Evidence map›Paper›PMID 42016603›Full record

ArticleAnnals of statistics2024

EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Hongxiang Qiu, Eric Tchetgen Tchetgen, Edgar Dobriban

Abstract read
In one paragraph

Article in Annals of statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

3 authors.

Hongxiang QiuDepartment of Epidemiology and Biostatistics, Michigan State University.ORCID 0000-0002-5207-1818
Eric Tchetgen TchetgenDepartment of Statistics and Data Science, the Wharton School, University of Pennsylvania.
Edgar DobribanDepartment of Statistics and Data Science, the Wharton School, University of Pennsylvania.

Funding

Next Generation Missing Data Methods in HIV ResearchR01AI127271 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI TCHETGEN TCHETGEN, ERIC JOEL · 2017 to 2021
$3.2M
Novel Designs and Methods to Remove Hidden Confounding Bias in Health SciencesR01AG065276 · NIA · UNIVERSITY OF PENNSYLVANIA · PI TCHETGEN TCHETGEN, ERIC JOEL · 2020 to 2024
$2.4M
Theory and methods for mediation and interactionR01CA222147 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI TCHETGEN TCHETGEN, ERIC JOEL, VANDERWEELE, TYLER · 2018 to 2022
$2.1M
Accounting for Hidden Bias in Vaccine Studies: A Negative Control FrameworkR01GM139926 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SHI, XU, TCHETGEN TCHETGEN, ERIC JOEL · 2021 to 2024
$1.5M
NCI NIH HHS R01 CA222147NIAID NIH HHS R01 AI127271NIA NIH HHS R01 AG065276NIGMS NIH HHS R01 GM139926Wellcome Trust
6 · The paper itself

Abstract

Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. Techniques leveraging such

Indexed as

62G2068Q32Dataset shiftdomain adaptationefficiencymultiple robustnesssemiparametric modeltransfer learning

Identifiers

PMID42016603
PMCPMC13095157

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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 Socratic graph.