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A simulation study of diagnostics for selection bias.
Journal of Official Statistics ( IF 1.1 ) Pub Date : 2021-09-12 , DOI: 10.2478/jos-2021-0033
Philip S Boonstra 1 , Roderick J A Little 1, 2 , Brady T West 2 , Rebecca R Andridge 3 , Fernanda Alvarado-Leiton 2
Affiliation  

A non-probability sampling mechanism arising from non-response or non-selection is likely to bias estimates of parameters with respect to a target population of interest. This bias poses a unique challenge when selection is 'non-ignorable', i.e. dependent upon the unobserved outcome of interest, since it is then undetectable and thus cannot be ameliorated. We extend a simulation study by Nishimura et al. [International Statistical Review, 84, 43-62 (2016)], adding two recently published statistics: the so-called 'standardized measure of unadjusted bias (SMUB)' and 'standardized measure of adjusted bias (SMAB)', which explicitly quantify the extent of bias (in the case of SMUB) or non-ignorable bias (in the case of SMAB) under the assumption that a specified amount of non-ignorable selection exists. Our findings suggest that this new sensitivity diagnostic is more correlated with, and more predictive of, the true, unknown extent of selection bias than other diagnostics, even when the underlying assumed level of non-ignorability is incorrect.

中文翻译:

选择偏倚诊断的模拟研究。

因不响应或不选择而产生的非概率抽样机制可能会使与感兴趣的目标人群相关的参数估计产生偏差。This bias poses a unique challenge when selection is 'non-ignorable', ie dependent upon the unobserved outcome of interest, since it is then undetectable and thus cannot be ameliorated. 我们扩展了 Nishimura 等人的模拟研究。[International Statistical Review, 84, 43-62 (2016)],增加了两个最近发表的统计数据:所谓的“未调整偏倚的标准化度量(SMUB)”和“调整偏倚的标准化度量(SMAB)”,它们明确量化在存在特定数量的不可忽略选择的假设下,偏差的程度(在 SMUB 的情况下)或不可忽略的偏差(在 SMAB 的情况下)。
更新日期:2021-09-12
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