Simple approaches to assess the possible impact of missing outcome information on estimates of risk ratios, odds ratios, and risk differences
Simple approaches to assess the possible impact of missing outcome information on estimates of risk ratios, odds ratios, and risk differences
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DOI：
10.1016/s0197-2456(03)00021-7
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发表时间：
2003-08-01
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影响因子：
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通讯作者：
Magder, LS
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文献类型：
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作者：
Magder, LS
通常在临床试验中，主要结局是二元的，干预的影响是用风险比（rr）、优势比（ORs）或风险差异（rd）来总结的。典型的是，在这类研究中，一些研究参与者没有观察到二元结果变量。当存在缺失数据时，众所周知，基于具有完整数据的参与者的分析可能存在偏差，除非可以假设缺失结果的概率与缺失的二进制结果的值无关（即随机缺失）。不幸的是，这一假设无法用数据来评估，因为根据定义，没有观察到缺失的结果。解决这个问题的一种方法是进行敏感性分析，看看在随机假设与缺失数据偏离不同程度的情况下，仅基于完整数据得出的结论受到影响的程度。在本文中，我们为研究人员提供了进行这种敏感性分析的公式。我们用一个我们称为“响应概率比”（RPR）的参数来量化与随机假设缺失的偏差。这是具有一个二元结果值的人得到一个非缺失结果的概率与具有另一个结果值的人得到一个非缺失结果的概率之间的比率。然后，我们提供了简单的公式，用于在给定任何特定rpr值的情况下估计RRs、or和rrd。除了对敏感性分析有用之外，这些公式还提供了对发生偏差所必需的条件的一些见解。特别是，可以看到，在某些貌似合理的假设下，即使缺失结果的概率同时取决于治疗和结果，基于数据完整的参与者的OR估计也将是渐近无偏的。(C) 2003爱思唯尔科学有限公司版权所有。
Often in clinical trials, the primary outcome is binary and the impact of an intervention is summarized using risk ratios (RRs), odds ratios (ORs), or risk differences (RDs). It is typical that in such studies, the binary outcome variable is not observed for some study participants. When there is missing data, it is well known that analyses based on those participants with complete data can be biased unless it can be assumed that the probability of a missing outcome is unrelated to the value of the missing binary outcome (i.e., missing at random). Unfortunately, this assumption cannot be assessed with the data since the missing outcomes, by definition, are not observed. One approach to this problem is to perform a sensitivity analysis to see the degree to which conclusions based only on the complete data would be affected given various degrees of departure from the missing at random assumption. In this paper we provide researchers formulae for doing such a sensitivity analysis. We quantify the departure from the missing at random assumption with a parameter we call the "response probability ratio" (RPR). This is the ratio between the probability of a nonmissing outcome among those with one value of the binary outcome and the probability of a nonmissing outcome among those with the other value of the outcome. Then we provide simple formulae for the estimation of the RRs, ORs, and RDs given any specific values of the RPRs. In addition to being useful for sensitivity analyses, these formulae provide some insight into the conditions that are necessary for bias to occur. In particular, it can be seen that, under certain plausible assumptions, OR estimates based on participants with complete data will be asymptotically unbiased, even if the probability of missing outcome depends on both the treatment and the outcome. (C) 2003 Elsevier Science Inc. All rights reserved.