ArticleGenetic epidemiology2013
Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Prognostic value of health-related quality of life in patients with metastatic pancreatic adenocarcinoma: a random forest methodology.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2016Trial
- Internal validation strategy for high dimensional prognosis model: A simulation study and application to transcriptomic in head and neck tumors.Computational and structural biotechnology journal · 2025Article
- Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study.Environmental monitoring and assessment · 2024Article
- Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts.mSystems · 2023Article
- Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging.Scientific reports · 2021Article
- Article
- Early prediction of spontaneous Patent Ductus Arteriosus (PDA) closure and PDA-associated outcomes: a prospective cohort investigation.BMC pediatrics · 2019Article
- Small sample sizes in high-throughput miRNA screens: A common pitfall for the identification of miRNA biomarkers.Biomolecular detection and quantification · 2018Article
- Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery.Proteomes · 2018Review
- Association of Interleukin-10 gene promoter polymorphisms with obstructive sleep apnea.Sleep & breathing = Schlaf & Atmung · 2016Article
- Serum protein profiling using an aptamer array predicts clinical outcomes of stage IIA colon cancer: A leave-one-out crossvalidation.Oncotarget · 2016Article
- Statistical aspect of translational and correlative studies in clinical trials.Chinese clinical oncology · 2016Article
- Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.PloS one · 2016Article
- Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach.Medicine · 2015Article
- A leave-one-out cross-validation SAS macro for the identification of markers associated with survival.Computers in biology and medicine · 2015Article
- The expression pattern of 19 genes predicts the histology of endometrial carcinoma.Scientific reports · 2014Article
- Statistical Issues in the Design and Analysis of nCounter Projects.Cancer informatics · 2014Review
- Association of Ugrp2 gene polymorphisms with adenoid hypertrophy in the pediatric population.Brazilian journal of otorhinolaryngologyArticle
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Authors and funding
2 authors.
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Abstract
A variety of prediction methods are used to relate high-dimensional genome data with a clinical outcome using a prediction model. Once a prediction model is developed from a data set, it should be validated using a resampling method or an independent data set. Although the existing prediction methods have been intensively evaluated by many investigators, there has not been a comprehensive study investigating the performance of the validation methods, especially with a survival clinical outcome. Understanding the properties of the various validation methods can allow researchers to perform more powerful validations while controlling for type I error. In addition, sample size calculation strategy based on these validation methods is lacking. We conduct extensive simulations to examine the statistical properties of these validation strategies. In both simulations and a real data example, we have found that 10-fold cross-validation with permutation gave the best power while controlling type I error close to the nominal level. Based on this, we have also developed a sample size calculation method that will be used to design a validation study with a user-chosen combination of prediction. Microarray and genome-wide association studies data are used as illustrations. The power calculation method in this presentation can be used for the design of any biomedical studies involving high-dimensional data and survival outcomes.
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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.