. Author manuscript; available in PMC: 2015 Aug 1.

Published in final edited form as: Lancet Respir Med. 2014 May 19;2(8):611–620. doi: 10.1016/S2213-2600(14)70097-9

Table 3.

Fit statistics for latent class models from two to five classes

ARMA Cohort:
Number of Individuals Per Class/Subphenotype
Number of classes	BIC	Entropy*	N₁	N₂	N₃	N₄	N₅	p-value**
2	39947.9	.78	318	155				.036
3	39760.2	.88	308	119	46			.59
4	39656.7	.86	212	126	43	92		.28
5	39583.8	.86	150	120	36	36	131	.64

ALVEOLI Cohort:
Number of Individuals Per Class/Subphenotype
Number of classes	BIC	Entropy^*	N₁	N₂	N₃	N₄	N₅	p-value^**
2	49709.5	.87	404	145				.016
3	49383.7	.92	400	145	4			.58
4	49098.8	.94	386	129	4	30		.35
5	48955.1	.87	242	154	4	30	119	.80

Abbreviations: BIC = Bayesian Information Criterion

Entropy is an index of how well the classes are separated. It ranges from zero to one and values around .8 and up are generally considered a sign of a useful model.

^**

By Vuong-Lo-Mendell-Rubin test, testing whether the number of classes provides improved model fit compared to a model using one fewer class.