title: A case-base sampling method for estimating recurrent event intensities.
abstract: Case-base sampling provides an alternative to risk set sampling based methods to estimate hazard regression models, in particular when absolute hazards are also of interest in addition to hazard ratios. The case-base sampling approach results in a likelihood expression of the logistic regression form, but instead of categorized time, such an expression is obtained through sampling of a discrete set of person-time coordinates from all follow-up data. In this paper, in the context of a time-dependent exposure such as vaccination, and a potentially recurrent adverse event outcome, we show that the resulting partial likelihood for the outcome event intensity has the asymptotic properties of a likelihood. We contrast this approach to self-matched case-base sampling, which involves only within-individual comparisons. The efficiency of the case-base methods is compared to that of standard methods through simulations, suggesting that the information loss due to sampling is minimal.
authors.affiliation: Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada. olli.saarela@utoronto.ca.
authors.forename: Olli
authors.initials: O
authors.lastname: Saarela
mesh_terms: D016022:Case-Control Studies; D006801:Humans; D016015:Logistic Models; D016016:Proportional Hazards Models
keywords: Case-base sampling; Conditional logistic regression; Hazard regression; Recurrent events; Self-matching