Leveraging external data in the analysis of randomized controlled trials: a comparative analysis

TL;DR

This paper reviews and compares statistical methods for integrating external data into randomized controlled trial analysis, highlighting trade-offs in bias control, power, and validity through simulations and real glioblastoma datasets.

Contribution

It provides a comprehensive comparison of methods for leveraging external data in clinical trials, offering practical recommendations for glioblastoma research.

Findings

Propensity score and random effects methods have distinct bias and efficiency trade-offs.

Simulation studies reveal varying control of false positives and power among methods.

Real data application demonstrates practical implications for glioblastoma trials.

Abstract

The use of patient-level information from previous studies, registries, and other external datasets can support the analysis of single-arm and randomized controlled trials to evaluate and test experimental treatments. However, the integration of external data in the analysis of clinical trials can also compromise the scientific validity of the results due to selection bias, study-to-study differences, unmeasured confounding, and other distortion mechanisms. Therefore, leveraging external data in the analysis of a clinical trial requires the use of appropriate methods that can detect, prevent or mitigate the risks of bias and potential distortion mechanisms. We review several methods that allow investigators to leverage external datasets, such as propensity score procedures and random effects modeling. Different methods present distinct trade-offs between risks and efficiencies. We conduct a comparative analysis of statistical methods to leverage external data and analyze randomized controlled trials. Multiple operating characteristics are discussed, such as the control of false positive results, power, and the bias of the treatment effect estimates, across candidate statistical methods. We compare the statistical methods through a broad set of simulation scenarios. We then compare the methods using a collection of datasets with individual patient-level information from several glioblastoma studies in order to provide recommendations for future glioblastoma trials.