Regression Modeling and Meta-Analysis for Decision Making
讨论回归模型在决策中的使用，通过一个关于激励减少调查无应答的元分析案例，展示如何用回归推断估计成本与收益，并应用于纽约市社会指标调查。
AbstractRegression models are often used, explicitly or implicitly, for decision making. However, the choices made in setting up the models (e.g., inclusion of predictors based on statistical significance) do not map directly into decision procedures. Bayesian inference works more naturally with decision analysis but presents problems in practice when noninformative prior distributions are used with sparse data. We do not attempt to provide a general solution to this problem, but rather present an application of a decision problem in which inferences from a regression model are used to estimate costs and benefits. Our example is a reanalysis of a recent meta-analysis of incentives for reducing survey nonresponse. We then apply the results of our fitted model to the New York City Social Indicators Survey, a biennial telephone survey with a high nonresponse rate. We consider the balance of estimated costs, cost savings, and response rate for different choices of incentives. The explicit analysis of the decision problem reveals the importance of interactions in the fitted regression model.KEY WORDS: Decision analysisHierarchical linear regressionMeta-analysisSurvey nonresponse