Evidence map›Paper›PMID 35973786›Full record

ArticleBiometrics2023

Integrative Bayesian models using Post-selective inference: A case study in radiogenomics.

Snigdha Panigrahi, Shariq Mohammed, Arvind Rao, Veerabhadran Baladandayuthapani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Snigdha PanigrahiDepartment of Statistics, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0001-8869-207X
Shariq MohammedDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-3107-2969
Arvind RaoDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Veerabhadran BaladandayuthapaniDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0001-9107-3157

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alexey I Nesvizhskii · 1988 to 2026
$178.2M
Synthesizing Image-derived Heterogeneity with Genomic measurements for Assessing Disease Aggressiveness in Lower Grade GliomasR37CA214955 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI KURTEK, SEBASTIAN, RAO, ARVIND · 2018 to 2024
$3.9M
Graph-based Bayesian analysis of Genomics and Proteomics DataR01CA194391 · NCI · TEXAS A&M UNIVERSITY · PI BALADANDAYUTHAPANI, VEERABHADRAN, MALLICK, BANI K · 2016 to 2020
$2.0M
Bayesian Network-Based Integrative Genomics Methods for Precision MedicineR01CA244845 · NCI · UNIVERSITY OF PENNSYLVANIA · PI BALADANDAYUTHAPANI, VEERABHADRAN, MORRIS, JEFFREY S · 2021 to 2024
$1.8M
Integrative methods for high-dimensional genomics dataR01CA160736 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BALADANDAYUTHAPANI, VEERABHADRAN · 2011 to 2014
$1.4M
Proteomic-based integrated subject-specific networks in cancerR21CA220299 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BALADANDAYUTHAPANI, VEERABHADRAN, HA, MIN JIN · 2018 to 2019
$378k
NCI NIH HHS P30 CA046592NCI NIH HHS R01 CA160736NCI NIH HHS R01 CA194391NCI NIH HHS R01 CA244845NCI NIH HHS R21 CA220299NCI NIH HHS R37 CA214955
6 · The paper itself

Abstract

Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clinical relevance in terms of the disease mechanisms. Estimates for uncertainty in the resulting integrative models are however unreliable unless inference accounts for the selection of these associations with accuracy. In this paper, we develop selection-aware Bayesian methods, which (1) counteract the impact of model selection bias through a "selection-aware posterior" in a flexible class of integrative Bayesian models post a selection of promising variables via ℓ

Indexed as

AlgorithmsBayes TheoremHumansLikelihood FunctionsMarkov ChainsMonte Carlo MethodPhenotypeBayesian methodsconditional inferencegenomic dataintegrative modelspostselection inferenceradiogenomicssparse regression

Identifiers

PMID35973786
PMCPMC9931934

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Registered trials

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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.