Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Paperback)

There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield ..".students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.

有許多書籍是關於個別統計工具（生存模型、一般線性模型等）的知識寶庫，但資料分析的藝術在於選擇和使用多種工具。用 Chatfield 的話來說，「學生通常對於回歸等技術細節很熟悉，但不一定知道何時以及如何應用它們。這說明了文獻和統計教學中需要更好地平衡技術和問題解決策略。」無論是分析風險因素、調整觀察研究中的偏差，還是開發預測模型，都存在一些常見問題，但很少有回歸分析的文獻涉及這些問題。例如，大多數數據集（除了教科書中使用的數據集）中都存在缺失數據，但大多數回歸分析的文獻並未包含有效處理此類數據的方法，而缺失數據的文獻也未涵蓋回歸建模。