ArticleStatistical methods in medical research2019
Measurement error correction in the least absolute shrinkage and selection operator model when validation data are available.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
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- Identification of Hub Neutrophil Coexpressed Genes in Acute PancreatitisEndocrine, metabolic & immune disorders drug targets · 2026Article
- Risk factor identification and prediction of pleural effusion following coronary artery bypass grafting.American journal of translational research · 2025Article
- Identifcation of candidate biomarkers for polyarteritis nodosa using data-independent acquisition mass spectrometry.American journal of translational research · 2025Article
- Incidence rate of occult lymph node metastasis in clinical TRespiratory research · 2024Article
- Identification of immune-related genes and integrated analysis of immune-cell infiltration in melanoma.Aging · 2024Article
- Advancing polytrauma care: developing and validating machine learning models for early mortality prediction.Journal of translational medicine · 2023Article
- Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection.Scientific reports · 2021Article
- Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis.Frontiers in oncology · 2021Article
- Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma.Translational oncology · 2020Article
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5 authors.
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Abstract
Measurement of serum biomarkers by multiplex assays may be more variable as compared to single biomarker assays. Measurement error in these data may bias parameter estimates in regression analysis, which could mask true associations of serum biomarkers with an outcome. The Least Absolute Shrinkage and Selection Operator (LASSO) can be used for variable selection in these high-dimensional data. Furthermore, when the distribution of measurement error is assumed to be known or estimated with replication data, a simple measurement error correction method can be applied to the LASSO method. However, in practice the distribution of the measurement error is unknown and is expensive to estimate through replication both in monetary cost and need for greater amount of sample which is often limited in quantity. We adapt an existing bias correction approach by estimating the measurement error using validation data in which a subset of serum biomarkers are re-measured on a random subset of the study sample. We evaluate this method using simulated data and data from the Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD). We show that the bias in parameter estimation is reduced and variable selection is improved.
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