New tricks with old dogs: personalised medicine and clinical trials

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

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.

Personalized medicine: a competitor or an upgrade of evidence-based medicine?

Evidence-based medicine is a method of healthcare decision-making that intends to combine the most reliable scientific information with individual expertise and patient preferences in order to offer the optimal diagnostic and therapeutic option for the patient. In recent years, the term personalized medicine has been introduced to represent an approach considering differences among individual patients. In modern medicine the most important sources of evidence are clinical trials using epidemiological methods, and molecular biological and genetic methods characterizing individual patients. Responses to a certain therapeutic intervention differ among patients for several reasons. Identifying benefits and harms of an intervention can be handled by two approaches. The first is a statistical approach using the rule of large numbers, resulting in statistically meaningful conclusions. The other approach is personalized: the conclusions are valid for individual patients or subgroups identified by well-defined markers. Whether the evidence is statistical deriving from a large number of clinical observations or personalized based on molecular studies - it should be scientifically sound to apply in clinical practice. Confronting evidence-based medicine with personalized medicine would be justified only if the former is misinterpreted and restricted only to the use of randomized trials and their systematic reviews. The practice of personalized medicine is also based on evidence, and in many instances evidence from molecular research is also statistical in nature. Regarding healthcare decision about an individual patient, whether using evidence from randomized trials or from molecular studies of biomarkers, we have to base our decisions on reliable, good quality evidence. Evidence from molecular and genetic medicine thus improves the armament of evidence-based medicine, and this upgrade yields a more reliable support for our decisions in everyday practice.