Article first published online: 15 NOV 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 4, pages 366–382, 20 February 2012
How to Cite
French, B., Farjah, F., Flum, D. R. and Heagerty, P. J. (2012), A general framework for estimating volume-outcome associations from longitudinal data. Statist. Med., 31: 366–382. doi: 10.1002/sim.4410
- Issue published online: 17 JAN 2012
- Article first published online: 15 NOV 2011
- Manuscript Accepted: 24 AUG 2011
- Manuscript Received: 6 JUL 2010
- Estimating equations;
- health services research;
- informative cluster size;
- mixed models;
- surgeon experience
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.