference framework ensures robust analysis of longitudinal outcomes when incorporating external data.

Conclusions:

  • The rdborrow R package enhances the design and analysis of randomized controlled trials by enabling effective external control borrowing.
  • The implemented causal inference methods and simulation tools address key challenges in clinical development, particularly with limited sample sizes.
  • This package provides researchers with a valuable resource for improving the efficiency and validity of clinical trial research.