title: Evolution of Reporting P Values in the Biomedical Literature, 1990-2015.
abstract: OBJECTIVE
  The use and misuse of P values has generated extensive debates.
  
  
  OBJECTIVE
  To evaluate in large scale the P values reported in the abstracts and full text of biomedical research articles over the past 25 years and determine how frequently statistical information is presented in ways other than P values.
  
  
  METHODS
  Automated text-mining analysis was performed to extract data on P values reported in 12,821,790 MEDLINE abstracts and in 843,884 abstracts and full-text articles in PubMed Central (PMC) from 1990 to 2015. Reporting of P values in 151 English-language core clinical journals and specific article types as classified by PubMed also was evaluated. A random sample of 1000 MEDLINE abstracts was manually assessed for reporting of P values and other types of statistical information; of those abstracts reporting empirical data, 100 articles were also assessed in full text.
  
  
  METHODS
  P values reported.
  
  
  RESULTS
  Text mining identified 4,572,043 P values in 1,608,736 MEDLINE abstracts and 3,438,299 P values in 385,393 PMC full-text articles. Reporting of P values in abstracts increased from 7.3% in 1990 to 15.6% in 2014. In 2014, P values were reported in 33.0% of abstracts from the 151 core clinical journals (n = 29,725 abstracts), 35.7% of meta-analyses (n = 5620), 38.9% of clinical trials (n = 4624), 54.8% of randomized controlled trials (n = 13,544), and 2.4% of reviews (n = 71,529). The distribution of reported P values in abstracts and in full text showed strong clustering at P values of .05 and of .001 or smaller. Over time, the "best" (most statistically significant) reported P values were modestly smaller and the "worst" (least statistically significant) reported P values became modestly less significant. Among the MEDLINE abstracts and PMC full-text articles with P values, 96% reported at least 1 P value of .05 or lower, with the proportion remaining steady over time in PMC full-text articles. In 1000 abstracts that were manually reviewed, 796 were from articles reporting empirical data; P values were reported in 15.7% (125/796 [95% CI, 13.2%-18.4%]) of abstracts, confidence intervals in 2.3% (18/796 [95% CI, 1.3%-3.6%]), Bayes factors in 0% (0/796 [95% CI, 0%-0.5%]), effect sizes in 13.9% (111/796 [95% CI, 11.6%-16.5%]), other information that could lead to estimation of P values in 12.4% (99/796 [95% CI, 10.2%-14.9%]), and qualitative statements about significance in 18.1% (181/1000 [95% CI, 15.8%-20.6%]); only 1.8% (14/796 [95% CI, 1.0%-2.9%]) of abstracts reported at least 1 effect size and at least 1 confidence interval. Among 99 manually extracted full-text articles with data, 55 reported P values, 4 presented confidence intervals for all reported effect sizes, none used Bayesian methods, 1 used false-discovery rates, 3 used sample size/power calculations, and 5 specified the primary outcome.
  
  
  CONCLUSIONS
  In this analysis of P values reported in MEDLINE abstracts and in PMC articles from 1990-2015, more MEDLINE abstracts and articles reported P values over time, almost all abstracts and articles with P values reported statistically significant results, and, in a subgroup analysis, few articles included confidence intervals, Bayes factors, or effect sizes. Rather than reporting isolated P values, articles should include effect sizes and uncertainty metrics.
authors.affiliation: Centre d'Analyse et de Mathématiques Sociales (CAMS), EHESS-CNRS UMR8557 and Complex Systems Institute of Paris Île-de-France (ISC-PIF, UPS3611), Paris, France.
authors.forename: David
authors.initials: D
authors.lastname: Chavalarias
authors.affiliation: Departments of Health Research and Policy and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California.
authors.forename: Joshua David
authors.initials: JD
authors.lastname: Wallach
authors.affiliation: Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada.
authors.forename: Alvin Ho Ting
authors.initials: AH
authors.lastname: Li
authors.affiliation: Departments of Medicine, Health Research and Policy, and Statistics, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California.
authors.forename: John P A
authors.initials: JP
authors.lastname: Ioannidis
mesh_terms: D057225:Data Mining / Q000706:statistics & numerical data; D016239:MEDLINE / Q000706:statistics & numerical data; D015233:Models, Statistical; D011336:Probability