Robust indices of clinical data: meaningless means.

The recent study by Asman et al. 1 demonstrated the practical limitations of a method commonly used to identify the general height (85th percentile value) of visual field sensitivity. In that paper the methods simulated outcomes by substituting within normal regions of the visual field, obtained from a large sample (n 82) of normal observers, a zone of abnormality, whose features were derived individually from a large group (n 123) of patients having glaucoma, to yield a synthetic abnormal field. The outcomes derived from this synthetic field were then compared to those of the normal field before corruption. These simulations showed that the presence of a local scotoma results in an underestimate of the general height (overestimate of the mean defect; MD) with the magnitude of error being related to the size of the scotoma (number of involved points). Although the average effect on MD was small (range, 0.2 to 2.3 dB), it produced a substantial corruption of the pattern defect index and its associated probability scales, frustrating the detection of progression. The authors 1 conclude that improved methods are needed for describing the general height or sensitivity of the visual field. In this article we describe and evaluate two candidate methods that can be applied for such purposes. Our logic stems from the fact that one of the challenges in clinical science is to identify normal signals given the presence of abnormality or noise. In perimetry, the distribution of outcomes for the dependent variable (in this case decibels) can be