Evidence map›Paper›PMID 31161279›Full record

ReviewEuropean journal of epidemiology2019

A descriptive review of variable selection methods in four epidemiologic journals: there is still room for improvement.

Denis Talbot, Victoria Kubuta Massamba

Abstract readReview
PubMed Publisher
In one paragraph

Review in European journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 3 pooled it
–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

52 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Causal Model Building in the Context of Cardiac Rehabilitation: A Systematic Review.International journal of environmental research and public health · 2023
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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.

Denis TalbotDépartement de médecine sociale et préventive, Faculté de médecine, Université Laval, 1050, avenue de la Médecine, Pavillon Ferdinand-Vandry, room 2454, Quebec, QC, G1V 0A6, Canada. denis.talbot@fmed.ulaval.ca.ORCID http://orcid.org/0000-0003-0431-3314
Victoria Kubuta MassambaDépartement de médecine sociale et préventive, Faculté de médecine, Université Laval, 1050, avenue de la Médecine, Pavillon Ferdinand-Vandry, room 2454, Quebec, QC, G1V 0A6, Canada.ORCID http://orcid.org/0000-0003-0227-3099

Funding

Centre Hospitalier Universitaire de Québec 2710Fonds de Recherche du Québec - Santé 251607
6 · The paper itself

Abstract

A review of epidemiological papers conducted in 2009 concluded that several studies employed variable selection methods susceptible to introduce bias and yield inadequate inferences. Many new confounder selection methods have been developed since then. The goal of the study was to provide an updated descriptive portrait of which variable selection methods are used by epidemiologists for analyzing observational data. Studies published in four major epidemiological journals in 2015 were reviewed. Only articles concerned with a predictive or explicative objective and reporting on the analysis of individual data were included. Method(s) employed for selecting variables were extracted from retained articles. A total of 975 articles were retrieved and 299 met eligibility criteria, 292 of which pursued an explicative objective. Among those, 146 studies (50%) reported using prior knowledge or causal graphs for selecting variables, 34 (12%) used change in effect estimate methods, 26 (9%) used stepwise approaches, 16 (5%) employed univariate analyses, 5 (2%) used various other methods and 107 (37%) did not provide sufficient details to allow classification (more than one method could be employed in a single article). Despite being less frequent than in the previous review, stepwise and univariable analyses, which are susceptible to introduce bias and produce inadequate inferences, were still prevalent. Moreover, 37% studies did not provide sufficient details to assess how variables were selected. We thus believe there is still room for improvement in variable selection methods used by epidemiologists and in their reporting.

Indexed as

Confounding Factors, EpidemiologicEpidemiologic StudiesEpidemiologistsPeriodicals as TopicBiasHumansResearch DesignConfoundingEpidemiologic methodsModelingVariable selection

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.