Statistical considerations in reporting cardiovascular research

Merry L Lindsey, Gillian A. Gray, Susan K. Wood, Douglas Curran-Everett

Research output: Contribution to journalReview article

19 Citations (Scopus)

Abstract

The problem of inadequate statistical reporting is long standing and widespread in the biomedical literature, including in cardiovascular physiology. Although guidelines for reporting statistics have been available in clinical medicine for some time, there are currently no guidelines specific to cardiovascular physiology. To assess the need for guidelines, we determined the type and frequency of statistical tests and procedures currently used in the American Journal of Physiology-Heart and Circulatory Physiology. A PubMed search for articles published in the American Journal of Physiology-Heart and Circulatory Physiology between January 1, 2017, and October 6, 2017, provided a final sample of 146 articles evaluated for methods used and 38 articles for indepth analysis. The t-test and ANOVA accounted for 71% (212 of 300 articles) of the statistical tests performed. Of six categories of post hoc tests, Bonferroni and Tukey tests were used in 63% (62 of 98 articles). There was an overall lack in details provided by authors publishing in the American Journal of Physiology-Heart and Circulatory Physiology, and we compiled a list of recommended minimum reporting guidelines to aid authors in preparing manuscripts. Following these guidelines could substantially improve the quality of statistical reports and enhance data rigor and reproducibility.

Original languageEnglish (US)
Pages (from-to)H303-H313
JournalAmerican Journal of Physiology - Heart and Circulatory Physiology
Volume315
Issue number2
DOIs
StatePublished - Aug 1 2018

Fingerprint

Guidelines
Cardiovascular Physiological Phenomena
Research
Manuscripts
Clinical Medicine
PubMed
Analysis of Variance

Keywords

  • Big data
  • Cardiovascular disease
  • Meta-research
  • Meta-science
  • Physiology
  • Rigor and reproducibility
  • Statistics

ASJC Scopus subject areas

  • Physiology
  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)

Cite this

Statistical considerations in reporting cardiovascular research. / Lindsey, Merry L; Gray, Gillian A.; Wood, Susan K.; Curran-Everett, Douglas.

In: American Journal of Physiology - Heart and Circulatory Physiology, Vol. 315, No. 2, 01.08.2018, p. H303-H313.

Research output: Contribution to journalReview article

Lindsey, Merry L ; Gray, Gillian A. ; Wood, Susan K. ; Curran-Everett, Douglas. / Statistical considerations in reporting cardiovascular research. In: American Journal of Physiology - Heart and Circulatory Physiology. 2018 ; Vol. 315, No. 2. pp. H303-H313.
@article{f34c0c4a47d8421b82900468260917d4,
title = "Statistical considerations in reporting cardiovascular research",
abstract = "The problem of inadequate statistical reporting is long standing and widespread in the biomedical literature, including in cardiovascular physiology. Although guidelines for reporting statistics have been available in clinical medicine for some time, there are currently no guidelines specific to cardiovascular physiology. To assess the need for guidelines, we determined the type and frequency of statistical tests and procedures currently used in the American Journal of Physiology-Heart and Circulatory Physiology. A PubMed search for articles published in the American Journal of Physiology-Heart and Circulatory Physiology between January 1, 2017, and October 6, 2017, provided a final sample of 146 articles evaluated for methods used and 38 articles for indepth analysis. The t-test and ANOVA accounted for 71{\%} (212 of 300 articles) of the statistical tests performed. Of six categories of post hoc tests, Bonferroni and Tukey tests were used in 63{\%} (62 of 98 articles). There was an overall lack in details provided by authors publishing in the American Journal of Physiology-Heart and Circulatory Physiology, and we compiled a list of recommended minimum reporting guidelines to aid authors in preparing manuscripts. Following these guidelines could substantially improve the quality of statistical reports and enhance data rigor and reproducibility.",
keywords = "Big data, Cardiovascular disease, Meta-research, Meta-science, Physiology, Rigor and reproducibility, Statistics",
author = "Lindsey, {Merry L} and Gray, {Gillian A.} and Wood, {Susan K.} and Douglas Curran-Everett",
year = "2018",
month = "8",
day = "1",
doi = "10.1152/ajpheart.00309.2018",
language = "English (US)",
volume = "315",
pages = "H303--H313",
journal = "American Journal of Physiology - Renal Physiology",
issn = "0363-6127",
publisher = "American Physiological Society",
number = "2",

}

TY - JOUR

T1 - Statistical considerations in reporting cardiovascular research

AU - Lindsey, Merry L

AU - Gray, Gillian A.

AU - Wood, Susan K.

AU - Curran-Everett, Douglas

PY - 2018/8/1

Y1 - 2018/8/1

N2 - The problem of inadequate statistical reporting is long standing and widespread in the biomedical literature, including in cardiovascular physiology. Although guidelines for reporting statistics have been available in clinical medicine for some time, there are currently no guidelines specific to cardiovascular physiology. To assess the need for guidelines, we determined the type and frequency of statistical tests and procedures currently used in the American Journal of Physiology-Heart and Circulatory Physiology. A PubMed search for articles published in the American Journal of Physiology-Heart and Circulatory Physiology between January 1, 2017, and October 6, 2017, provided a final sample of 146 articles evaluated for methods used and 38 articles for indepth analysis. The t-test and ANOVA accounted for 71% (212 of 300 articles) of the statistical tests performed. Of six categories of post hoc tests, Bonferroni and Tukey tests were used in 63% (62 of 98 articles). There was an overall lack in details provided by authors publishing in the American Journal of Physiology-Heart and Circulatory Physiology, and we compiled a list of recommended minimum reporting guidelines to aid authors in preparing manuscripts. Following these guidelines could substantially improve the quality of statistical reports and enhance data rigor and reproducibility.

AB - The problem of inadequate statistical reporting is long standing and widespread in the biomedical literature, including in cardiovascular physiology. Although guidelines for reporting statistics have been available in clinical medicine for some time, there are currently no guidelines specific to cardiovascular physiology. To assess the need for guidelines, we determined the type and frequency of statistical tests and procedures currently used in the American Journal of Physiology-Heart and Circulatory Physiology. A PubMed search for articles published in the American Journal of Physiology-Heart and Circulatory Physiology between January 1, 2017, and October 6, 2017, provided a final sample of 146 articles evaluated for methods used and 38 articles for indepth analysis. The t-test and ANOVA accounted for 71% (212 of 300 articles) of the statistical tests performed. Of six categories of post hoc tests, Bonferroni and Tukey tests were used in 63% (62 of 98 articles). There was an overall lack in details provided by authors publishing in the American Journal of Physiology-Heart and Circulatory Physiology, and we compiled a list of recommended minimum reporting guidelines to aid authors in preparing manuscripts. Following these guidelines could substantially improve the quality of statistical reports and enhance data rigor and reproducibility.

KW - Big data

KW - Cardiovascular disease

KW - Meta-research

KW - Meta-science

KW - Physiology

KW - Rigor and reproducibility

KW - Statistics

UR - http://www.scopus.com/inward/record.url?scp=85051293191&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051293191&partnerID=8YFLogxK

U2 - 10.1152/ajpheart.00309.2018

DO - 10.1152/ajpheart.00309.2018

M3 - Review article

VL - 315

SP - H303-H313

JO - American Journal of Physiology - Renal Physiology

JF - American Journal of Physiology - Renal Physiology

SN - 0363-6127

IS - 2

ER -