A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model
Prabhashi W. Withana Gamage (withanpw@jmu.edu),
Christopher S. McMahan and
Lianming Wang
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Prabhashi W. Withana Gamage: James Madison University
Christopher S. McMahan: Clemson University
Lianming Wang: University of South Carolina
Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2023, vol. 29, issue 1, No 7, 188-212
Abstract:
Abstract 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). Left truncated data occur in studies that exclude participants who have experienced the event prior to being enrolled in the study. If not accounted for, both of these features can lead to inaccurate inferences about the population under study. Thus, to overcome this challenge, herein we propose a novel unified PH model that can be used to accommodate both of these features. In particular, our approach can seamlessly analyze exactly observed failure times along with interval-censored observations, while aptly accounting for left truncation. To facilitate model fitting, an expectation–maximization algorithm is developed through the introduction of carefully structured latent random variables. To provide modeling flexibility, a monotone spline representation is used to approximate the cumulative baseline hazard function. The performance of our methodology is evaluated through a simulation study and is further illustrated through the analysis of two motivating data sets; one that involves child mortality in Nigeria and the other prostate cancer.
Keywords: Censored data; EM algorithm; Interval-censored data; Monotone splines; Proportional hazards model (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)
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DOI: 10.1007/s10985-022-09579-z
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