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Causal Inference From Observational Data: New Guidance From Pulmonary, Critical Care, and Sleep Journals.

Critical Care Medicine www.ccmjournal.org 1 This month’s issue of Annals of the ATS features a special article by a group of editors from top pulmonary, sleep, and critical care journals—including Critical Care Medicine—that provides guidance for authors of observational studies (1). Spurred in part by a perceived uptick in the number of submissions of this type, the article aims to build common ground for both authors and readers alike, encouraging the use of modern statistical methods, the transparent reporting of results, and a measured interpretation of findings. The guidance does not apply to observational studies that are purely descriptive, that identify associations only, or that develop predictive models, but rather is intended specifically for those that focus on causal inference. Although Critical Care Medicine endorses the article’s main principles—outlined below and unpacked more fully in the article itself—they are not intended to be strict requirements; as with any submission, observational studies are considered individually on a case-by-case basis and proceed through the editorial process based on their merits. Why generate this document now? Observational studies in critical care are on the rise. A cursory Pubmed search suggests that their ranks have increased in the last 5 years and that as of 2015, these outnumber studies identified as randomized controlled trials (Fig. 1). A few key factors may account for this trend. First, a dearth of actionable evidence from interventional trials—most of which are costly and difficult to carry out—has in some circles cast observational studies in a new and favourable light (2). Although they generate a different class of evidence, observational studies may be cheaper, easier, and faster to conduct than interventional ones, especially when retrospective datasets are used. There are also cases where randomization poses ethical challenges, where the event of interest is rare, where the research question itself pertains specifically to real-world practices, or where the effects of long exposures are being examined, in which observational studies may be the design of choice. Second, the proliferation of new sources of data, including electronic medical records, patient registries, and administrative databases, ensure that fodder for observational studies is now in ample supply. Secular trends encouraging data sharing and open access have also made such datasets more readily available to researchers, including those who were not involved in the data collection itself. One illustrative example of this phenomenon is the Medical Information Mart for Intensive Care (MIMIC) database, which includes granular data collected from tens of thousands of ICU admissions at a single center, and has been used as a data source for hundreds of publications (3). Third, the analytic tools used in observational studies, including current popular statistical programming languages, are readily available (and in some cases open source), with both the clinical and academic workforces becoming increasingly adept at using them.