Variable selection and prediction in biased samples with censored outcomes

Ying Wu () and Richard J. Cook ()
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Ying Wu: Nankai University
Richard J. Cook: University of Waterloo

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 1, No 5, 72-93

Abstract: Abstract With the increasing availability of large prospective disease registries, scientists studying the course of chronic conditions often have access to multiple data sources, with each source generated based on its own entry conditions. The different entry conditions of the various registries may be explicitly based on the response process of interest, in which case the statistical analysis must recognize the unique truncation schemes. Moreover, intermittent assessment of individuals in the registries can lead to interval-censored times of interest. We consider the problem of selecting important prognostic biomarkers from a large set of candidates when the event times of interest are truncated and right- or interval-censored. Methods for penalized regression are adapted to handle truncation via a Turnbull-type complete data likelihood. An expectation–maximization algorithm is described which is empirically shown to perform well. Inverse probability weights are used to adjust for the selection bias when assessing predictive accuracy based on individuals whose event status is known at a time of interest. Application to the motivating study of the development of psoriatic arthritis in patients with psoriasis in both the psoriasis cohort and the psoriatic arthritis cohort illustrates the procedure.

Keywords: Expectation–maximization algorithm; Inverse probability weighted estimator; Truncation; Penalized regression; Prediction error; ROC curve (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-017-9392-5

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