Evidence map›Paper›PMID 33209012›Full record

ArticleStatistica Sinica2020

IDENTIFICATION AND INFERENCE FOR MARGINAL AVERAGE TREATMENT EFFECT ON THE TREATED WITH AN INSTRUMENTAL VARIABLE.

Lan Liu, Wang Miao, Baoluo Sun, James Robins, Eric Tchetgen Tchetgen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistica Sinica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2018
    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

5 authors.

Lan LiuSchool of Statistics, University of Minnesota, Minneapolis, Minnesota 55455, USA.
Wang MiaoSchool of Mathematical Sciences, Peking University, Beijing 100871, China.
Baoluo SunGenome Institute of Singapore, 138672, Singapore.
James RobinsHarvard T.H. Chan School of Public Health, Harvard University, Massachusetts, 02115, USA.
Eric Tchetgen TchetgenHarvard T.H. Chan School of Public Health, Harvard University, Massachusetts, 02115, USA.

Funding

Next Generation Missing Data Methods in HIV ResearchR01AI127271 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI TCHETGEN TCHETGEN, ERIC JOEL · 2017 to 2021
$3.2M
ANALYTIC METHODS FOR HIV-TREATMENT AND COFACTOR EFFECTSR01AI032475 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI ROBINS, JAMES M · 1992 to 2004
$2.1M
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
Identification and Inference for Longitudinal Causal Mediation Analysis in HIVR01AI104459 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI SHPITSER, ILYA, VANDERWEELE, TYLER · 2013 to 2017
$2.0M
Semiparametric Methods for Gene-environment InteractionR01ES020337 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI TCHETGEN TCHETGEN, ERIC JOEL · 2012 to 2015
$1.4M
A new Statistical Framework to Tame Selection Bias in Health SciencesR21AI113251 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI PHIRI, KELESITSE, TCHETGEN TCHETGEN, ERIC JOEL · 2014 to 2015
$449k
NCI NIH HHS R01 CA222147NIAID NIH HHS R01 AI032475NIAID NIH HHS R01 AI104459NIAID NIH HHS R01 AI127271NIAID NIH HHS R21 AI113251NIEHS NIH HHS R01 ES020337
6 · The paper itself

Abstract

In observational studies, treatments are typically not randomized and therefore estimated treatment effects may be subject to confounding bias. The instrumental variable (IV) design plays the role of a quasi-experimental handle since the IV is associated with the treatment and only affects the outcome through the treatment. In this paper, we present a novel framework for identification and inference using an IV for the marginal average treatment effect amongst the treated (ETT) in the presence of unmeasured confounding. For inference, we propose three different semiparametric approaches: (i) inverse probability weighting (IPW), (ii) outcome regression (OR), and (iii) doubly robust (DR) estimation, which is consistent if either (i) or (ii) is consistent, but not necessarily both. A closed-form locally semiparametric efficient estimator is obtained in the simple case of binary IV and outcome and the efficiency bound is derived for the more general case.

Indexed as

CounterfactualsDouble robustnessEffect of treatment on the treatedInstrumental variableUnmeasured confounding

Identifiers

PMID33209012
PMCPMC7671747

What OpenQuestion holds

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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.