Big Data Analytics

Abstract

It is not uncommon to read that long-held beliefs about medical treatments have been dislodged by new studies. For example, there is now doubt as to whether women should undergo annual mammograms, previously a cornerstone of cancer screening. Hormone replacement therapy for menopausal women, once considered highly suspect in light of worrisome research findings, is now being reconsidered as a beneficial therapy. These reversals trouble and confuse many Americans. This Article explores why medical research findings can be erroneous and what can go wrong in the process of designing and conducting research studies. It provides readers with essential analytical tools and scientific vocabulary. The challenges of medical research include data quality deficiencies; selection, confounding, measurement, and confirmation biases; inadequate sample sizes; sampling errors; effect modifiers; and causal interactions, among others. All of these can cause researchers to mistake mere associations for causal relationships and to reach conclusions that are invalid and cannot be replicated in subsequent studies. Erroneous research findings can mislead legislators, regulators, and lawyers who use them for purposes of policy-making or litigation. Thus, understanding the pitfalls of big data analysis is important not only for scientists but also for anyone working with or reading about research studies, that is, for attorneys, health policy professionals, and the public at large.