• Mark J Bolland, Greg D Gamble, Alison Avenell, and Andrew Grey.
• Department of Medicine, University of Auckland, Private Bag 92 019, Auckland 1142, New Zealand. Electronic address: m.bolland@auckland.ac.nz.
• J Clin Epidemiol. 2019 Jun 1; 110: 50-62.

ObjectivesTo investigate whether comparing observed with expected P-value distributions for baseline continuous variables in randomized controlled trials (RCTs) might be limited by randomization methods, normality and correlation of variables, or calculation of P-values from rounded summary statistics.Study Design And SettingWe assessed how each factor affects differences from expected for P-value distributions and area under the curve of the cumulative distribution function (AUC-CDF) of baseline P-values in 13 RCTs and in simulations.ResultsThe P-value distributions and AUC-CDF for variables with possible non-normal distribution and in simulations using eight different randomization methods were consistent with the theoretical uniform distribution and AUC-CDF, respectively, although stratification and minimization produced smaller-than-expected proportions of P-values <0.10. Seventy-seven percentage of 3,813 pairwise correlations between baseline variables in the 13 individual RCTs were between -0.2 and 0.2. P-value distribution and AUC-CDF remained consistent with the uniform distribution in simulations with incrementally increasing correlation strength. The P-value distributions calculated from rounded summary statistics were not uniform, but expected distributions could be empirically generated.ConclusionsRandomization methods, non-normality, and strength of correlation of baseline variables did not have important effects on baseline P-value distribution or AUC-CDF, but baseline P-values calculated from rounded summary statistics are non-uniformly distributed.Copyright © 2019 Elsevier Inc. All rights reserved.

21 keywords...

hide…