Evidence map›Paper›PMID 28458613›Full record

ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2017

Causal analysis of ordinal treatments and binary outcomes under truncation by death.

Linbo Wang, Thomas S Richardson, Xiao-Hua Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2017. 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. Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021
    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.

Linbo WangUniversity of Washington, Seattle, USA.
Thomas S RichardsonUniversity of Washington, Seattle, USA.
Xiao-Hua ZhouUniversity of Washington, Seattle, USA.

Funding

LOC: HIV Vaccine Trials NetworkUM1AI068614 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Dan H. Barouch, Lawrence Corey · 2011 to 2026
$1175.6M
SDMC: HIV Vaccine Trials NetworkUM1AI068635 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Peter B. Gilbert, Yunda Huang · 2011 to 2026
$385.9M
ANALYTIC METHODS FOR HIV-TREATMENT AND COFACTOR EFFECTSR01AI032475 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI ROBINS, JAMES M · 1992 to 2004
$2.1M
NIAID NIH HHS R01 AI032475NIAID NIH HHS UM1 AI068614NIAID NIH HHS UM1 AI068635
6 · The paper itself

Abstract

It is common that in multi-arm randomized trials, the outcome of interest is "truncated by death," meaning that it is only observed or well-defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal treatments and binary outcomes in presence of truncation by death. We illustrate via examples the appropriateness of our assumptions in different scientific contexts.

Indexed as

Bayesian analysisCausal inferenceMulti-arm trialsOrdinal treatment variablePrincipal stratificationSurvey incentives

Identifiers

PMID28458613
PMCPMC5407214

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.