Evidence map›Paper›PMID 33063887›Full record

ReviewGenetic epidemiology2021

On the application, reporting, and sharing of in silico simulations for genetic studies.

Kaleigh Riggs, Huann-Sheng Chen, Melissa Rotunno, Bing Li, Naoko I Simonds, Leah E Mechanic, Bo Peng

Open access · hybridAbstract readReview
In one paragraph

Review in Genetic epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 25% of its field
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
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

7 authors at 6 institutions in 2 countries.

Kaleigh RiggsDepartment of Statistics, Rice University, Houston, Texas, USA.
Huann-Sheng ChenDivision of Cancer Control and Population Sciences, Statistical Research and Applications Branch, Surveillance Research Program, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, USA.
Melissa RotunnoDivision of Cancer Control and Population Sciences, Genomic Epidemiology Branch, Epidemiology and Genomics Research Program, NCI, NIH, Bethesda, Maryland, USA.
Bing LiDepartment of Biostatistics, Brown University, Providence, Rhode Island, USA.
Naoko I SimondsNaoko Ishibe Simonds Consulting, Englewood, New Jersey, USA.
Leah E MechanicDivision of Cancer Control and Population Sciences, Genomic Epidemiology Branch, Epidemiology and Genomics Research Program, NCI, NIH, Bethesda, Maryland, USA.
Bo PengDepartment of Medicine, Baylor College of Medicine, Houston, Texas, USA.ORCID 0000-0001-8225-2284
Center for Cancer Research · USBaylor College of Medicine · USBrown University · USFIT Consulting (Italy) · ITNational Institutes of Health · USRice University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In silico simulations play an indispensable role in the development and application of statistical models and methods for genetic studies. Simulation tools allow for the evaluation of methods and investigation of models in a controlled manner. With the growing popularity of evolutionary models and simulation-based statistical methods, genetic simulations have been applied to a wide variety of research disciplines such as population genetics, evolutionary genetics, genetic epidemiology, ecology, and conservation biology. In this review, we surveyed 1409 articles from five journals that publish on major application areas of genetic simulations. We identified 432 papers in which genetic simulations were used and examined the targets and applications of simulation studies and how these simulation methods and simulated data sets are reported and shared. Whereas a large proportion (30%) of the surveyed articles reported the use of genetic simulations, only 28% of these genetic simulation studies used existing simulation software, 2% used existing simulated data sets, and 19% and 12% made source code and simulated data sets publicly available, respectively. Moreover, 15% of articles provided no information on how simulation studies were performed. These findings suggest a need to encourage sharing and reuse of existing simulation software and data sets, as well as providing more information regarding the performance of simulations.

Indexed as

Models, GeneticSoftwareComputer SimulationGenetics, PopulationHumansModels, Statisticaldata setsGenetic simulationsreproducibility

Identifiers

PMID33063887
PMCPMC7984380
OpenAlexW3093288420

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.