. Author manuscript; available in PMC: 2014 Jan 1.

Published in final edited form as: J R Stat Soc Ser C Appl Stat. 2012 Oct 22;62(2):233–250. doi: 10.1111/j.1467-9876.2012.01057.x

Table 2.

Comparison of proportional odds logistic regression parameter estimates for the post-operative surgical complications data, full sample (n=102)

Effect	Approach	Estimate	SE	Z-statistic	P-value
Intercept (j ≥ 2)	Standard ML^a	2.436	1.072	2.27	0.023
	ML:Bias-Corrected	2.055	0.922	2.23	0.027
	Clogg et al.	2.363	1.038	2.28	0.023
Intercept (j = 3)	Standard ML^a	4.385	1.186	3.70	<0.001
	ML:Bias-Corrected	3.819	1.026	3.72	<0.001
	Clogg et al.	4.254	1.145	3.71	<0.001
Apgar 3–4	Standard ML^a	2.869	1.262	2.27	0.023
	ML:Bias-Corrected	2.440	1.147	2.13	0.034
	Clogg et al.	2.785	1.230	2.26	0.024
Apgar 5–6	Standard ML^a	1.156	1.155	1.00	0.317
	ML:Bias-Corrected	0.845	1.015	0.83	0.406
	Clogg et al.	1.110	1.122	0.99	0.323
Apgar 7–8	Standard ML^a	0.261	1.134	0.23	0.818
	ML:Bias-Corrected	−0.029	0.992	−0.03	0.977
	Clogg et al.	0.246	1.099	0.22	0.823
Pre-operative	Standard ML^a	0.390	0.550	0.71	0.478
Disease	ML:Bias-Corrected	0.376	0.534	0.70	0.482
	Clogg et al.	0.376	0.541	0.69	0.487

Standard ML is not bias-corrected, with convergence criterion: relative change in the log-likelihood between successive iterations is less than 0.000001