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系统评价中应考虑试验基线不平衡的影响:一项方法学案例研究。

The impact of trial baseline imbalances should be considered in systematic reviews: a methodological case study.

作者信息

Trowman Rebecca, Dumville Jo C, Torgerson David J, Cranny Gillian

机构信息

Epidemiology and Biostatistics, University of Leeds, Leeds LS2 9LN, UK.

出版信息

J Clin Epidemiol. 2007 Dec;60(12):1229-33. doi: 10.1016/j.jclinepi.2007.03.014. Epub 2007 Aug 24.

Abstract

OBJECTIVES

It is possible for baseline imbalances to occur between treatment groups for one or more variables in a randomized controlled trial, although the identification and detection of baseline imbalances remain controversial. If trials with baseline imbalances are combined in a meta-analysis, then this may result in misleading conclusions.

STUDY DESIGN AND SETTING

The identification and consequences of baseline imbalances in meta-analyses are discussed. Metaregression using mean baseline scores as a covariate is proposed as a potential method for adjusting baseline imbalances within meta-analysis. We will use a recent systematic review looking at the effect of calcium supplements on weight as an illustrative case study.

RESULTS

Meta-analysis conducted using the mean final values of the treatment groups as the outcome resulted in an apparent, statistically significant, treatment effect. However, using a meta-analysis of baseline values, this was shown to be due to the baseline imbalance between treatment groups, rather than as a result of any intervention received by the participants. Applying the method of metaregression demonstrated that there was in fact a smaller, statistically insignificant effect between treatment groups.

CONCLUSION

The meta-analyst should always consider the possibility of baseline imbalances and adjustments should be made wherever possible.

摘要

目的

在随机对照试验中,一个或多个变量的治疗组之间可能会出现基线不平衡,尽管基线不平衡的识别和检测仍存在争议。如果在荟萃分析中合并存在基线不平衡的试验,那么这可能会导致误导性结论。

研究设计与背景

讨论了荟萃分析中基线不平衡的识别及其后果。提出使用平均基线分数作为协变量的元回归作为在荟萃分析中调整基线不平衡的一种潜在方法。我们将以最近一项关于钙补充剂对体重影响的系统评价作为案例进行说明。

结果

以治疗组的平均最终值作为结果进行的荟萃分析产生了明显的、具有统计学意义的治疗效果。然而,通过对基线值进行荟萃分析表明,这是由于治疗组之间的基线不平衡,而不是参与者接受任何干预的结果。应用元回归方法表明,实际上治疗组之间的效果较小,且无统计学意义。

结论

荟萃分析者应始终考虑基线不平衡的可能性,并应尽可能进行调整。

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