Maximizing statistical power in neurological trials by covariate adjustment, exploiting ordinality and repeated assessments: A simulation study and application to GBS

ObjectiveRandomized controlled trials (RCTs) for rare neurological diseases, such as the Guillain-Barr ́esyndrome (GBS), have a disappointing lack of success, possibly due to inefficient statisticalanalysis. We aimed to evaluate the impact of covariate adjustment for baseline characteristics,ordinal analysis and repeated assessments on statistical power in randomized controlled trialswith ordinal scales as outcome measure.MethodsWe re-analysed a previous trial in GBS (the IVIg + placebo vs IVIg + Methylprednisolone trial,n= 221) and conducted power simulations to assess performance of different approaches foranalysis of ordinal scales such as the GBS Disability Scale under different conditions. The ap-proaches consist of binary logistic regression and proportional odds logistic regression, with andwithout covariate adjustment for important prognostic factors (MRC sum score and days sinceonset of weakness to randomisation). The conditions consist of satisfaction of the...Show moreObjectiveRandomized controlled trials (RCTs) for rare neurological diseases, such as the Guillain-Barr ́esyndrome (GBS), have a disappointing lack of success, possibly due to inefficient statisticalanalysis. We aimed to evaluate the impact of covariate adjustment for baseline characteristics,ordinal analysis and repeated assessments on statistical power in randomized controlled trialswith ordinal scales as outcome measure.MethodsWe re-analysed a previous trial in GBS (the IVIg + placebo vs IVIg + Methylprednisolone trial,n= 221) and conducted power simulations to assess performance of different approaches foranalysis of ordinal scales such as the GBS Disability Scale under different conditions. The ap-proaches consist of binary logistic regression and proportional odds logistic regression, with andwithout covariate adjustment for important prognostic factors (MRC sum score and days sinceonset of weakness to randomisation). The conditions consist of satisfaction of the proportionalodds assumption, the use of weaker prognostic baseline characteristics, and quantitative versusqualitative violation of the proportional odds assumption. We extended these approaches to alongitudinal proportional odds model. Simulations varied in sample size and treatment effect.ResultsCovariate adjustment led to an increased estimated treatment effect and increased standard errorin the GBS trial. Proportional odds analysis decreased the standard error in comparison to abinary logistic regression analysis, indicating a more sensitive analysis. The longitudinal pro-portional odds resulted in a larger standard error as compared to single time point proportionalodds analyses. Simulations for analysis of continuous data with a linear mixed model confirmedthat a longitudinal approach does not increase power as compared to a single time point analysisin case of a low within-subject variance, as was observed for the GBS trial. In simulations wefocused on the effect of covariate adjustment and ordinal analysis. Simulations indicated thatType I errors were generally around 5%. A small gain in power was achieved by covariate ad-justment for two known prognostic factors in GBS, and a larger gain by exploiting ordinalityinstead of dichotomizing the ordinal scale. The gains translated to a gain in power of up to 7 and13% points by covariate adjustment and exploiting ordinality respectively. The gains in powerwere only slightly smaller under violation of the proportional odds assumption and with smallerprognostic effects of the covariates.ConclusionOptimal analysis of ordinal scales should adjust for baseline characteristics (covariate adjust-ment) and should respect the ordinality of the outcome measure. A longitudinal proportionalodds model for analysis of repeated assessments may not have added benefit as compared to asingle time point proportional odds model. Further research should confirm that the use of alongitudinal proportional odds model is only beneficial when the observed disease course withinpatients is more variable over time.Show less

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Maximizing statistical power in neurological trials by covariate adjustment, exploiting ordinality and repeated assessments: A simulation study and application to GBS