Identifying appropriate external control datasets in support of future glioblastoma clinical trials leveraging external data.

Abstract

Background: Recent interest in leveraging external data for clinical trial design and analysis in glioblastoma has raised questions on the identification of appropriate data to use as external controls for future trials. We perform a comprehensive analysis assessing candidate sources of external data and comparing clinical trial and real-world datasets in newly diagnosed glioblastoma.

Methods: Individual patient-level data (PLD) from several clinical trials, a large academic institutional database and a registry (National Cancer Database) were used for analysis of patients receiving standard of care radiation with concurrent and adjuvant temozolomide. Data summaries from randomized trials 2012-2022 were analyzed to account for trials without available PLD. Multivariable modeling was employed to compare survival across datasets.

Results: In total, 8 datasets with PLD for 3061 patients with newly diagnosed glioblastoma treated with standard chemoradiation were analyzed. Patients on trials were younger (age < 60:64% vs. 48%, p < 0.001) and had higher KPS (KPS≥90:58% vs. 48%, P < .001) compared to non-trial patients. Patients in clinical trials exhibited inferior survival relative to non-trial patients (HR 1.30,95%CI 1.13-1.48, P < .001) after adjustment for age, sex, KPS, extent of resection and MGMT methylation status. In assessment of data summaries of 19 randomized trials, there was no detectable time-trend toward improved outcomes 2012-2022.

Conclusions: In newly diagnosed glioblastoma patients treated with standard of care chemoradiation, there were significant differences between trial datasets and real-world datasets but no evidence of a trial effect benefit from trial participation. After adjustment of relevant covariates, there was no evidence of temporal drift of improved survival over the last decade.