Combined p-values of baseline variables of randomized controlled trials published in 2022 indicate non-randomness beyond chance
2Örebro University School of Business, Sweden
Background: Randomized controlled trials (RCT) are crucial for the evaluation of interventions. This, however, requires that the randomization is carried out correctly.
The anaesthetist Carlisle has developed a method to test whether the baseline variables of an RCT could reasonably originate from a true randomization, assuming the p-values are uniformly distributed. In a study from 2017, based on 5087 RCTs from 8 medical journals, 5.6% more RCTs than expected had a combined p-value>0.95 or p<0.05 .
Objectives: Apply Carlisle’s method to a sample of recent RCTs, and compare the findings to Carlisle’s results.
Methods: A sample of 1075 RCTs, published February 2022, indexed with the MeSH term ‘Randomized Controlled Trial’ in MEDLINE, were checked for eligibility. The inclusion criteria were primary/secondary analyses of RCTs providing number of participants, mean and standard deviation or standard error, of baseline variables. Carlisle’s method adopts Monte Carlo simulation, ANOVA and t-test to get p-values of baseline variables, and Stouffer’s method combines them for comparison to a uniform distribution, using R software. A smaller combined p-value indicate that the groups are similar; larger indicate that they are dissimilar.
Results: 566 RCTs were included and 13,085 means of 5780 (range 1-100) baseline variables were extracted. The proportion of p-values within p>0.95 or p<0.05, p<0.01 or p<0.00001 was 22.8%, 4.8% and 0.05% respectively, i.e. 2, 5 and 500 times larger than would be expected by chance (Table 1). Possible non-randomness was more common in this sample compared to Carlisle’s with the arbitrary limit of 0.95