Novel immunotherapies Lean On Old Methods For Statistical Challenges

By Natasa Rajicic

Immunotherapy has brought us many promises, most notably, of a future where humans are able to harness their body’s own ability to protect them from illness. Immuno-oncology (IO) may be the ultimate frontier of that future reality, with a promise of being able to help our bodies deflect or cure us of any malignancies. Today, these therapies include cell therapies, cancer vaccine, and T-cell–stimulating antibodies, with the field continuously expanding.

While medical science behind immune-oncology (IO) treatments is fascinating and expanding at a rapid pace, so too are the statistical challenges posed by the development of these agents. Efficacy of cancer therapies has been typically summarized and evaluated via log-rank tests and Cox PH models. As with any statistical tools, these standard approaches rely on conditions to be met in order for the methodology to work its way in the most efficient and appropriate manner. For example, as the name suggests, Cox Proportional Hazards models assume the hazard rates in the two study groups being compared are proportional, with their ratio constant over time. When plotted, the Kaplan-Meier curves of the two groups separate early with a gradually increasing separation over time. 

However, many drugs in the IO arena do not follow these well understood traditions, and so the standard statistical approaches to evaluating safety and efficacy may not always be optimal or efficient. IO drugs can exhibit all kinds of ‘inappropriate’ behavior such as late-onset toxicity, pseudo-progression, or delayed and lasting clinical activity (i.e., cure). IO drugs are therefore misbehaving, and we love them for it, but this also means we need to expand our usual analysis toolkit.