ArticleStat2019
Logistic regression error-in-covariate models for longitudinal high-dimensional covariates.
Article in Stat, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
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Who cites it
1 citing paper in PubMed.
- A sparse additive model for treatment effect-modifier selection.Biostatistics (Oxford, England) · 2022Trial
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We consider a logistic regression model for a binary response where part of its covariates are subject-specific random intercepts and slopes from a large number of longitudinal covariates. These random effect covariates must be estimated from the observed data, and therefore, the model essentially involves errors in covariates. Because of high dimension and high correlation of the random effects, we employ longitudinal principal component analysis to reduce the total number of random effects to some manageable number of random effects. To deal with errors in covariates, we extend the conditional-score equation approach to this moderate dimensional logistic regression model with random effect covariates. To reliably solve the conditional-score equations in moderate/high dimension, we apply a majorization on the first derivative of the conditional-score functions and a penalized estimation by the smoothly clipped absolute deviation. The method was evaluated through a set of simulation studies and applied to a data set with longitudinal cortical thickness of 68 regions of interest to identify biomarkers that are related to dementia transition.
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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.