Should Stroke Trials Adjust Functional Outcome for Baseline Prognostic Factors?
Should Stroke Trials Adjust Functional Outcome for Baseline Prognostic Factors?
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DOI：
10.1161/strokeaha.108.519207
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发表时间：
2009-03-01
期刊：
影响因子：
8.3
通讯作者：
Collier, Timothy
中科院分区：
文献类型：
--
作者：
Bath, Philip;Gray, Laura J.;Collier, Timothy
背景和目的--许多中风试验都提供了中性的结果。次优的统计分析可能无法发现有效的干预措施。在分析中根据基线预后因素调整结果可能会提高结果分析的效率。方法-包括23项中风试验(25674名患者)的数据，评估功能结果。考虑的预后变量包括年龄、性别和基线严重程度。使用来自每个试验的模拟数据(每个试验10000个模拟)比较未调整和调整后的有序Logistic回归模型。评价三个水平的治疗效果，OR值分别为0.95、0.74和0.57。结果-根据基线因素调整结果导致样本量减少，这在所有3种处理效果(中位数百分比减少，四分位数范围)中是相似的：OR=0.95：35.3%(21.0~42.1)；OR=0.74：38.4%(29.4~42.7)；OR=0.57：38.4%(27.4~42.2)。随着治疗效果的增加，调整后的模型的治疗效果大于未调整的模型的模拟比例也增加。结论对中风试验中的预后因素进行调整，对于给定的功率，可以减少至少20%到30%的样本量(较低的四分位数范围)。相反，试验者可能希望进行未经调整的分析，然后通过对预测因素进行调整来增加统计能力。(笔划。2009年；40：888-894。)
Background and Purpose-Many stroke trials have provided neutral results. Suboptimal statistical analyses may be failing to detect effective interventions. Adjusting outcomes for baseline prognostic factors in the analysis may improve the efficiency of analysis of outcomes.Methods-Data from 23 stroke trials (25 674 patients) assessing functional outcome were included. The prognostic variables considered were age, sex, and baseline severity. Unadjusted and adjusted ordinal logistic regression models were compared using simulated data from each trial (10 000 simulations per trial). Three levels of treatment effect were assessed with ORs of 0.95, 0.74, and 0.57. The reduction in sample size gained from using the adjusted models, as compared with an unadjusted model, was then calculated as a reflection of the increase in statistical power.Results-Adjusting outcome for baseline factors led to a reduction in sample size, which was similar across all 3 treatment effects (median percentage reduction, interquartile range): OR = 0.95: 35.3% (21.0 to 42.1); OR = 0.74: 38.4% (29.4 to 42.7); and OR = 0.57: 38.4% (27.4 to 42.2). As the treatment effect increased, the proportion of simulations in which the treatment effect for the adjusted model was greater than for the unadjusted model also increased.Conclusion-Adjusting for prognostic factors in stroke trials can reduce sample size by at least 20% to 30% (the lower interquartile range) for a given power. Conversely, trialists may want to power for an unadjusted analysis and then increase statistical power by adjusting for prognostic factors. (Stroke. 2009; 40: 888-894.)