混淆
统计
泊松回归
逻辑回归
泊松分布
计量经济学
协变量
计算机科学
结果(博弈论)
可能性
回归
膨胀(宇宙学)
信息偏差
选择偏差
数学
医学
数理经济学
物理
环境卫生
理论物理学
人口
作者
Sander Greenland,Mohammad Ali Mansournia,Douglas G. Altman
出处
期刊:BMJ
[BMJ]
日期:2016-04-27
卷期号:: i1981-i1981
被引量:540
摘要
Effects of treatment or other exposure on outcome events are commonly measured by ratios of risks, rates, or odds. Adjusted versions of these measures are usually estimated by maximum likelihood regression (eg, logistic, Poisson, or Cox modelling). But resulting estimates of effect measures can have serious bias when the data lack adequate case numbers for some combination of exposure and outcome levels. This bias can occur even in quite large datasets and is hence often termed sparse data bias. The bias can arise or be worsened by regression adjustment for potentially confounding variables; in the extreme, the resulting estimates could be impossibly huge or even infinite values that are meaningless artefacts of data sparsity. Such estimate inflation might be obvious in light of background information, but is rarely noted let alone accounted for in research reports. We outline simple methods for detecting and dealing with the problem focusing especially on penalised estimation, which can be easily performed with common software packages.
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