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

Jeffrey M Switchenko, Arielle L Heeke, Tony C Pan and William L Read

PLOS ONE, 2019, vol. 14, issue 9, 1-13

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.

Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221336 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 21336&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0221336

DOI: 10.1371/journal.pone.0221336

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().