Walsh Daniel P, Norton Andrew S, Storm Daniel J, Van Deelen Timothy R, Heisey Dennis M
National Wildlife Health CenterU.S. Geological Survey Madison WI USA.
Department of Forest and Wildlife Ecology University of Wisconsin-Madison Madison WI USA.
Ecol Evol. 2017 Nov 30;8(1):509-520. doi: 10.1002/ece3.3701. eCollection 2018 Jan.
Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.
在生态分析中隐性和显性地运用专家知识变得越来越普遍，因为在许多情况下它往往是唯一的信息来源。因此，有必要开发明确纳入专家知识的统计方法，这些方法能够在分析过程中成功利用这些信息，同时妥善考虑相关的不确定性。当死亡原因不确定且根据观察者对最可能原因的了解来确定时，特定原因死亡率的研究就是隐性运用专家知识的一个例子。为了明确纳入这种对专家知识的运用以及相关的不确定性，我们在贝叶斯分层框架内使用数据增强方法开发了一个用于估计特定原因死亡率的统计模型。具体而言，对于每一个死亡事件，我们通过让观察者指定死亡归因于每个潜在原因的概率来引出他们对死亡原因的信念。然后这些概率在我们的框架中用作先验预测值。这个分层框架允许一种简单而严谨的估计方法，该方法很容易修改以纳入协变量效应和正则化项。尽管该方法应用于生存分析，但它可以扩展到任何具有多种事件类型的事件时间分析，对于这些分析，真实结果存在不确定性。我们进行了模拟，以确定我们的框架与隐性使用专家知识并假设死亡原因被准确指定的传统方法相比如何。模拟结果支持在特定原因死亡率建模中纳入观察者在死亡原因分配方面的不确定性，以提高模型性能和推断能力。最后，我们将我们开发的统计模型和一种传统方法应用于白尾鹿的特定原因生存数据，并比较结果。我们证明两种方法之间的模型选择结果有所不同，并且与传统方法相比，在死亡原因中纳入观察者知识会增加与参数估计相关的变异性。这两种方法之间的这些差异可能会影响报告的结果，因此，在统计方法中明确纳入专家知识以确保严谨的推断至关重要。
