How Useful Is Population Data for Informing Visual Field Progression Rate Estimation?

Abstract

PURPOSE: Bayesian estimators allow the frequency of visual field progression rates in the population (the prior distribution) to constrain rate estimates for individuals. We examined the benefits of a prior distribution accounting for one of progression's major risk factors--whether intraocular pressure is treated--to gauge the maximum benefit expected from developing priors for other glaucoma risk factors. METHODS: Our prior distribution was derived from published data from either treated (matched-prior condition) or untreated (unmatched-prior condition) glaucoma patients. We simulated MD values (6-monthly) with true underlying progression rates drawn from the same distribution as the prior for the matched-prior condition. We estimated rates through linear regression, and determined the likelihood of obtaining this estimate as a function of a range of true underlying progression rates (the likelihood function). The maximum likelihood estimate of rate was the most likely value of the posterior distribution (the product of the prior distribution and likelihood function). RESULTS: For short (4) visual field series, the matched-prior condition, unmatched-prior condition, and linear regression gave median errors (estimated minus true rate) of 0.02, 0.20, and 0.00 dB/y, respectively. Positive predictive values for determining rapidly progressing (<-1 dB/y) rates were 0.46, 0.42, and 0.38, with negative predictive values of 0.93, 0.94, and 0.95. For more extended series the magnitude of the differences between techniques decreased, although the order was unchanged. CONCLUSIONS: Performance shifts in bayesian estimators of visual field progression are modest even when prior distributions do not reflect large risk factors, such as IOP treatment.