Health indicators: Eliminating bias from convenience sampling estimators
Health indicators: Eliminating bias from convenience sampling estimators
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DOI：
10.1002/sim.3920
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发表时间：
2011-02-28
影响因子：
2
通讯作者：
Pagano, Marcello
中科院分区：
文献类型：
--
作者：
Hedt, Bethany L.;Pagano, Marcello
当唯一可用的信息是从方便的样本中收集的数据，例如从诊所来访者收集的数据时，公共卫生从业人员经常被要求对整个人口的健康指标作出推断。这些数据可能具有最高的质量和相当广泛的范围，但便利样本中固有的偏差妨碍了通常与随机样本相关的强大推断工具的合法使用。一般来说，我们对那些不去诊所的人一无所知，除了他们不去诊所这个事实。另一种选择是从总体中随机抽取样本。然而，我们表明，如果排除可用信息的使用，该解决方案将是浪费的。因此，我们提出了一种简单的退火方法，将相对较小的随机样本与方便样本相结合，并且可能便宜得多。这使我们不仅可以利用强大的推理工具，而且还可以提供比仅使用随机样本数据更准确的信息。版权所有：John Wiley & Sons， Ltd。
Public health practitioners are often called upon to make inference about a health indicator for a population at large when the sole available information are data gathered from a convenience sample, such as data gathered on visitors to a clinic. These data may be of the highest quality and quite extensive, but the biases inherent in a convenience sample preclude the legitimate use of powerful inferential tools that are usually associated with a random sample. In general, we know nothing about those who do not visit the clinic beyond the fact that they do not visit the clinic. An alternative is to take a random sample of the population. However, we show that this solution would be wasteful if it excluded the use of available information. Hence, we present a simple annealing methodology that combines a relatively small, and presumably far less expensive, random sample with the convenience sample. This allows us to not only take advantage of powerful inferential tools, but also provides more accurate information than that available from just using data from the random sample alone. Copyright (C) 2011 John Wiley & Sons, Ltd.