Randomized trials in oncology: A fertile ground for controversy

Markman, Maurie

doi:10.1007/s11912-003-0014-x

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…A virtual control arm would strengthen phase II or accelerated approval studies that currently have no control arm[3], while avoiding ethical issues associated with placebo controls. [11] Clinical trials designed using a virtual control arm would also be more economical, as the control group is now a mathematical prediction and fewer participants will be required to enroll to complete the study, Additionally, current studies anticipate their accrual needs using the observed outcomes of historical groups of people perceived to be similar to the population of interest–a historical control group. A historical control group is not the same thing as the proposed virtual control method.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Discussionmentioning

confidence: 99%