Applying Quantitative Bias Analysis to Epidemiologic Data (Statistics for Biology and Health) (2ND)

個数:

Applying Quantitative Bias Analysis to Epidemiologic Data (Statistics for Biology and Health) (2ND)

  • 【入荷遅延について】
    世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
    おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。
  • ◆画像の表紙や帯等は実物とは異なる場合があります。
  • ◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
    また、洋書販売価格は、ご注文確定時点での日本円価格となります。
    ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。
  • 製本 Hardcover:ハードカバー版/ページ数 467 p.
  • 言語 ENG
  • 商品コード 9783030826727

Full Description

This textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods.

As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing:

Measurement error pertaining to continuous and polytomous variables
Methods surrounding person-time (rate) data
Bias analysis using missing data, empirical (likelihood), and Bayes methods

A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.

Contents

1. Introduction and Objectives.- 2. A Guide to Implementing Quantitative Bias Analysis.- 3. Data Sources for Bias Analysis.- 4. Selection Bias.- 5. Uncontrolled Confounders.- 6. Misclassification.- 7. Measurement Error for Continuous Variables.- 8. Multiple Bias Modeling.- 8. Bias Analysis by Simulation for Summary Level Data.- 9. Bias Analysis by Simulation for Record Level Data.- 10. Combining Systematic and Random Error.- 11. Bias Analysis by Missing Data Methods.- 12. Bias Analysis by Empirical Methods.- 13. Bias Analysis by Bayesian Methods.- 14. Multiple Bias Modeling.- 15. Good Practices for Quantitative Bias Analysis.- 15. Presentation and Inference.- References.- Index.

最近チェックした商品