Toward a standardized evaluation of imputation methodology
Toward a standardized evaluation of imputation methodology
复制标题
实现插补方法的标准化评估
DOI：
10.1002/bimj.202200107
复制
发表时间：
2023
影响因子：
1.7
通讯作者：
G. Vink
中科院分区：
文献类型：
--
作者：
Hanne I Oberman;G. Vink
发展新的估算方法已成为一个非常活跃的领域。不幸的是，在如何进行模拟研究以评估插补方法的属性方面没有达成共识。部分原因可能是由于不同领域和研究的目的不同。例如，在评估旨在预测的插补技术时，可能会制定与关注统计推断时不同的目标。缺乏共识也可能源于不同的个人偏好或科学背景。总而言之，在评价估算方法方面缺乏共同点可能导致在实践中的次优使用。在本文中，我们提出了一个走向标准化评价的插补方法。为了证明标准化的必要性，我们强调了一组可能的陷阱，带来了一系列潜在的问题，在客观评估的性能插补例程。此外，我们提出了一个模拟和评估缺失数据问题的行动方案。我们建议的行动方针绝不是要作为一本完整的食谱，而是要激发批判性思维，并推动对归因方法的客观和公正评价。我们请本文件的读者对建议的行动方针作出贡献。
Developing new imputation methodology has become a very active field. Unfortunately, there is no consensus on how to perform simulation studies to evaluate the properties of imputation methods. In part, this may be due to different aims between fields and studies. For example, when evaluating imputation techniques aimed at prediction, different aims may be formulated than when statistical inference is of interest. The lack of consensus may also stem from different personal preferences or scientific backgrounds. All in all, the lack of common ground in evaluating imputation methodology may lead to suboptimal use in practice. In this paper, we propose a move toward a standardized evaluation of imputation methodology. To demonstrate the need for standardization, we highlight a set of possible pitfalls that bring forth a chain of potential problems in the objective assessment of the performance of imputation routines. Additionally, we suggest a course of action for simulating and evaluating missing data problems. Our suggested course of action is by no means meant to serve as a complete cookbook, but rather meant to incite critical thinking and a move to objective and fair evaluations of imputation methodology. We invite the readers of this paper to contribute to the suggested course of action.
DOI：
10.1056/nejmsr1203730
发表时间：
2012-10-04
期刊：
The New England journal of medicine
影响因子：
--
作者：
Little RJ;D'Agostino R;Cohen ML;Dickersin K;Emerson SS;Farrar JT;Frangakis C;Hogan JW;Molenberghs G;Murphy SA;Neaton JD;Rotnitzky A;Scharfstein D;Shih WJ;Siegel JP;Stern H
通讯作者：
Stern H
用于分析非单调可忽略缺失数据的无响应模型。
影响因子：
2
作者：
Robins,JM;Gill,RD
通讯作者：
Gill,RD