Evidence map›Paper›PMID 31065968›Full record

ArticleLifetime data analysis2019

A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting.

Ditte Nørbo Sørensen, Torben Martinussen, Eric Tchetgen Tchetgen

Abstract read
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In one paragraph

Article in Lifetime data analysis, 2019. 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. 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.

Ditte Nørbo SørensenSection of Biostatistics, University of Copenhagen, Øster Farimagsgade 5B, 1014, Copenhagen K, Denmark.
Torben MartinussenSection of Biostatistics, University of Copenhagen, Øster Farimagsgade 5B, 1014, Copenhagen K, Denmark. tma@sund.ku.dk.ORCID http://orcid.org/0000-0002-9760-6791
Eric Tchetgen TchetgenStatistics Department, Wharton, University of Pennsylvania, 467 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA, 19104, USA.

Funding

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
NIAID NIH HHS R01 AI104459
6 · The paper itself

Abstract

In this paper we present a framework to do estimation in a structural Cox model when there may be unobserved confounding. The model is phrased in terms of a selection bias function and a baseline model that describes how covariates affect the survival time in a scenario without exposure. In this way model congeniality is ensured. The method uses an instrumental variable. Interestingly, the formulated model turns out to have similarities to the so-called Cox-Aalen survival model for the observed data. We exploit this to enhance estimation of the unknown parameters. This also allows us to derive large sample properties of the proposed estimator.

Indexed as

Proportional Hazards ModelsSurvival AnalysisModels, StatisticalSelection BiasCausal effectInstrumental variableSelection bias functionStructural Cox modelTreatment effect on the treated

Identifiers

PMID31065968

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.