Introduction
What is new?
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Covariate adjustment in post-hoc statistical analyses applied to the largest trial in traumatic brain injury (TBI) to date led to relative sample sizes of approximately 0.75 to attain the same power as the unadjusted reference analysis.
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Application of the strict selection and prognostic targeting strategies previously used in TBI included approximately 25% of the study population.
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Potential reductions in sample size can also be viewed in terms of the gain in power achievable for the same sample size. For example, a 20% reduction in sample size for a trial powered at 80% is equivalent to an increase in power to 87%.
The randomized controlled trial (RCT) is the most important tool to estimate effects of medical interventions [1]. When trials are designed to detect unrealistically large treatment effects, they are underpowered to detect more realistic moderate effects [2], [3], [4]. Traumatic brain injury (TBI) is an area where trials have frequently been underpowered [5], [6]. This is perhaps one of the reasons why current treatment guidelines do not include any class I recommendations (i.e., based on evidence from RCTs) [7]. Yet, with large numbers of deaths and high global burden of disease, treatments for TBI with even modest effects could have substantial public health benefits.
RCT populations, such as those in TBI, are typically heterogeneous in baseline characteristics and prognostic risk. More heterogeneous populations may require larger RCTs to detect differences because of treatment. Alternatively, such heterogeneity can be accounted for by the use of baseline characteristics in both the design and analysis phases of trials. In the design phase, these include the use of strict study enrolment criteria (strict selection) [8] or the selection of those with a specified level of risk of the outcome of interest (prognostic targeting) [9], [10], so that only individuals thought to gain most benefit from the treatment are enrolled in the trial. In the analysis phase, adjustment for baseline characteristics (covariate adjustment) can be used to account for differences between individuals in important prognostic factors of outcome [11], [12].
The three strategies of covariate adjustment, strict selection, and prognostic targeting were previously applied to the International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) database to assess their effect on power in six trials and three surveys of TBI, containing data from 8,033 individuals [13], [14], [15]. Because no significant treatment effects were demonstrated in the constituting studies, two such effects were simulated based on the odds ratio (OR) effect measure; one equally effective in all individuals, a so-called uniform effect and the second equally effective only in individuals with risk of the outcome of 20–80%, a so-called targeted effect. Although gains in power could be obtained with each of the three strategies, the design strategies of prognostic targeting and strict selection were inefficient because of up to 60% increases in study duration. Covariate adjustment led to gains around 25% for the required sample size in an earlier simulation study using the IMPACT database [16].
We aimed to evaluate the effects of covariate adjustment and related design strategies to deal with heterogeneity in a trial with a real, rather than simulated, treatment effect. We herein analyzed data from the Corticosteroid Randomization After Significant Head Injury (CRASH) trial of corticosteroid vs. placebo in 10,008 individuals [17], with its large, heterogeneous population.