PubMed · 16294189
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C Mélot. 2005. [Multivariate analysis].. https://doi.org/10.1016/s0761-8425(05)85622-0
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This paper addresses the modelling of missing covariate data with the logistic regression model. The aim of this paper is to evaluate the properties of an efficient score for logistic regression in a two-phase design. Simulation studies show that the efficient score is more efficient than two other pseudo-likelihood methods when the correlation between the missing covariate and its surrogate is high or the sampling proportion is small. These methods are illustrated with data from the National Wilms Tumor Study Group. Results from the example confirm the simulation study findings with the exception that the pseudo-likelihood approach produces more reliable estimates than the weighted pseudo-likelihood approach.
A tree procedure is proposed to check the adequacy of a fitted logistic regression model. The proposed method not only makes natural assessment for the logistic model, but also provides clues to amend its lack-of-fit. The resulting tree-augmented logistic model facilitates a refined model with meaningful interpretation. We demonstrate its use via simulation studies and an application to the Pima Indians diabetes data.
The behavior of single molecule defines whether a cell lives, dies, or responds to a specific drug treatment. Single molecule microscopies have begun to reveal the number, location, and functionalities of molecules outside and inside living cells. This issue of Biopolymers presents a first set of reviews that aim to highlight the accomplishments and future prospects of single molecule microscopies.