title: A general framework for estimating volume-outcome associations from longitudinal data.
abstract: 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.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, 625 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA. bcfrench@upenn.edu
authors.forename: Benjamin
authors.initials: B
authors.lastname: French
authors.forename: Farhood
authors.initials: F
authors.lastname: Farjah
authors.forename: David R
authors.initials: DR
authors.lastname: Flum
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Heagerty
mesh_terms: D015982:Bias; D002423:Cause of Death; D003198:Computer Simulation / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008137:Longitudinal Studies / Q000706:statistics & numerical data*; D008175:Lung Neoplasms / Q000401:mortality / Q000601:surgery; D008297:Male; D015233:Models, Statistical; D011183:Postoperative Complications / Q000401:mortality; D019616:Thoracic Surgical Procedures / Q000379:methods / Q000401:mortality; D016896:Treatment Outcome
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