Some old and some new statistical tools for outcomes research.

Outcomes research “seeks to understand the end results of particular health care practices and interventions” in order to inform the development of clinical practice guidelines, to evaluate the quality of medical care, and to foster effective interventions to improve the quality of care. While randomized trial designs have been utilized to assess quality of care and to identify effective interventions in the real world, the empirical basis of outcomes research largely rests on data collected in the observational setting, e.g., in the routine setting of every day practice. With more emphasis placed on increasing the value of health care in terms of lives saved and morbidity avoided, outcomes researchers are making unprecedented demands of observational databases. This is evidenced by the increasing number of and participation in national registries. These include the American College of Cardiology’s (ACC) National Cardiac Data Registry; the Implantable Cardioverter Defibrillator Registry launched jointly by ACC and the Heart Rhythm Society; the Society of Thoracic Surgeons National Cardiac Surgery Database; the Interagency Registry of Mechanically Assisted Circulatory Support devices funded by the National Heart, Lung, and Blood Institute, the Centers for Medicare and Medicaid Services; and others. Empirical analyses of these databases require statistical tools that can handle the complexity of the data: observational, sometimes hierarchical, often multiple outcomes, and always some missing data. The purpose of this paper is to review key statistical methods important to outcomes research as well as to introduce newer methodology. The paper describes four methodological issues commonly present when analyzing observational data; summarizes the primary assumptions associated with strategies to handle these common problems; demonstrates methods to assess the plausibility of the assumptions associated with each strategy; and illustrates these concepts using examples from cardiovascular outcomes research. While the intent of this paper is not to provide a comprehensive summary of statistical approaches to data analysis, it is intended to provide a clear understanding of the assumptions associated with some common methodological tools and of the strategies used to assess their plausibility. If these two goals are achieved, then both the rigor and the scientific findings from outcomes research will be substantially strengthened. PMC ID: PMC2535854 (August 19, 2008)