A general framework for estimating volume-outcome associations from longitudinal data


Benjamin French, Department of Biostatistics and Epidemiology, University of Pennsylvania, 625 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, U.S.A.

E-mail: bcfrench@upenn.edu


Recently, there has been much interest in using volume-outcome data to establish causal associations between measures of surgical experience or quality and patient outcomes following a surgical procedure, such as coronary artery bypass graft, total hip replacement, and radical prostatectomy. However, there does not appear to be a standard approach to a volume-outcome analysis with respect to specifying a volume measure and selecting an estimation method. We establish the recurrent marked point process as a general framework from which to approach a longitudinal volume-outcome analysis and examine the statistical issues associated with using longitudinal data analysis methods to model aggregate volume-outcome data. We review assumptions to ensure that linear or generalized linear mixed models and generalized estimating equations provide valid estimates of the volume-outcome association. In addition, we provide theoretical and empirical evidence that bias may be introduced when an aggregate volume measure is used to address a scientific question regarding the effect of cumulative experience. We conclude with the recommendation that analysts carefully specify a volume measure that most accurately reflects their scientific question of interest and select an estimation method that is appropriate for their scientific context. Copyright © 2011 John Wiley & Sons, Ltd.