Evaluation of Treatment When Center‐Specific Selection Criteria Vary with Respect to Patient Risk

• Published in: Biometrics Vol. 61; no. 4; pp. 942 - 949
• Main Authors: DeLong, Elizabeth R., Coombs, Laura P., Ferguson, T. Bruce, Peterson, Eric D.
• Format: Journal Article
• Language: English
• Published: 350 Main Street , Malden , MA 02148 , U.S.A , and P.O. Box 1354, 9600 Garsington Road , Oxford OX4 2DQ , U.K Blackwell Publishing 01.12.2005; International Biometric Society; Blackwell Publishing Ltd
• Subjects: biometry; Biostatistics; Center effects; Clinical outcomes; clinical trials; Cluster analysis; Clustering; Computer Simulation; Confounding; Confounding Factors (Epidemiology); Coronary Artery Bypass; Data Interpretation, Statistical; Dichotomous outcomes; Health care; Hierarchical logistic regression; Humans; Mixed model; Multicenter Studies as Topic - methods; Patient risk; Patient Selection; Patients; Quality of service; Research methodology; Respiration, Artificial; risk; Risk Adjustment; surgery; Treatment Outcome
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/j.1541-0420.2005.00358.x

Many standards of medical care are based on the demonstrated effects of various treatment strategies or processes. Unlike pharmacological treatments, these strategies or processes are not necessarily subjected to rigorous clinical trials and their benefit is frequently assessed from observational data. For evaluating the influence of such medical processes on patient outcomes, not only is risk adjustment an issue, but also the “center effect” represents an important, often overlooked consideration. Both the quality of care and the tendency to use certain treatments or processes vary from one center to another. The induced similarity in outcomes within center, as well as the potential for confounding by center, needs to be addressed within the context of risk adjustment. In addition, center‐specific selection criteria for a treatment strategy can vary with respect to patient risk. Because of these considerations, it is important to adequately separate the within‐center effects of the treatment or strategy from the across‐center effects, which relate more to center performance. The primary objective of this article is to explore and extend current methods of dealing with center confounding for dichotomous outcomes, primarily for the situation where selection on the basis of patient risk can vary from center to center. A simulation study compares results from several different analytic methods and provides evidence for the importance of considering confounding due to both risk and center when evaluating the effectiveness of a process. An example that examines the effect of early extubation after bypass surgery is also presented.