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A comparison of the Hosmer–Lemeshow, Pigeon–Heyse, and Tsiatis goodness-of-fit tests for binary logistic regression under two grouping methods

Canary, JD ORCID: 0000-0002-8510-0611, Blizzard, L ORCID: 0000-0002-9541-6943, Barry, RP, Hosmer, DW and Quinn, SJ 2017 , 'A comparison of the Hosmer–Lemeshow, Pigeon–Heyse, and Tsiatis goodness-of-fit tests for binary logistic regression under two grouping methods' , Communications in Statistics: Simulation and Computation, vol. 46, no. 3 , pp. 1871-1894 , doi: 10.1080/03610918.2015.1017583.

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Abstract

Algebraic relationships between Hosmer–Lemeshow (HL), Pigeon–Heyse (J2), and Tsiatis (T) goodness-of-fit statistics for binary logistic regression models with continuous covariates were investigated, and their distributional properties and performances studied using simulations. Groups were formed under deciles-of-risk (DOR) and partition-covariate-space (PCS) methods. Under DOR, HL and T followed reported null distributions, while J2 did not. Under PCS, only T followed its reported null distribution, with HL and J2 dependent on model covariate number and partitioning. Generally, all had similar power. Of the three, T performed best, maintaining Type-I error rates and having a distribution invariant to covariate characteristics, number, and partitioning.

Item Type: Article
Authors/Creators:Canary, JD and Blizzard, L and Barry, RP and Hosmer, DW and Quinn, SJ
Keywords: binary logistic regression, deciles-of-risk, goodness-of-fit, Hosmer–Lemeshow, partition the covariate space, Pigeon–Heyse, Tsiatis
Journal or Publication Title: Communications in Statistics: Simulation and Computation
Publisher: Marcel Dekker Inc
ISSN: 0361-0918
DOI / ID Number: 10.1080/03610918.2015.1017583
Copyright Information:

Copyright 2017 Taylor & Francis Group, LLC

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