Preventing bias from selective non-response in population-based survey studies: findings from a Monte Carlo simulation study
Preventing bias from selective non-response in population-based survey studies: findings from a Monte Carlo simulation study
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DOI：
10.1186/s12874-019-0757-1
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发表时间：
2019-06-13
影响因子：
4
通讯作者：
Borren, Ingrid
中科院分区：
文献类型：
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作者：
Gustavson, Kristin;Roysamb, Espen;Borren, Ingrid
背景健康研究人员经常使用调查研究来检查在一个时间点的危险因素和以后的生活中的健康结果之间的关联。以往的研究表明，在这类研究中，非随机缺失（MNAR）可能会产生有偏估计。医学研究人员通常不采用统计方法来治疗MNAR。因此，有必要增加有关如何防止发生这种偏差的知识摆在首位。MethodsMonte Carlo模拟被用来检查在何种程度上选择性不响应导致有偏见的估计风险因素和健康结果之间的关联时，最高水平的健康问题的人是代表不足或完全失踪的样本。这是根据不同的响应率和不同程度的依赖性之间的非响应和研究variable.ResultsResponse率本身的偏差影响不大。当健康结果的极端值完全缺失时，而不是代表性不足时，即使在70%的应答率下，结果也存在严重偏差。在大多数情况下，通过在样本中包括一些在健康结果方面得分极高的人，即使这些人的代表性不足，也可以防止50-100%的这种偏见。当一些极端的分数存在，协会的估计是公正的，在几种情况下，只有轻微的偏见，在其他情况下，只有当无反应是相关的风险因素和健康结果，以实质性degrees.ConclusionsThe潜在的预防偏见，包括一些极端的得分在样本中是高的（50-100%，在许多情况下）。在许多情况下，估计数可能相对无偏，在答复率低的情况下也是如此。因此，研究人员应该优先将资源用于招募和保留至少一些具有极端健康问题的人，而不是从通常对调查研究做出反应的人那里获得非常高的反应率。这可能有助于防止由于在风险因素和健康结果的纵向研究中选择性不回答而产生的偏倚。
BackgroundHealth researchers often use survey studies to examine associations between risk factors at one time point and health outcomes later in life. Previous studies have shown that missing not at random (MNAR) may produce biased estimates in such studies. Medical researchers typically do not employ statistical methods for treating MNAR. Hence, there is a need to increase knowledge about how to prevent occurrence of such bias in the first place.MethodsMonte Carlo simulations were used to examine the degree to which selective non-response leads to biased estimates of associations between risk factors and health outcomes when persons with the highest levels of health problems are under-represented or totally missing from the sample. This was examined under different response rates and different degrees of dependency between non-response and study variables.ResultsResponse rate per se had little effect on bias. When extreme values on the health outcome were completely missing, rather than under-represented, results were heavily biased even at a 70% response rate. In most situations, 50-100% of this bias could be prevented by including some persons with extreme scores on the health outcome in the sample, even when these persons were under-represented. When some extreme scores were present, estimates of associations were unbiased in several situations, only mildly biased in other situations, and became biased only when non-response was related to both risk factor and health outcome to substantial degrees.ConclusionsThe potential for preventing bias by including some extreme scorers in the sample is high (50-100% in many scenarios). Estimates may then be relatively unbiased in many situations, also at low response rates. Hence, researchers should prioritize to spend their resources on recruiting and retaining at least some individuals with extreme levels of health problems, rather than to obtain very high response rates from people who typically respond to survey studies. This may contribute to preventing bias due to selective non-response in longitudinal studies of risk factors and health outcomes.