ArticleBMC bioinformatics2015
Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06365060 (Screening for AL Amyloidosis in Smoldering Multiple Myeloma), which is not on this map. Cited by 13 papers.
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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.
Screening for AL Amyloidosis in Smoldering Multiple Myeloma
Who cites it
13 citing papers in PubMed.
- Identifying county-level effect modifiers of the association between heat waves and preterm birth using a Bayesian spatial meta regression approach.medRxiv : the preprint server for health sciences · 2025Article
- Robust Cancer Biomarker Identification From Matched Transcriptomic Data Via Bootstrapped Regularized Conditional Logistic Regression.Cancer informatics · 2025Article
- Random forests for the analysis of matched case-control studies.BMC bioinformatics · 2024Article
- penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers.BMC bioinformatics · 2024Article
- Association between number of medications and hip fractures in Japanese elderly using conditional logistic LASSO regression.Scientific reports · 2023Article
- Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity.Cell reports. Medicine · 2023Article
- Circulating amino acids and amino acid-related metabolites and risk of breast cancer among predominantly premenopausal women.NPJ breast cancer · 2021Article
- Learning-based CBCT correction using alternating random forest based on auto-context model.Medical physics · 2019Article
- Extending Classification Algorithms to Case-Control Studies.Biomedical engineering and computational biology · 2019Article
- Travel to farms in the lowlands and inadequate malaria information significantly predict malaria in villages around Lake Tana, northwest Ethiopia: a matched case-control study.Malaria journal · 2018Article
- Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data.PloS one · 2017Article
- The case-crossover design via penalized regression.BMC medical research methodology · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper considers the problem of estimation and variable selection for large high-dimensional data (high number of predictors p and large sample size N, without excluding the possibility that N < p) resulting from an individually matched case-control study. We develop a simple algorithm for the adaptation of the Lasso and related methods to the conditional logistic regression model. Our proposal relies on the simplification of the calculations involved in the likelihood function. Then, the proposed algorithm iteratively solves reweighted Lasso problems using cyclical coordinate descent, computed along a regularization path. This method can handle large problems and deal with sparse features efficiently. We discuss benefits and drawbacks with respect to the existing available implementations. We also illustrate the interest and use of these techniques on a pharmacoepidemiological study of medication use and traffic safety.
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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 Socratic graph.