What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data
What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data
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DOI：
10.1002/sim.1761
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发表时间：
2004-05-15
影响因子：
2
通讯作者：
Lambert, PC
中科院分区：
文献类型：
--
作者：
Sweeting, MJ;Sutton, AJ;Lambert, PC
目的：为了比较不同的荟萃分析方法的性能，汇集优势比时，适用于稀疏甚至data. Background的连续性corrections.Background的使用强调：荟萃分析的副作用，从随机对照试验或罕见疾病的危险因素在流行病学研究中经常需要稀疏事件率的数据合成。结合这些数据可能是有问题的，当零事件存在于一个或两个武器的研究，连续性校正往往是必要的，但是，这些可能会影响结果和conclusions.Methods：进行了模拟研究，比较几种荟萃分析方法结合优势比（使用各种经典和贝叶斯估计方法）稀疏事件数据。必要时，还比较了常规使用的恒定和两种替代连续性校正;一种基于相对组臂大小的倒数的函数;另一种是荟萃分析中剩余研究的汇总效应量的经验估计。一些荟萃分析的情况下进行模拟和复制1000倍，不同的研究手臂sizes.Results的比率：曼特尔-Haenszel总结估计使用替代连续性校正因子给所有组大小不平衡的最小偏差的结果。逻辑回归几乎是无偏的所有场景，并给出了良好的覆盖性能。Peto方法为平衡治疗组提供了无偏倚结果，但偏倚随着研究组大小的比例而增加。贝叶斯固定效应模型提供了良好的覆盖面，所有组规模的不平衡。这两种替代的连续性校正在几乎所有情况下都优于恒定校正因子。逆方差法一贯表现不佳，无论连续性correction used.Conclusions：许多常规使用的汇总方法提供广泛的估计时，应用到稀疏的数据与研究的武器的大小之间的高度不平衡。在常规实践中，提倡使用几种方法和连续性校正因子进行敏感性分析。版权所有（C）2004约翰威利父子有限公司。
Objectives: To compare the performance of different meta-analysis methods for pooling odds ratios when applied to sparse even data with emphasis on the use of continuity corrections.Background: Meta-analysis of side effects from RCTs or risk factors for rare diseases in epidemiological studies frequently requires the synthesis of data with sparse event rates. Combining such data can be problematic when zero events exist in one or both arms of a study as continuity corrections are often needed, but, these can influence results and conclusions.Methods: A simulation study was undertaken comparing several meta-analysis methods for combining odds ratios (using various classical and Bayesian methods of estimation) on sparse event data. Where required, the routine use of a constant and two alternative continuity corrections; one based on a function of the reciprocal of the opposite group arm size; and the other an empirical estimate of the pooled effect size from the remaining studies in the meta-analysis, were also compared. A number of meta-analysis scenarios were simulated and replicated 1000 times, varying the ratio of the study arm sizes.Results: Mantel-Haenszel summary estimates using the alternative continuity correction factors gave the least biased results for all group size imbalances. Logistic regression was virtually unbiased for all scenarios and gave good coverage properties. The Peto method provided unbiased results for balanced treatment groups but bias increased with the ratio of the study arm sizes. The Bayesian fixed effect model provided good coverage for all group size imbalances. The two alternative continuity corrections outperformed the constant correction factor in nearly all situations. The inverse variance method performed consistently badly, irrespective of the continuity correction used.Conclusions: Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and continuity correction factors is advocated for routine practice. Copyright (C) 2004 John Wiley Sons, Ltd.