Development of omics-based clinical tests for prognosis and therapy selection: the challenge of achieving statistical...

Many articles have been published in biomedical journals reporting on the development of prognostic and therapy-guiding biomarkers or predictors developed from high-dimensional data generated by omics technologies. Few of these tests have advanced to routine clinical use.

We discuss statistical issues in the development and evaluation of prognostic and therapy-guiding biomarkers and omics-based tests.

Concepts relevant to the development and evaluation of prognostic and therapy-guiding clinical tests are illustrated through discussion and examples. Some differences between statistical approaches for test evaluation and therapy evaluation are explained. The additional complexities introduced in the evaluation of omics-based tests are highlighted.

Distinctions are made between clinical validity of a test and clinical utility. To establish clinical utility for prognostic tests, it is explained why absolute risk should be evaluated in addition to relative risk measures. The critical role of an appropriate control group is emphasized for evaluation of therapy-guiding tests. Common pitfalls in the development and evaluation of tests generated from high-dimensional omics data such as model overfitting and inappropriate methods for test performance evaluation are explained, and proper approaches are suggested.

The cited references do not comprise an exhaustive list of useful references on this topic, and a systematic review of the literature was not performed. Instead, a few key points were highlighted and illustrated with examples drawn from the oncology literature.

Approaches for the development and statistical evaluation of clinical tests useful for predicting prognosis and selecting therapy differ from standard approaches for therapy evaluation. Proper evaluation requires an understanding of the clinical setting and what information is likely to influence clinical decisions. Specialized expertise relevant to building mathematical predictor models from high-dimensional data is helpful to avoid common pitfalls in the development and evaluation of omics-based tests.