ArticleProceedings of the National Academy of Sciences of the United States of America2020
Methods for correcting inference based on outcomes predicted by machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Doubly robust evaluation of high-dimensional surrogate markers.Biostatistics (Oxford, England) · 2023Trial
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- Scaling medical device regulatory science using large language models.NPJ digital medicine · 2026Article
- Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift.Proceedings of machine learning research · 2025Article
- Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.PLoS biology · 2025Article
- ipd: an R package for conducting inference on predicted data.Bioinformatics (Oxford, England) · 2025Article
- A Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes.Statistics in medicine · 2024Article
- Task-Agnostic Machine-Learning-Assisted Inference.Advances in neural information processing systems · 2024Article
- Valid inference for machine learning-assisted genome-wide association studies.Nature genetics · 2024Article
- Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks.Nature genetics · 2024Article
- Protein remote homology detection and structural alignment using deep learning.Nature biotechnology · 2024Article
- VARIANCE AS A PREDICTOR OF HEALTH OUTCOMES: SUBJECT-LEVEL TRAJECTORIES AND VARIABILITY OF SEX HORMONES TO PREDICT BODY FAT CHANGES IN PERI- AND POSTMENOPAUSAL WOMEN.The annals of applied statistics · 2024Article
- Cross-prediction-powered inference.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Postprediction Inference for Clinical Characteristics Extracted With Machine Learning on Electronic Health Records.JCO clinical cancer informatics · 2023Article
- Replication of Real-World Evidence in Oncology Using Electronic Health Record Data Extracted by Machine Learning.Cancers · 2023Article
- Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies.Current opinion in plant biology · 2023Review
- Identifying surrogate markers in real-world comparative effectiveness research.Statistics in medicine · 2022Article
- Systematic tissue annotations of genomics samples by modeling unstructured metadata.Nature communications · 2022Article
Corrections and comments
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3 authors.
Funding
Abstract
Many modern problems in medicine and public health leverage machine-learning methods to predict outcomes based on observable covariates. In a wide array of settings, predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes. We call inference with predicted outcomes postprediction inference. In this paper, we develop methods for correcting statistical inference using outcomes predicted with arbitrarily complicated machine-learning models including random forests and deep neural nets. Rather than trying to derive the correction from first principles for each machine-learning algorithm, we observe that there is typically a low-dimensional and easily modeled representation of the relationship between the observed and predicted outcomes. We build an approach for postprediction inference that naturally fits into the standard machine-learning framework where the data are divided into training, testing, and validation sets. We train the prediction model in the training set, estimate the relationship between the observed and predicted outcomes in the testing set, and use that relationship to correct subsequent inference in the validation set. We show our postprediction inference (postpi) approach can correct bias and improve variance estimation and subsequent statistical inference with predicted outcomes. To show the broad range of applicability of our approach, we show postpi can improve inference in two distinct fields: modeling predicted phenotypes in repurposed gene expression data and modeling predicted causes of death in verbal autopsy data. Our method is available through an open-source R package: https://github.com/leekgroup/postpi.
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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.