Biostatistics: A Bayesian Introduction

Description:

An essential introductory text linking traditional biostatistics with bayesian methods
In recent years, Bayesian methods have seen an explosion of interest, with applications in fields including biochemistry, ecology, medicine, oncology, pharmacology, and public health. As an interpretive system integrating data with observation, the Bayesian approach provides a nuanced yet mathematically rigorous means of conceptualizing biomedical statistics--from diagnostic tests to DNA evidence.
Biostatistics: A Bayesian Introduction offers a pioneering approach by presenting the foundations of biostatistics through the Bayesian lens. Using easily understood, classic Dutch Book thought experiments to derive subjective probability from a simple principle of rationality, the book connects statistical science with scientific reasoning. The author shows how to compute, interpret, and report Bayesian statistical analyses in practice, and illustrates how to reinterpret traditional statistical reporting--such as confidence intervals, margins of error, and one-sided p-values--in Bayesian terms. Topics covered include:
* Probability and subjective probability
* Distributions and descriptive statistics
* Continuous probability distributions
* Comparing rates and means
* Linear models and statistical adjustment
* Logistic regression and adjusted odds ratios
* Survival analysis
* Hierarchical models and meta-analysis
* Decision theory and sample size determination
The book includes extensive problem sets and references in each chapter, as well as complete instructions on computer analysis with the versatile SAS and WinBUGS software packages as well as the Excel spreadsheet program. For professionals and students, Biostatistics: A Bayesian Introduction offers an unique, real-world entry point into a remarkable alternative method of interpreting statistical data.

 

Table of Contents:

Preface.

1. Introduction to Statistical Science.

2. Probability.

3. Subjective Probability.

4. Distributions and Descriptive Statistics.

5. Statistical Inference.

6. Continuous Probability Distributions.

7. Comparing Two Rates.

8. Inference on Means.

9. Linear Models and Statistical Adjustment.

10. Logistic Regression.

11. Hierarchical Models.

12. Time to Event Analysis.

13. Decision Analysis.

A. Tables.

B. Introduction to WinBUGS.

C. Introduction to SAS System Software

Index.

描述：
最近幾年，貝葉斯方法在生物統計學領域引起了廣泛的興趣，並在生物化學、生態學、醫學、腫瘤學、藥理學和公共衛生等領域得到了應用。作為一種將數據與觀察相結合的解釋系統，貝葉斯方法提供了一種細緻而數學嚴謹的方式來概念化生物醫學統計學，從診斷測試到DNA證據。《生物統計學：貝葉斯導論》通過貝葉斯觀點呈現了生物統計學的基礎知識。書中使用易於理解的經典荷蘭書思想實驗，從理性的簡單原則中推導出主觀概率，將統計科學與科學推理相結合。作者展示了如何在實踐中計算、解釋和報告貝葉斯統計分析，並說明如何以貝葉斯術語重新解釋傳統的統計報告，如置信區間、誤差邊界和單側p值。書中涵蓋的主題包括：概率和主觀概率、分布和描述統計、連續概率分布、比較率和平均值、線性模型和統計調整、邏輯回歸和調整後的勝算比、生存分析、階層模型和元分析、決策理論和樣本量確定。該書每章都包含大量的問題集和參考文獻，以及有關使用多功能SAS和WinBUGS軟件包以及Excel電子表格程序進行計算分析的完整說明。對於專業人士和學生來說，《生物統計學：貝葉斯導論》提供了一個獨特的現實世界入口點，以了解一種非凡的統計數據解釋方法。

目錄：
前言
1. 統計科學簡介
2. 概率
3. 主觀概率
4. 分布和描述統計
5. 統計推斷
6. 連續概率分布
7. 比較兩個比率
8. 平均值的推斷
9. 線性模型和統計調整
10. 邏輯回歸
11. 階層模型
12. 時間到事件分析
13. 決策分析
A. 表格
B. WinBUGS入門
C. SAS系統軟件入門
索引