ArticleFrontiers in nutrition2014
A free-knot spline modeling framework for piecewise linear logistic regression in complex samples with body mass index and mortality as an example.
Article in Frontiers in nutrition, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Modeling growth performance of heterogeneous rabbits in a pastured system using nonlinear, spline and random regression models.Veterinary and animal science · 2025Article
- Establishing Pediatric and Adult RBC Reference Intervals With NHANES Data Using Piecewise Regression.American journal of clinical pathology · 2019Article
- Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare.Computational and mathematical methods in medicine · 2019Article
- How Much Variation in Outcomes Is Too Much in a Center of Excellence for Bariatric Surgery?JAMA · 2018Article
- Low, rather than High, Body Mass Index Is a Risk Factor for Acute Kidney Injury in Multiethnic Asian Patients: A Retrospective Observational Study.International journal of nephrology · 2018Article
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2 authors.
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Abstract
This paper details the design, evaluation, and implementation of a framework for detecting and modeling nonlinearity between a binary outcome and a continuous predictor variable adjusted for covariates in complex samples. The framework provides familiar-looking parameterizations of output in terms of linear slope coefficients and odds ratios. Estimation methods focus on maximum likelihood optimization of piecewise linear free-knot splines formulated as B-splines. Correctly specifying the optimal number and positions of the knots improves the model, but is marked by computational intensity and numerical instability. Our inference methods utilize both parametric and nonparametric bootstrapping. Unlike other nonlinear modeling packages, this framework is designed to incorporate multistage survey sample designs common to nationally representative datasets. We illustrate the approach and evaluate its performance in specifying the correct number of knots under various conditions with an example using body mass index (BMI; kg/m
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