Evidence map›Paper›PMID 36305081›Full record

ArticleBiometrics2023

Instrumented difference-in-differences.

Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, Dylan S Small

Open access · greenAbstract read
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. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
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

8 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Ting YeDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.ORCID 0000-0001-6009-641X
Ashkan ErtefaieDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.ORCID 0000-0003-2611-9512
James FloryDepartment of Subspecialty Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Sean HennessyDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Dylan S SmallDepartment of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-4928-2646
University of Pennsylvania · USMemorial Sloan Kettering Cancer Center · USUniversity of Rochester · USUniversity of Washington · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Enhancing the Utility of the Trend-in-Trend Research DesignR01AG064589 · NIA · UNIVERSITY OF PENNSYLVANIA · PI HENNESSY, SEAN, SMALL, DYLAN S · 2019 to 2022
$2.2M
Advancing Personalized Medicine in PD Using Harmonized Multi-Site Clinical DataR33NS120240 · NINDS · UNIVERSITY OF ROCHESTER · PI ERTEFAIE, ASHKAN, VENUTO, CHARLES STANLEY · 2022 to 2024
$1.7M
Analyzing Sequential, Multiple Assignment, Randomized Trials in the Presence of Partial ComplianceR01DA048764 · NIDA · UNIVERSITY OF ROCHESTER · PI ERTEFAIE, ASHKAN · 2019 to 2022
$1.6M
NCI NIH HHS P30 CA008748NIA NIH HHS R01 AG064589NIDA NIH HHS R01 DA048764NIH HHS R01AG064589NINDS NIH HHS R33 NS120240
6 · The paper itself

Abstract

Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variables and difference-in-differences, we propose a new method called instrumented difference-in-differences that explicitly leverages exogenous randomness in an exposure trend to estimate the average and conditional average treatment effect in the presence of unmeasured confounding. We develop the identification assumptions using the potential outcomes framework. We propose a Wald estimator and a class of multiply robust and efficient semiparametric estimators, with provable consistency and asymptotic normality. In addition, we extend the instrumented difference-in-differences to a two-sample design to facilitate investigations of delayed treatment effect and provide a measure of weak identification. We demonstrate our results in simulated and real datasets.

Indexed as

Causalitycausal inferenceeffect modificationexclusion restrictioninstrumental variablesmultiple robustness

Identifiers

PMID36305081
PMCPMC10484497
OpenAlexW4307503881

What OpenQuestion 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 OpenQuestion graph.