OBJECTIVE: To investigate whether comparing observed with expected p-value distributions for baseline continuous variables in randomised controlled trials (RCTs) might be limited by randomisation 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 randomisation methods were consistent with the theoretical uniform distribution and AUC-CDF respectively, although stratification and minimisation produced smaller-than-expected proportions of p-values <0.10. 77% of 3813 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: Randomisation 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.
- Statistical Methods
- Research Integrity
- Research integrity
- Statistical methods
- NORMAL POSTMENOPAUSAL WOMEN
- CALCIUM SUPPLEMENTATION
Rounding, but not randomization method, non-normality, or correlation, affected baseline P-value distributions in randomized trials
Bolland, M. J. (Creator), Gamble, G. D. (Creator) & Avenell, A. (Creator), Elsevier, 8 Mar 2019