The proportional hazards (PH) model is, arguably, the most popular model for the analysis of lifetime data arising from epidemiological studies, among many others. In such applications, analysts may be faced with censored outcomes and/or studies which institute enrollment criterion leading to left truncation. Censored outcomes arise when the event of interest is not observed but rather is known relevant to an observation time(s).
View Article and Find Full Text PDFComput Stat Data Anal
December 2018
Correlated survival data naturally arise from many clinical and epidemiological studies. For the analysis of such data, the Gamma-frailty proportional hazards (PH) model is a popular choice because the regression parameters have marginal interpretations and the statistical association between the failure times can be explicitly quantified via Kendall's tau. Despite their popularity, Gamma-frailty PH models for correlated interval-censored data have not received as much attention as analogous models for right-censored data.
View Article and Find Full Text PDFThe proportional hazards (PH) model is arguably one of the most popular models used to analyze time to event data arising from clinical trials and longitudinal studies. In many such studies, the event time is not directly observed but is known relative to periodic examination times; i.e.
View Article and Find Full Text PDFIn this work, we delineate an altered study design of a pre-existing clinical trial that is currently being implemented in the Department of Pediatrics at the University of North Carolina at Chapel Hill. The purpose of the ongoing investigation of the desensitized pediatric cohort is to address the effectiveness of sublingual immunotherapy in achieving sustained unresponsiveness (SU) as assessed by repeated double-blind placebo-controlled food challenges (DBPCFC). With scarce published literature characterizing SU, the length of time off-therapy that would represent clinically meaningful benefit remains undefined.
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