Statistical inference in abstracts of major medical and epidemiology journals 1975–2014: a systematic review
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Since its introduction in the twentieth century, null hypothesis significance testing (NHST), a hybrid of significance testing (ST) advocated by Fisher and null hypothesis testing (NHT) developed by Neyman and Pearson, has become widely adopted but has also been a source of debate. The principal alternative to such testing is estimation with point estimates and confidence intervals (CI). Our aim was to estimate time trends in NHST, ST, NHT and CI reporting in abstracts of major medical and epidemiological journals. We reviewed 89,533 abstracts in five major medical journals and seven major epidemiological journals, 1975–2014, and estimated time trends in the proportions of abstracts containing statistical inference. In those abstracts, we estimated time trends in the proportions relying on NHST and its major variants, ST and NHT, and in the proportions reporting CIs without explicit use of NHST (CI-only approach). The CI-only approach rose monotonically during the study period in the abstracts of all journals. In Epidemiology abstracts, as a result of the journal’s editorial policy, the CI-only approach has always been the most common approach. In the other 11 journals, the NHST approach started out more common, but by 2014, this disparity had narrowed, disappeared or reversed in 9 of them. The exceptions were JAMA, New England Journal of Medicine, and Lancet abstracts, where the predominance of the NHST approach prevailed over time. In 2014, the CI-only approach is as popular as the NHST approach in the abstracts of 4 of the epidemiology journals: the American Journal of Epidemiology (48%), the Annals of Epidemiology (55%), Epidemiology (79%) and the International Journal of Epidemiology (52%). The reporting of CIs without explicitly interpreting them as statistical tests is becoming more common in abstracts, particularly in epidemiology journals. Although NHST is becoming less popular in abstracts of most epidemiology journals studied and some widely read medical journals, it is still very common in the abstracts of other widely read medical journals, especially in the hybrid form of ST and NHT in which p values are reported numerically along with declarations of the presence or absence of statistical significance.
KeywordsStatistics Confidence intervals Statistics and numerical data
The authors thank Sander Greenland, DrPH for valuable comments on an early draft.
Andreas Stang receives a grant from the German Federal Ministry of Education and Science (BMBF), Grant Number 01ER1305.
Compliance with ethical standards
Conflict of interest
None of the authors declares a conflict of interest.
AS, MD, CP, and KJR were involved in the study design. AS and MD performed the statistical analyses. AS wrote the first draft of the report. All authors contributed to the final version.
- 12.Gastwirth JL. Statistical considerations support the supreme court’s decision in Matrixx Initiatives v. Siracusano. Jurimetrics. 2012;52:155–75.Google Scholar
- 14.Anonymous. Psychology journal bans P values. Nature 2015; 519:9.Google Scholar
- 25.Milne PH. Presentation graphics for engineering, science, and business. London: E & FN Spon; 2005.Google Scholar
- 30.Guidance for Industry. E9 Statistical Principles for Clinical Trials. Food and Drug Administration 1998. www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm073137.pdf. Accessed Oct 4, 2015.
- 31.Deeks JJ, Higgins JPT, Altman DG. Analysing data and undertaking meta-analyses. In: Higgins JPT, Green S, editors. Cochrane handbook for systematic reviews of interventions version 510 (updated March 2011): Cochrane Collaboration (www.handbook.cochrane.com); 2011.
- 32.Koricheva J, Gurevitch J. Place of meta-analysis among other methods of research synthesis. In: Koricheva J, Gurevitch J, Mengerson K, editors. Handbook of meta-analysis in ecology and evolution. Princeton: Princeton University Press; 2013. p. 1–13.Google Scholar
- 33.Freemantle N, Geddes J. Understanding and interpreting systematic reviews and meta-analyses. Part 2: meta-analyses. Evid Based. Mental Health. 1998;1:102–4.Google Scholar
- 34.Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to meta-analysis. Chichester: Wiley; 2009. P. 251–5, 297–302, 325–31.Google Scholar