title: An autoregressive model to detect steady-state departures of serial measures.
abstract: New measurement technologies enable the ophthalmologist to monitor serial parameters thought to characterize the disease status in patients with glaucoma or ocular hypertension. These parameters include quantitative visual field and optic nerve data in addition to intraocular pressure. The clinician faces a difficult problem of judging whether a significant shift or trend has emerged in the presence of variations within the patient, across time, within the testing procedure, or due to clinical factors peculiar to the patient. The problem is made more difficult because (a) visits rarely take place at evenly spaced, specified times; (b) there are likely to be patient-specific serial correlations; and (c) the frequency of measurements is likely to decrease over time. A new statistical procedure has been identified to deal with these three data-related problems for clinical studies.
authors.affiliation: Dr. P. T. Lavin at Boston Biostatistics, Inc., 1007 Chestnut Street, Newton Upper Falls, MA 02164, U.S.A.
authors.forename: P T
authors.initials: PT
authors.lastname: Lavin