New statistical method predicts glaucoma progression more accurately

This paper in the March issue of the Journal of Glaucoma introduces a new statistical method for predicting rates of change in glaucomatous defects called empirical Bayes estimates of best linear unbiased predictions (BLUPs), which incorporates population average rates of progression. The authors compared its performance at predicting visual fields with that of ordinary least squares (OLS) regression and found that BLUPs performed significantly better.

Using visual field data from 368 patients (643 eyes) followed for an average of 6.5years, they calculated that the mean square error was significantly higher for OLS, which considers only the measurements of an individual patient.

The superior performance of BLUP was most notable in eyes classified as moderate or fast progressors.  

The authors conclude that the use of BLUP estimates should be considered when evaluating rates of functional change in glaucoma and predicting future impairment from the disease. Its use could aid in managing patients and determining how aggressively to treat them.

They say that in clinical trials comparing the effect of different interventions on the rate of disease progression, the use of BLUP estimates would allow more precise and accurate evaluation of rates of change, potentially decreasing the need to collect additional data and reducing research costs.

BLUP methodology also could be extended to the estimation of rates of change measured by other functional tests, such as frequency doubling technology or short-wavelength automated perimetry.