Causal analyses of existing databases: no power calculations required.
Causal analyses of existing databases: no power calculations required.
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DOI：
10.1016/j.jclinepi.2021.08.028
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发表时间：
2022-04
影响因子：
7.2
通讯作者：
Hernán MA
中科院分区：
文献类型：
--
作者：
Hernán MA
观察数据库通常用于研究因果问题。在获得数据或资金之前，研究人员可能需要证明“他们分析的统计能力很高”。预期功效较低的分析将不会获得批准，从而导致不精确的估计。这种对观察分析的限制性态度是错误的。一个关键的误解是认为因果分析的目标是“检测”一种效应。因果效应不是可检测或不可检测的二元信号;因果效应是需要估计的数值。由于目标是尽可能无偏和精确地量化效应，因此对具有不精确效应估计的观察分析的解决方案不是避免具有不精确估计的观察分析，而是鼓励进行许多观察分析。最好是有多项研究与不精确的估计，而不是没有任何研究。在几项研究可用后，我们将对其进行荟萃分析，并提供更精确的汇总效应估计。因此，不对已有数据进行观察性分析的理由不能是我们的估计不精确。在进行将个体置于风险中的随机试验之前进行功效计算的伦理论据不能转移到现有数据库的观察性分析中。如果一个因果问题很重要，分析你的数据，发表你的估计，鼓励其他人也这样做，然后进行荟萃分析。另一种选择是一个没有答案的问题。
Observational databases are often used to study causal questions. Before being granted access to data or funding, researchers may need to prove that “the statistical power of their analysis will be high”. Analyses expected to have low power, and hence result in imprecise estimates, will not be approved. This restrictive attitude towards observational analyses is misguided. A key misunderstanding is the belief that the goal of a causal analysis is to “detect” an effect. Causal effects are not binary signals that are either detected or undetected; causal effects are numerical quantities that need to be estimated. Because the goal is to quantify the effect as unbiasedly and precisely as possible, the solution to observational analyses with imprecise effect estimates is not avoiding observational analyses with imprecise estimates, but rather encouraging the conduct of many observational analyses. It is preferable to have multiple studies with imprecise estimates than having no study at all. After several studies become available, we will meta-analyze them and provide a more precise pooled effect estimate. Therefore, the justification to withhold an observational analysis of pre- existing data cannot be that our estimates will be imprecise. Ethical arguments for power calculations before conducting a randomized trial which place individuals at risk are not transferable to observational analyses of existing databases. If a causal question is important, analyze your data, publish your estimates, encourage others to do the same, and then meta-analyze. The alternative is an unanswered question.
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