Evidence map›Paper›PMID 42348288›Full record

ArticleStatistics in medicine2026

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Tamir Zehavi, Uri Obolski, Michal Chowers, Daniel Nevo

Abstract read
In one paragraph

Article in Statistics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Tamir ZehaviDepartment of Statistics and Operations Research, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel.
Uri ObolskiDepartment of Epidemiology and Preventive Medicine, School of Public Health, Tel Aviv University, Tel Aviv, Israel.ORCID https://orcid.org/0000-0001-7594-9745
Michal ChowersGray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Daniel NevoDepartment of Statistics and Operations Research, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel.

Funding

Israel Science Foundation 1286/21Israel Science Foundation 827/21Tel Aviv University Center for AI and Data Science (TAD)
6 · The paper itself

Abstract

Comparing future antibiotic resistance levels resulting from different antibiotic treatments is challenging because some patients may survive only under one of the antibiotic treatments. We embed this problem within a semi-competing risks approach to study the causal effect on resistant infection, treated as a nonterminal event time. We argue that existing principal stratification estimands for such problems exclude patients for whom a causal effect is well-defined and is of clinical interest. Therefore, we present a new principal stratum, the infected-or-survivors (

Indexed as

Anti-Bacterial AgentsDrug Resistance, BacterialDrug Resistance, MicrobialModels, StatisticalCausalityComputer SimulationHumansAnti-Bacterial Agentsfrailtyillness‐death modelprincipal stratificationsemi‐competing riskssurvivor average causal effect

Identifiers

PMID42348288
PMCPMC13297036

What OpenQuestion holds

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LicenceCC BY-NC
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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.