ArticleComputational statistics & data analysis2014
Bayesian Variable Selection under the Proportional Hazards Mixed-effects Model.
Article in Computational statistics & data analysis, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Efficient Computation of High-Dimensional Penalized Piecewise Constant Hazard Random Effects Models.Statistics in medicine · 2025Article
- A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.BMC bioinformatics · 2022Article
- Three measures of explained variation for correlated survival data under the proportional hazards mixed-effects model.Statistics in medicine · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Over the past decade much statistical research has been carried out to develop models for correlated survival data; however, methods for model selection are still very limited. A stochastic search variable selection (SSVS) approach under the proportional hazards mixed-effects model (PHMM) is developed. The SSVS method has previously been applied to linear and generalized linear mixed models, and to the proportional hazards model with high dimensional data. Because the method has mainly been developed for hierarchical normal mixture distributions, it operates on the linear predictor under the Cox type models. The PHMM naturally incorporates the normal distribution via the random effects, which enables SSVS to efficiently search through the candidate variable space. The approach was evaluated through simulation, and applied to a multi-center lung cancer clinical trial data set, for which the variable selection problem was previously debated upon in the literature.
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Registered trials
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