Special issue introduction: Statistical Methods in Precision Medicine: Diagnostic, Prognostic, Predictive and Therapeutic

We are delighted to offer this special issue of Biostatistics & Epidemiology on ‘Statistical Methods in Precision Medicine: Diagnostic, Prognostic, Predictive and Therapeutic.’ Precision medicine, often referred to as personalized medicine, has a relatively short history but presents great opportunities and challenges. As former US Health and Human Services Secretary Michael Leavitt said in a 2007 meeting of the Personalized Medicine Coalition, advances in science and technology present an unprecedented ‘opportunity to bring health care to a new level of effectiveness and safety’ [1]. In particular, recent advances have been made in omicsbased in vitro measurements [2–4], quantitative imaging biomarkers [5], artificial intelligence/ machine learning [6], and electronic health record keeping [7]. These advances and others have led to a surge in medical research activity into personalized medicine, which has been described as ‘providing the right drug for the right patient at the right time’ [8]. As a result, the potential has never been greater to obtain powerful information for individualizing medical decision making, including but not limited to information on diagnosis, prognosis, and treatment selection, and for predicting dose, monitoring disease, modifying behavior, and aiding the development of a tailored therapy, that is, a drug or a medical device [9, 10]. The recognition that advances in science, technology, mathematics, and data collection could revolutionize healthcare has led to many important government initiatives. In 2015, the US launched the Precision Medicine Initiative (PMI), with the mission ‘to enable a new era of medicine through research, technology, and policies that empower patients, researchers, and providers to work together toward development of individualized care.’ This announcement was followed by the 21st Century Cures Act [11], which provided funding for PMI to drive research into the genetic, lifestyle and environmental variations of disease. Prior to PMI, the US Food and Drug Administration (FDA) had already made personalized medicine a top priority, issuing the discussion paper Paving the Way for Personalized Medicine: FDA’s Role in a New Era of Medical Product Development [12]. The FDA and the National Institutes of Health (NIH) published a working glossary of terminology for Biomarkers, EndpointS, and other Tools (BEST) [13]. The European Union Council [14] provided discussions on personalizedmedicine, including a formal definition. The EuropeanMedicinesAgency (EMA) provided a perspective on pharmacogenomic information in drug labeling [15]. The first goal of EMA’s vision of Regulatory Science Strategy to 2025 [16] is ‘Catalysing the integration of science and technology in medicines development,’ under which the first core recommendation is to ‘support developments in precision medicine, biomarkers and omics’. These are just a few selected examples of regulatory efforts being made across the globe to facilitate the promise of precision medicine. Many of the success stories in precision medicine have involved the discovery of a single biomarker or a set of biomarker variants understood to have pharmacokinetic effects (drug metabolism) or pharmacodynamic effects (drug target) [17]. Genetic polymorphisms in cytochrome P450 (CYP) enzymes (e.g. CYP 2D6 and 2C19) are well-understood to be