title: A geometric approach to the analysis of physiological flow data.
abstract: Physiological flow data are common in various medical fields. Examples include urinary, blood and expiratory flows. They are widely used in assessing functions in the urinary, circulatory, or pulmonary systems, respectively. Current statistical methods for analysing these flow data in clinical trials are either univariate analyses, which do not utilize all the information together, or some conventional multivariate methods (such as regression analyses) which yield results that do not render clear medical interpretations. This paper presents a new approach to analysing the flow data, using urinary flow as the primary focus. The basic idea and technical steps are applicable to other flow data as well.  The proposed method aims to transform the flow measurements back to the shape of the flow graphs. Since the whole geometric pattern of the flow graph provides more information about the patient's flow condition than any individual flow parameter alone, the method is a meaningful way of combining and analysing the flow data in both statistical and clinical senses. The method is a three-stage procedure. Patients are classified into three classes in the first stage and then ranked in sequence in the second stage, according to the geometry of the shape pattern and some clinical criteria. The classification procedure is shown to be very reliable when compared with the clinician's visual evaluation, and hence can be implemented by computer programming to aid clinical trials involving many patients. The whole ranking score is then readily analysed at the third stage for comparing treatment effects by the analysis of covariance method based on ranks, with the post-treatment score as the response variable and the baseline score as the covariate. An example of a urinary flow data set is provided to illustrate the use of the procedure.
authors.affiliation: Biostatistics and Research Data Systems, Merck Research Laboratories, Rahway, New Jersey 07065-0914.
authors.forename: W J
authors.initials: WJ
authors.lastname: Shih
mesh_terms: D000704:Analysis of Variance; D015982:Bias; D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D006801:Humans; D016014:Linear Models; D008297:Male; D008991:Monitoring, Physiologic; D011470:Prostatic Hyperplasia / Q000145:classification / Q000175:diagnosis / Q000628:therapy; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D016896:Treatment Outcome; D014563:Urodynamics