ArticleProceedings of the National Academy of Sciences of the United States of America2015
Why significant variables aren't automatically good predictors.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 2 of them syntheses that pooled it.
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Who cites it
117 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Resolving Interpretation Challenges in Machine Learning Feature Selection With an Iterative Approach in Biomedical Pain Data.European journal of pain (London, England) · 2026Article
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- A Priori Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Using Deep Features from Pre-Treatment MRI and CT.Cancers · 2025Article
- Beyond P-values: A Multi-Metric Framework for Robust Feature Selection and Predictive Modeling.bioRxiv : the preprint server for biology · 2025Article
- Decoding of image properties from single-trial visual evoked potentials recorded by ultra-high-density EEG.Scientific reports · 2025Article
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- Geostatistical analysis to guide treatment decisions for soil-transmitted helminthiasis control in Uganda.PLoS neglected tropical diseases · 2025Article
- A Priori Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using CT Radiomics.Cancers · 2025Article
- AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations.Journal of medical Internet research · 2025Review
57 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.
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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.