Testing for baseline differences in clinical trials
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Date
2020-04
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Publisher
Medip Academy
Abstract
Reporting statistical tests for baseline measures of clinical trials does not make sense since the statistical significance is dependent on sample size, as a large trial can find significance in the same difference that a small trial did not find to be statistically significant.We use 3 published trials using the same baseline measures to provide the relationship between trial sample size and p value.For trial 1 sequential organ failure assessment (SOFA)score, p=0.01, 10.4±3.4 vs. 9.6±3.2, difference=0.8; p=0.007 for vasopressors, 83.0% vs. 72.6%. Trial 2 has SOFA score 11±3 vs. 12±3, difference=1, p=0.42. Trial 3 has vasopressors 73% vs. 83%, p=0.21. Based on trial 2, supine group has a mean of 12 and an SD of 3 for SOFA score, while prone group has a mean of 11 and an SD of 3 for SOFA score. The pvalues are 0.29850, 0.09877, 0.01940, 0.00094, 0.00005, and <0.00001 when n (per arm) is 20, 50, 100, 200, 300 and 400, respectively.Based on trial 3 information, the vasopressors percentages are 73.0% in the supine group vs. 83.0% in the prone group. The pvalues are 0.4452, 0.2274, 0.0878, 0.0158, 0.0031, and 0.0006 when n (per arm) is 20, 50, 100, 200, 300 and 400, respectively.Small trials provide larger pvalues than big trials for the same baseline differences. We cannot define the imbalance in baseline measures only based on these pvalues. There is no statistical basis for advocating the baseline difference tests
Description
Keywords
Baseline difference, Statistical significant testing, Randomization, Trial size
Citation
Henian Chen, Yuanyuan Lu, Nicole Slye. Testing for baseline differences in clinical trials . International Journal of Clinical Trials. 2020 Apr; 7(2): 150-153