Almost daily, the media reports another health-related discovery. A study in a leading medical journal finds an association between certain genetic variants and increased risk of heart disease. Another suggests rethinking how bone density testing is used to diagnose and treat osteoporosis. A clinical trial shows no evidence that a new anti-platelet therapy reduces the incidence of death or serious health outcomes relative to an existing treatment in coronary artery disease patients. Three major studies suggest that exposure to air pollution may be implicated in stroke or cognitive decline.
Behind all of these findings? Biostatistics – the science of development of statistical theory and methods for application to data-driven challenges in the health and biological sciences. The classical tools of biostatistics – for design and analysis of randomized clinical trials, the gold standard approach for comparing treatments; for design and analysis of laboratory studies; for analyzing studies that follow large cohorts of patients over time; for “meta-analysis,” combining results of several studies in a principled way – have for decades provided the basis for learning from data while taking account of the inherent uncertainty.
And for decades, collaborations between biostatisticians and domain scientists have produced scores of now ubiquitous techniques. Type “Kaplan-Meier” into Google Scholar, and you’ll find over 200,000 hits in the medical literature. Paul Meier, an iconic clinical trials biostatistician, and his collaborator proposed a method for analyzing data in the form of a “time-to-an-event” – like death – that is “censored” (unobserved) for some subjects because they have not experienced the event by the end of the study or “drop out” and disappear, leaving their event times unrecorded. This one biostatistical advance, and extensions of it developed since, have aided countless biomedical breakthroughs. The landmark clinical trial assessing the benefit of AZT for reducing the risk of transmission of HIV from infected pregnant women to their infants was stopped six months early using these methods, altering the landscape of HIV prevention.
But what of the future? “Big Data” are here, and the challenges they pose for biomedical and biological science could not be more complex or exciting. For biostatisticians, they bring unprecedented opportunities to collaborate with the scientists generating the data to develop innovative new theory and methods to tackle problems never envisioned by the biostatisticians of yesterday. A list of emerging trends is as long as the emerging scientific challenges; here’s just a sample.