. Author manuscript; available in PMC: 2012 Aug 15.

Published in final edited form as: Vision Res. 2011 Jun 16;51(16):1801–1810. doi: 10.1016/j.visres.2011.06.009

Table 6.

Nagelkerke pseudo-R² index for models that used the baseline value of each index to predict the probability of the participant being in the worst tertile of subsequent rate of change. Backwards-elimination logistic regression was used as outlined in the Methods section. The indices labeled ‘cap’ are calculated after pointwise sensitivities were capped at the age-corrected normal value.

		Participants with GON		Participants without GON
Baseline Index:	Based on model from:	R² predicting tertile of slope of MD	R² predicting tertile of slope of same index	R² predicting tertile of slope of MD	R² predicting tertile of slope of same index
MD		0.33	0.33	0.31	0.31
LMS	Linear-scaled mean	0.34	0.35	0.29	0.26
Count_G	Garway-Heath	0.35	0.04	0.28	0.22
Count_Ha	Harwerth	0.35	0.22	0.28	0.07
Loss_Ho	Hood	0.34	0.36	0.28	0.27
Count_D	Drasdo	0.34	0.18	0.26	0.28
Count_DA	Drasdo (age corrected)	0.34	0.12	0.26	0.29
LMS (cap)	Linear-scaled mean	0.31	0.18	0.31	0.50
Count_G (cap)	Garway-Heath	0.29	0.09	0.30	0.10
Count_Ha (cap)	Harwerth	0.30	0.21	0.31	0.19
Loss_Ho (cap)	Hood	0.34	0.34	0.34	0.65
Count_D (cap)	Drasdo	0.28	0.04	0.24	0.29
Count_DA (cap)	Drasdo (age-corrected)	0.28	0.33	0.31	0.29