Article

An Investigation of Penalization and Data Augmentation to Improve Convergence of Generalized Estimating Equations for Clustered Binary Outcomes

2022-02-22

Abstract excerpt

<h4>Background: </h4> In binary logistic regression data are ‘separable’ if there exists a linear combination of explanatory variables which perfectly predicts the observed outcome, leading to non-existence of some of the maximum likelihood coefficient estimates. A popular solution to obtain finite estimates even with separable data is Firth’s logistic regression, which was originally proposed to reduce the bias i...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7ff35163-88b2-5a17-b0d8-b0733b222e76
DOI: 10.21203/rs.3.rs-1369776/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

An Investigation of Penalization and Data Augmentation to Improve Convergence of Generalized Estimating Equations for Clustered Binary OutcomesDOI 10.21203/rs.3.rs-1369776/v1Read

A two-step penalization and shrinkage approach for binary response data that is jointly separated and correlated: The effects of social networks on diarrheal diseaseSemantic proximity Read

Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes.Semantic proximity Read

Penalized generalized estimating equations for relative risk regression with applications to brain lesion dataSemantic proximity Read

LASSO type Penalized Spline Regression for Binary DataSemantic proximity Read

Recurrent Events Analysis with Piece-wise exponential Additive Mixed ModelsSemantic proximity Read

A Ranked Sparsity Extension to the Bayesian Information Criterion: A Tool for Selecting Variables from Multiple Data ModalitiesSemantic proximity Read

FAMES: Federated additive model using piecewise exponential survival dataSemantic proximity Read

Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for Competing Risk Data.Semantic proximity Read

Bridging Classical Statistics and Modern Data Analysis: A Comprehensive Survey of Generalized Linear ModelsSemantic proximity Read

Evaluating algorithmic fairness in the presence of clinical guidelines: the case of atherosclerotic cardiovascular disease risk estimationSemantic proximity Read

Revisiting VERTIGO and VERTIGO-CI: Identifying confidentiality breaches and introducing a statistically sound, efficient alternativeSemantic proximity Read

Developing clinical prediction models for nominal polytomous outcomes: a simulation study comparing available approachesSemantic proximity Read