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Precision of estimators in interval censored parametric survival models

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Show simple item record Peng, Defen MacKenzie, Gilbert 2013-01-09T14:41:18Z 2013-01-09T14:41:18Z 2011
dc.description peer-reviewed en_US
dc.description.abstract Recently, several advances have been made in the analysis of interval censored (IC) data mainly in relation to semi-parametric proportional hazard (PH) models (Gómez et al., 2009, Lesaffre et al., 2005). It is arguable, however, that the parametric case has been somewhat neglected, overall, and that more can be learned, especially in relation to non-PH models. Accordingly, we focus on simple parametric models for interval censored survival data arising in longitudinal RCTs. For the exponential regression model we compare the performance of a general likelihood with commonly used proxy likelihoods, which ignore the interval censoring by treating the interval censored times to events as if they were exact. We show analytically that use of proxy likelihoods leads to estimators which are artificially precise and we quantify the extent of the resulting biases in a simulation study and by analyzing real data. We also compare the likelihoods using non-PH models and obtain different findings. en_US
dc.language.iso eng en_US
dc.publisher IWSM en_US
dc.relation.ispartofseries Proceedings of the 26th International Workshop on Statistical Modelling;
dc.subject artificial precision en_US
dc.subject interval censoring en_US
dc.subject longitudinal RCTs en_US
dc.subject PH & non-PH survival modelling en_US
dc.subject proxy liklihoods en_US
dc.title Precision of estimators in interval censored parametric survival models en_US
dc.type info:eu-repo/semantics/conferenceObject
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid 07/MI/012 en_US
dc.relation.projectid 05/RF/MAT/026 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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