Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

ABSTRACT:

Background

Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification.

Methods

Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating

SUBMITTER: Schell GJ 

PROVIDER: S-EPMC3878032 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature