Analytical Methods for Comparing Uncontrolled Trials With External Controls From Real-World Data: A Systematic Literature Review and Comparison With European Regulatory and Health Technology Assessment Practice.

Abstract

Objectives: This study aimed to provide an overview of analytical methods in scientific literature for comparing uncontrolled medicine trials with external controls from individual patient data real-world data (IPD-RWD) and to compare these methods with recommendations made in guidelines from European regulatory and health technology assessment (HTA) organizations and with their evaluations described in assessment reports.

Methods: A systematic literature review (until March 1, 2023) in PubMed and Connected Papers was performed to identify analytical methods for comparing uncontrolled trials with external controls from IPD-RWD. These methods were compared descriptively with methods recommended in method guidelines and encountered in assessment reports of the European Medicines Agency (2015-2020) and 4 European HTA organizations (2015-2023).

Results: Thirty-four identified scientific articles described analytical methods for comparing uncontrolled trial data with IPD-RWD-based external controls. The various methods covered controlling for confounding and/or dependent censoring, correction for missing data, and analytical comparative modeling methods. Seven guidelines also focused on research design, RWD quality, and transparency aspects, and 4 of those recommended analytical methods for comparisons with IPD-RWD. The methods discussed in regulatory (n = 15) and HTA (n = 35) assessment reports were often based on aggregate data and lacked transparency owing to the few details provided.

Conclusions: Literature and guidelines suggest a methodological approach to comparing uncontrolled trials with external controls from IPD-RWD similar to target trial emulation, using state-of-the-art methods. External controls supporting regulatory and HTA decision making were rarely in line with this approach. Twelve recommendations are proposed to improve the quality and acceptability of these methods.