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IMPACT Recommendations for Improving the Design and Analysis of Clinical Trials in Moderate to Severe Traumatic Brain Injury

期刊

NEUROTHERAPEUTICS
卷 7, 期 1, 页码 127-134

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.nurt.2009.10.020

关键词

Traumatic brain injury; clinical trials; study design; sliding dichotomy; covariate adjustment; prognosis

资金

  1. U.S. National Institutes of Health [R01 NS-042691]
  2. MRC [G0800803] Funding Source: UKRI
  3. Medical Research Council [G0800803] Funding Source: researchfish
  4. NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R01NS042691] Funding Source: NIH RePORTER

向作者/读者索取更多资源

Clinical trials in traumatic brain injury (TBI) pose complex methodological challenges, largely related to the heterogeneity of the population. The International Mission on Prognosis and Clinical Trial Design in TBI study group has explored approaches for dealing with this heterogeneity with the aim to optimize clinical trials in TBI. Extensive prognostic analyses and simulation studies were conducted on individual patient data from eight trials and three observational studies. Here, we integrate the results of these studies into the International Mission on Prognosis and Clinical Trial Design in TBI recommendations for design and analysis of trials in TBI: Details of the major baseline prognostic characteristics should be provided in every report on a TBI study; in trials they should be differentiated per treatment group. We also advocate the reporting of the baseline prognostic risk as determined by validated prognostic models. Inclusion criteria should be as broad as is compatible with the current understanding of the mechanisms of action of the intervention being evaluated. This will maximize recruitment rates and enhance the generalizability of the results. The statistical analysis should incorporate prespecified covariate adjustment to mitigate the effects of the heterogeneity. The statistical analysis should use an ordinal approach, based on either sliding dichotomy or proportional odds methodology. Broad inclusion criteria, prespecified covariate adjustment, and an ordinal analysis will promote an efficient trial, yielding gains in statistical efficiency of more than 40%. This corresponds to being able to detect a 7% treatment effect with the same number of patients needed to demonstrate a 10% difference with an unadjusted analysis based on the dichotomized Glasgow outcome scale.

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