Evidence map›Paper›PMID 25613424›Full record

ArticleLifetime data analysis2015

A competing risks approach to "biologic" interaction.

Per Kragh Andersen, Anders Skrondal

Abstract read
PubMed Publisher
In one paragraph

Article in Lifetime data analysis, 2015. 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

2 authors.

Per Kragh AndersenDepartment of Biostatistics, University of Copenhagen, O. Farimagsgade 5, PB2099, 1014, Copenhagen K, Denmark, pka@biostat.ku.dk.
Anders Skrondal

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In epidemiology, the concepts of "biologic" and "statistical" interactions have been the subject of extensive debate. We present a new approach to biologic interaction based on Rothman's original (Am J Epidemiol, 104:587-592, 1976) discussion of sufficient causes. We do this in a probabilistic framework using competing risks and argue that sufficient cause interaction between two factors can be evaluated via the parameters in a particular statistical model, the additive hazard rate model. We present empirical conditions for presence of sufficient cause interaction and an example based on data from a liver cirrhosis trial illustrates the ideas.

Indexed as

Effect Modifier, EpidemiologicProportional Hazards ModelsAnti-Inflammatory AgentsBiasBiometryCausalityConfounding Factors, EpidemiologicData Interpretation, StatisticalDenmarkHumansLiver CirrhosisPoisson DistributionPrednisoneRandomized Controlled Trials as TopicRisk FactorsAnti-Inflammatory AgentsPrednisone

Identifiers

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.