Covariate-adjusted generalized pairwise comparisons in small samples.
generalized estimating equations
generalized pairwise comparisons
probabilistic index models
separation
small samples
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
04 Jul 2024
04 Jul 2024
Historique:
revised:
15
04
2024
received:
25
01
2024
accepted:
31
05
2024
medline:
4
7
2024
pubmed:
4
7
2024
entrez:
4
7
2024
Statut:
aheadofprint
Résumé
Semiparametric probabilistic index models allow for the comparison of two groups of observations, whilst adjusting for covariates, thereby fitting nicely within the framework of generalized pairwise comparisons (GPC). As with most regression approaches in this setting, the limited amount of data results in invalid inference as the asymptotic normality assumption is not met. In addition, separation issues might arise when considering small samples. In this article, we show that the parameters of the probabilistic index model can be estimated using generalized estimating equations, for which adjustments exist that lead to estimators of the sandwich variance-covariance matrix with improved finite sample properties and that can deal with bias due to separation. In this way, appropriate inference can be performed as is shown through extensive simulation studies. The known relationships between the probabilistic index and other GPC statistics allow to also provide valid inference for example, the net treatment benefit or the success odds.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Fonds Wetenschappelijk Onderzoek
ID : G0D1221N
Organisme : European Union's Horizon 2020 Research and Innovation Programme
ID : 825575
Informations de copyright
© 2024 John Wiley & Sons Ltd.
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