Evidence map›Paper›PMID 35844206›Full record

ArticleGlobal epidemiology2021

Practical data considerations for the modern epidemiology student.

Nguyen K Tran, Timothy L Lash, Neal D Goldstein

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Global epidemiology, 2021. 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. Trial
  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.

Nguyen K TranDepartment of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA.
Timothy L LashDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Neal D GoldsteinDepartment of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA.

Funding

Improving Reproducibility by Incorporating UncertaintyR01LM013049 · NLM · EMORY UNIVERSITY · PI LASH, TIMOTHY L. · 2019 to 2022
$1.1M
Towards prevention of HIV among vulnerable and stigmatized populations using geospatial dataK01AI143356 · NIAID · DREXEL UNIVERSITY · PI GOLDSTEIN, NEAL D · 2019 to 2023
$607k
NIAID NIH HHS K01 AI143356NLM NIH HHS R01 LM013049
6 · The paper itself

Abstract

As an inherent part of epidemiologic research, practical decisions made during data collection and analysis have the potential to impact the measurement of disease occurrence as well as statistical and causal inference from the results. However, the computational skills needed to collect, manipulate, and evaluate data have not always been a focus of educational programs, and the increasing interest in "data science" suggest that data literacy has become paramount to ensure valid estimation. In this article, we first motivate such practical concerns for the modern epidemiology student, particularly as it relates to challenges in causal inference; second, we discuss how such concerns may be manifested in typical epidemiological analyses and identify the potential for bias; third, we present a case study that exemplifies the entire process; and finally, we draw attention to resources that can help epidemiology students connect the theoretical underpinning of the science to the practical considerations as described herein.

Indexed as

BiostatisticsCausal inferenceData scienceEducation and trainingEpidemiology

Identifiers

PMID35844206
PMCPMC9286486

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.