Comparing Stochastic Optimization Methods for Variable Selection in Binary Outcome Prediction, With Application to Health Policy
Comparing Stochastic Optimization Methods for Variable Selection in Binary Outcome Prediction, With Application to Health Policy
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DOI：
10.1198/016214508000001048
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发表时间：
2008-12
影响因子：
3.7
通讯作者：
D. Fouskakis;D. Draper
中科院分区：
文献类型：
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作者：
D. Fouskakis;D. Draper
在广义线性模型（GLMs）中，传统的变量选择策略寻求在不考虑数据收集成本的情况下优化预测精度。当这种模型构建的目的是创建用于未来预算有限的研究的预测尺度时，标准方法可能不是最优的。我们提出了一个贝叶斯决策理论框架，用于二元结果glm中的变量选择，其中数据收集的预算受到限制，潜在的预测者可能在成本上有很大差异。该方法是用20世纪80年代美国医院护理质量的大型研究数据来说明的。特别是当可用预测因子p的数量很大时，使用适当的技术进行优化是很重要的（例如，在这里展示的应用程序中，p = 83，我们搜索的空间有283个要素，这太大了，无法使用暴力枚举进行探索）。具体来说，我们研究了运筹学中使用的模拟退火（SA）、遗传算法（GAs）和禁忌搜索（TS）方法，并开发了一种情境特定版本的SA，即改进的模拟退火（ISA），其性能优于通用版本。当p在我们的研究中适中时，我们发现除了最好的用户定义输入配置外，GAs在所有配置中表现相对较差，通用SA表现不佳，而TS具有出色的中位数性能，并且对用户定义输入的次优选择不太敏感。在我们的研究中，当p较大时，GA和ISA的最佳版本优于TS和通用SA。我们的结果是在卫生政策的背景下提出的，但也可以适用于其他具有二分结果的质量评估设置。
Traditional variable-selection strategies in generalized linear models (GLMs) seek to optimize a measure of predictive accuracy without regard for the cost of data collection. When the purpose of such model building is the creation of predictive scales to be used in future studies with constrained budgets, the standard approach may not be optimal. We propose a Bayesian decision-theoretic framework for variable selection in binary-outcome GLMs where the budget for data collection is constrained and potential predictors may vary considerably in cost. The method is illustrated using data from a large study of quality of hospital care in the U.S. in the 1980s. Especially when the number of available predictors p is large, it is important to use an appropriate technique for optimization (e.g., in an application presented here where p = 83, the space over which we search has 283 ≐ 1025 elements, which is too large to explore using brute force enumeration). Specifically, we investigate simulated annealing (SA), genetic algorithms (GAs), and the tabu search (TS) method used in operations research, and we develop a context-specific version of SA, improved simulated annealing (ISA), that performs better than its generic counterpart. When p was modest in our study, we found that GAs performed relatively poorly for all but the very best user-defined input configurations, generic SA did not perform well, and TS had excellent median performance and was much less sensitive to suboptimal choice of user-defined inputs. When p was large in our study, the best versions of GA and ISA outperformed TS and generic SA. Our results are presented in the context of health policy but can apply to other quality assessment settings with dichotomous outcomes as well.