Mark J Bolland1, Greg D Gamble2, Alison Avenell3, Andrew Grey2. 1. Department of Medicine, University of Auckland, Private Bag 92 019, Auckland 1142, New Zealand. Electronic address: m.bolland@auckland.ac.nz. 2. Department of Medicine, University of Auckland, Private Bag 92 019, Auckland 1142, New Zealand. 3. Health Services Research Unit, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, Scotland.
Abstract
OBJECTIVES: To 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 SETTING: We 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. RESULTS: The 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. CONCLUSIONS: Randomization 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.
OBJECTIVES: To 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 SETTING: We 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. RESULTS: The 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. CONCLUSIONS: Randomization 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.
Authors: Colby J Vorland; Andrew W Brown; John A Dawson; Stephanie L Dickinson; Lilian Golzarri-Arroyo; Bridget A Hannon; Moonseong Heo; Steven B Heymsfield; Wasantha P Jayawardene; Chanaka N Kahathuduwa; Scott W Keith; J Michael Oakes; Carmen D Tekwe; Lehana Thabane; David B Allison Journal: Int J Obes (Lond) Date: 2021-07-29 Impact factor: 5.095