Evidence map›Paper›PMID 37521168›Full record

ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2023

Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution.

Wei Li, Wang Miao, Eric Tchetgen Tchetgen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Single proxy control.Biometrics · 2024
    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.

Wei LiCenter for Applied Statistics and School of Statistics, Renmin University of China, Beijing, P.R. China.
Wang MiaoDepartment of Probability and Statistics, Peking University, Beijing, P.R. China.
Eric Tchetgen TchetgenDepartment of Statistics, University of Pennsylvania, Philadelphia, USA.

Funding

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
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
NIA NIH HHS R01 AG065276NIGMS NIH HHS R01 GM139926
6 · The paper itself

Abstract

We consider identification and inference about mean functionals of observed covariates and an outcome variable subject to non-ignorable missingness. By leveraging a shadow variable, we establish a necessary and sufficient condition for identification of the mean functional even if the full data distribution is not identified. We further characterize a necessary condition for

Indexed as

identificationmodel-free estimationnon-ignorable missingnessshadow variable

Identifiers

PMID37521168
PMCPMC10376447

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.