ArticleSankhya. Series B (2008)2021
Shape Detection using Semi-parametric Shape-Restricted Mixed Effects Regression Spline with Applications.
Article in Sankhya. Series B (2008), 2021. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications.Sankhya. Series B. [Methodological.] · 2024Article
- Article
- Shape-Restricted Detection of Natural Product Hormesis: A Re-Analysis of Complex Botanical Mixtures.Dose-response : a publication of International Hormesis SocietyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Linear models are widely used in the field of epidemiology to model the relationship between placental-fetal hormone and fetal/infant outcome. When researchers suspect curvilinear relationship exists, some nonparametric techniques, including regression splines, smoothing splines and penalized regression splines, can be used to model the relationship (Korevaar et al. 2016; Wu and Zhang 2006). By applying these nonparametric techniques, researchers can relax the linearity assumption and capture scientifically meaningful or appropriate shapes. In this paper, we focus on the regression spline technique and develop a method to help researchers select the most suitable shape to describe their data among increasing, decreasing, convex and concave shapes. Specifically, we develop a mixed effects regression spline to model hormonal data described in this paper. The proposed methodology is general enough to be applied to other similar problems. We illustrate the method using a state-wide prenatal screening program data set.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.