Statistical Analysis of Aneurysmal Subarachnoid Hemorrhage Trials

Abstract Background: Many randomized controlled trials (RCT) have assessed new treatments in subarachnoid hemorrhage (SAH), yet most show no treatment efficacy. One explanation is the statistical analysis of the primary endpoint was not as efficient as possible. We reanalyzed SAH RCTs with various statistical tests to determine whether the statistical method affects RCT primary outcome.Methods: Individual patient data for the primary outcome (Glasgow outcome scale [GOS]) of two SAH RCTs were analyzed using 15 statistical methods. For tests requiring outcome dichotomization, multiple cut-points in the 5-level GOS were assessed. Next, a synthetic dataset generated using random sampling with replacement from ten SAH RCTs was assessed using the same statistical tests. A Friedman test (two-way non-parametric analysis of variance) determined which tests produced the highest average absolute Z-values. The number of times each test reported significance of p<0.05 across the different datasets was calculated.Results: Bootstrapping with replacement produced the best-ranking results, followed by three χ2-tests: one differentiating excellent (GOS=5) from good (GOS=4), poor (GOS=2-3), or dead (GOS=1) outcomes; one differentiating favorable (GOS=4-5) from poor or dead outcomes; and one differentiating favorable (GOS=4-5) from unfavorable outcomes. Each of these reported statistical significance for both RCTs, as did the following ranked tests, respectively: Wilcoxon median test, Student’s t-test, ordinal logistic regression, median test, and a chi-square dichotomizing excellent (GOS≥4) and inferior outcomes. Statistical significance for one or neither RCT was reported by two Cochrane-Armitage tests and two logistic regressions with alternate versions of bucketing, the Kolmogorov-Smirnov test and chi-square test differentiating surviving from dead patients. The synthetic dataset returned similar results, with the same nine most and six least efficient tests.Conclusions: Bootstrapping produced the most efficient results but is time-and resource-intensive. Chi-square tests grouping outcomes into dichotomous or multiple buckets are also efficient, and their ease of use and popularity make them appropriate candidates for statistical analysis in future SAH RCTs.