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Goodness-of-fit statistics for log-link regression models
journal contribution
posted on 2023-05-18, 07:07 authored by Quinn, SJ, Hosmer, DW, Christopher BlizzardChristopher BlizzardThe use of log binomial regression, regression on binary outcomes using a log link, is becoming increasingly popular because it provides estimates of relative risk. However, little work has been done on model evaluation. We used simulations to compare the performance of five goodness-of-fit statistics applied to different models in a log binomial setting, namely the Hosmer–Lemeshow, the normalized Pearson chi-square, the normalized unweighted sum of squares, Le Cessie and van Howelingen's statistic based on smoothed residuals and the Hjort–Hosmer test. The normalized Pearson chi-square was unsuitable as the rejection rate depended also on the range of predicted probabilities. The Le Cessie and van Howelingen's test statistic had poor sampling properties when evaluating a correct model and was also considered to be unsuitable in this context. The performance of the remaining three statistics was comparable in most simulations. However, using real data the Hjort–Hosmer outperformed the other two statistics.
Funding
National Health & Medical Research Council
History
Publication title
Journal of Statistical Computation and SimulationVolume
85Issue
12Pagination
2533-2545ISSN
0094-9655Department/School
Menzies Institute for Medical ResearchPublisher
Taylor & Francis LtdPlace of publication
4 Park Square, Milton Park, Abingdon, England, Oxon, Ox14 4RnRights statement
Copyright 2014 Taylor & FrancisRepository Status
- Restricted