The use of a predictive statistical model to make a virtual control arm for a clinical trial

• Type of Material: Article
• Authors: Jeffrey Switchenko, Emory University; Arielle L. Heeke, Atrium Health; Tony C. Pan, Georgia Institute of Technology; William L. Read, Emory University
• Language: English
• Date: 2019-09-04
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2019 Switchenko et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0221336
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 14
• Issue: 9
• Start Page: e0221336
• End Page: e0221336
• Grant/Funding Information: This project was funded by Dr Read’s discretionary funds; and in part by the Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University; and NIH/NCI under award number P30CA138292.
• Keywords: BREAST; Science & Technology - Other Topics; Multidisciplinary Sciences; Science & Technology
• Research Categories: Health Sciences, Oncology; Biology, Biostatistics

Abstract

Background: Randomized clinical trials compare participants receiving an experimental intervention to participants receiving standard of care (SOC). If one could predict the outcome for participants receiving SOC, a trial could be designed where all participants received the experimental intervention, with the observed outcome of the experimental group compared to the prediction for those individuals. Methods: We used the CancerMath calculator to predict outcomes for participants in two large clinical trials of adjuvant chemotherapy for breast cancer: NSABPB15 and CALGB9344. NSABPB15 was the training set, and we used the modified algorithm to predict outcomes for two groups from CALGB9344: one which received standard of care (SOC) chemotherapy and one which received paclitaxel in addition. We made a prediction for each individual CALGB9344 participant, assuming each received only SOC. Results: The predicted outcome for the group which received only SOC matched what was observed in the CALGB9344 trial. In contrast, the predicted outcome for the group also receiving paclitaxel was significantly worse than what was observed for this group. This matches the conclusion of CALGB9344 that adding paclitaxel to SOC improves survival. Conclusion: This project proves that a statistical model can predict the outcome of clinical trial participants treated with SOC. In some circumstances, a predictive model could be used instead of a control arm, allowing all participants to receive experimental treatment. Predictive models for cancer and other diseases could be constructed using the vast amount of outcomes data available to the federal government, and made available for public use.