Some Old and Some New Statistical Tools for Outcomes Research

O utcomes research "seeks to understand the end results of particular health care practices and interventions" 1 to inform the development of clinical practice guidelines, evaluate the quality of medical care, and foster effective interventions to improve the quality of care. 2 Although randomized trial designs have been used to assess quality of care and to identify effective interventions in the real world, 3,4 the empirical basis of outcomes research largely rests on data collected in the observational setting (eg, in the routine setting of everyday 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 National Cardiac Data Registry of the American College of Cardiology; the Implantable Cardioverter Defibrillator Registry launched jointly by the American College of Cardiology and the Heart Rhythm Society; the National Cardiac Surgery Database of the Society of Thoracic Surgeons; and 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 with multiple outcomes, and always with some missing data.The purpose of the present article is to review key statistical methods important to outcomes research and to introduce newer methodology.The article describes 4 methodological issues commonly present when observational data are analyzed; 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.Although the intent of the present report 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 2 goals are achieved, then both the rigor of and the scientific findings from outcomes research will be strengthened substantially.