Theoretical Biology and Medical Modelling

Table 3 The Akaike information criterion (AIC), Bayesian information criterion (BIC) and the estimates of the cumulative incidence function under competing risks based on different distributions with the non-parametric method.

From: A parametric method for cumulative incidence modeling with a new four-parameter log-logistic distribution

	Time (years)
Distribution	0.75	1	1.5	2	3	5	10	AIC	BIC
Two-parameter log-logistic								1894.0	1912.0
Live birth	0.1145	0.2317	0.4946	0.6857	0.8556	0.9307	0.9497
Stillborn fetus or abortion	0.0189	0.0246	0.0333	0.0375	0.0457	0.0514	0.0477
Four-parameter log-logistic								1685.3	1721.1
Live birth	0.0257	0.2373	0.5552	0.6949	0.8133	0.8876	0.9274
Stillborn fetus or abortion	0.0200	0.0278	0.0370	0.0419	0.0467	0.0503	0.0525
Two -parameter Weibull								2195.0	2212.0
Live birth	0.1942	0.2749	0.4292	0.5626	0.7532	0.9098	0.9472
Stillborn fetus or abortion	0.0173	0.0225	0.0310	0.0372	0.0457	0.0507	0.0526
Two -parameter Gompertz								2299.9	2317.9
Live birth	0.2862	0.3617	0.4890	0.5897	0.7317	0.8718	0.9425
Stillborn fetus or abortion	0.0185	0.0231	0.0307	0.0365	0.0441	0.0507	0.0533
three-parameter Sparling								1817.2	1856.0
Live birth	0.0856	0.2198	0.5416	0.7290	0.8539	0.9047	0.9242
Stillborn fetus or abortion	0.0188	0.253	0.0345	0.0394	0.0439	0.0473	0.0499
Nonparametric
Live birth	0.0062	0.2601	0.5542	0.6723	0.8194	0.8934	0.9287
Stillborn fetus or abortion	0.0170	0.0279	0.0405	0.0437	0.0455	0.0490	0.0535

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ISSN: 1742-4682

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