Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Oct;22(10):e456-e465.
doi: 10.1016/S1470-2045(21)00488-5.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology

Rifaquat Rahman  1 , Steffen Ventz  2 , Jon McDunn  3 , Bill Louv  3 , Irmarie Reyes-Rivera  4 , Mei-Yin C Polley  5 , Fahar Merchant  6 , Lauren E Abrey  7 , Joshua E Allen  8 , Laura K Aguilar  9 , Estuardo Aguilar-Cordova  9 , David Arons  10 , Kirk Tanner  10 , Stephen Bagley  11 , Mustafa Khasraw  12 , Timothy Cloughesy  13 , Patrick Y Wen  14 , Brian M Alexander  15 , Lorenzo Trippa  2
Affiliations
Review

Leveraging external data in the design and analysis of clinical trials in neuro-oncology

Rifaquat Rahman et al. Lancet Oncol. 2021 Oct.

Abstract

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests RR received research support from the Project Data Sphere, outside of submitted work. IR-R reports employment and owns stocks of Roche and Genentech. FM reports employment at Medicenna Therapeutics. LEA reports employment and owns stocks of Novartis. JEA reports employment and owns stocks of Chimerix. LKA and EA-C report employment at Candel Therapuetics. SB reports grants and personal fees from Novocure; grants from Incyte, GSK, and Eli Lilly; and personal fees from Bayer and Sumitomo Dainippon. MK reports personal fees from Ipsen, Pfizer, Roche, and Jackson Laboratory for Genomic Medicine and research funding paid to his institution from Specialised Therapeutics. TC reports personal fees from Roche, Trizel, Medscape, Bayer, Amgen, Odonate Therapeutics, Pascal Biosciences, DelMar Pharmaceuticals, Tocagen, Karyopharm, GW Pharmaceuticals, Kiyatec, AbbVie, Boehinger Ingelheim, VBI Vaccines, Dicephera, VBL Therapeutics, Agios, Merck, Genocea, Puma, Lilly, Bristol Myers Squibb, Cortice, Wellcome Trust; and stock options from Notable Labs. TC has a patent (62/819,322) with royalties paid to Katmai and is a board member for the 501c3 Global Coalition for Adaptive Research. PYW reports personal fees from Abbvie, Agios, AstraZeneca, Blue Earth Diagnostics, Eli Lilly, Genentech, Roche, Immunomic Therapeutics, Kadmon, Kiyatec, Merck, Puma, Vascular Biogenics, Taiho, Tocagen, Deciphera, and VBI Vaccines; and research support from Agios, AstraZeneca, Beigene, Eli Lily, Genentech, Roche, Karyopharm, Kazia, MediciNova, Merck, Novartis, Oncoceutics, Sanofi-Aventis, and VBI Vaccines. BMA reports employment at Foundation Medicine; personal fees from AbbVie, Bristol Myers Squibb, Precision Health Economics, and Schlesinger Associates; and research support from Puma, Eli Lilly, Celgene. SV, JM, BL, M-YCP, DA, KT, and LT declare no competing interests.

Figures

Figure 1:
Figure 1:
Schematic representation of clinical trial designs. (A) A clinical study with patients enrollment to a single experimental arm and an external control arm (ECA-SAT). Adjustment methods are used to compare the experimental arm and the external control arm. (B) An example of a two-staged hybrid randomized trial design. (C) An example of a randomized trial design that utilizes external data for interim futility analyses. The external dataset is used to support the decision to continue or discontinue the clinical study. If the trial is not discontinued for futility, the final analysis does not utilize external data.
Figure 1:
Figure 1:
Schematic representation of clinical trial designs. (A) A clinical study with patients enrollment to a single experimental arm and an external control arm (ECA-SAT). Adjustment methods are used to compare the experimental arm and the external control arm. (B) An example of a two-staged hybrid randomized trial design. (C) An example of a randomized trial design that utilizes external data for interim futility analyses. The external dataset is used to support the decision to continue or discontinue the clinical study. If the trial is not discontinued for futility, the final analysis does not utilize external data.
Figure 1:
Figure 1:
Schematic representation of clinical trial designs. (A) A clinical study with patients enrollment to a single experimental arm and an external control arm (ECA-SAT). Adjustment methods are used to compare the experimental arm and the external control arm. (B) An example of a two-staged hybrid randomized trial design. (C) An example of a randomized trial design that utilizes external data for interim futility analyses. The external dataset is used to support the decision to continue or discontinue the clinical study. If the trial is not discontinued for futility, the final analysis does not utilize external data.
Figure 2:
Figure 2:
Schematic representation of a validation schema. A treatment effect estimate computed using only data from a previously completed RCT is compared to a second treatment effect estimate, computed using only the experimental arm of the same RCT and external control data.
See this image and copyright information in PMC

References

    1. Hwang TJ, Carpenter D, Lauffenburger JC, Wang B, Franklin JM, Kesselheim AS. Failure of Investigational Drugs in Late-Stage Clinical Development and Publication of Trial Results. JAMA Intern Med 2016; 176: 1826–33. - PubMed
    1. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics 2019; 20: 273–86. - PMC - PubMed
    1. Hu M, Yang M, Liu Y. Statistical adaptation to oncology drug development evolution. Contemp Clin Trials 2020; 99: 106180. - PubMed
    1. Mandrekar SJ, Sargent DJ. Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J Clin Oncol 2009; 27: 4027–34. - PMC - PubMed
    1. Alexander BM, Cloughesy TF. Adult Glioblastoma. J Clin Oncol 2017; 35: 2402–9. - PubMed

Publication types

Substances