Deconfounded and debiased estimation for high-dimensional linear regression under hidden confounding with application to omics data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40658470", "kind": "science-record", "revision": 2, "content_hash": "sha256:dd8236abe0a14641c1119bd9d23700a95cbfb24b22daee56f114c4547ee4904f", "created_at": "2026-11-01T20:30:45Z", "updated_at": "2026-11-01T21:35:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deconfounded and debiased estimation for high-dimensional linear regression under hidden confounding with application to omics data.", "doi": "10.1093/bioinformatics/btaf400", "pmid": "40658470", "authors": ["Zhaoyang Li", "Yahang Liu", "Kecheng Wei", "Yongfu Yu", "Guoyou Qin", "Zhongyi Zhu"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: A critical challenge in observational studies arises from the presence of hidden confounders in high-dimensional data. This leads to biases in causal effect estimation due to both hidden confounding and high-dimensional estimation. Some classical deconfounding methods are inadequate for high-dimensional scenarios and typically require prior information on hidden confounders. We propose a two-step deconfounded and debiased estimation for high-dimensional linear regression with hidden confounding.\nRESULTS: First, we reduce hidden confounding via spectral transformation. Second, we correct bias from the weighted ℓ1 penalty, commonly used in high-dimensional estimation, by inverting the Karush-Kuhn-Tucker conditions and solving convex optimization programs. This deconfounding technique by spectral transformation requires no prior knowledge of hidden confounders. This novel debiasing approach improves over recent work by not assuming a sparse precision matrix, making it more suitable for cases with intrinsic covariate correlations. Simulations show that the proposed method corrects both biases and provides more precise coefficient estimates than existing approaches. We also apply the proposed method to a deoxyribonucleic acid methylation dataset from the Alzheimer's disease (AD) neuroimaging initiative database to investigate the association between cerebrospinal fluid tau protein levels and AD severity.\nAVAILABILITY AND IMPLEMENTATION: The code for the proposed method is available on GitHub (https://github.com/Li-Zhaoy/Dec-Deb.git) and archived on Zenodo (DOI: https://10.5281/zenodo.15478745).", "claims": [{"id": "claim-method", "statement": "We propose a two-step deconfounded and debiased estimation for high-dimensional linear regression with hidden confounding.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40658470", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We propose a two-step deconfounded and debiased estimation for high-dimensional linear regression with hidden confounding."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article are available in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database at https://adni.loni.usc.edu/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40658470", "locator": "sections[16] \"Data availability\" paragraph 2", "quote": "The data underlying this article are available in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database at https://adni.loni.usc.edu/."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40658470"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40658470", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btaf400", "sha256": "sha256:3c7524b4f9eff91f806a4815a1ced7bf2c8a84ed8ec8647ad5aedf7423b56a86", "bytes": 33670, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btaf400", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12381636", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "ols", "label": "Regression modelling", "dsl_operations": ["ols_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.7, "band": "medium", "quote": "linear regression"}}, {"id": "method-2", "family": null, "specific": null, "label": "two-step deconfounded and debiased estimation", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We propose a two-step deconfounded and debiased estimation for high-dimensional linear regression with hidden confounding."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["ols_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: ols."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The data underlying this article are available in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database at https://adni.loni.usc.edu/."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["ols_fit"], "assets": ["source:pmid:40658470"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:56:53Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9901, "completion_tokens": 1382, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:40658470", "type": "paper", "label": "pmid:40658470"}, {"id": "source:pmid:40658470", "type": "manuscript", "label": "PMID 40658470 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40658470", "type": "paper", "label": "pmid:40658470"}, {"id": "source:pmid:40658470", "type": "manuscript", "label": "PMID 40658470 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40658470", "type": "extraction", "used": ["source:pmid:40658470"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40658470", "parent_hash": "sha256:c337d4e316f8c70c320f73122536fb36571433a17343ebdb2d8b01ea40a4c800", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["ols_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["linear regression"], "order_basis": "analysis_only", "id": "assay:1"}]}