Matthew P. Fox: Applying Quantitative Bias Analysis to Epidemiologic Data
Applying Quantitative Bias Analysis to Epidemiologic Data
Buch
lieferbar innerhalb 2-3 Wochen
(soweit verfügbar beim Lieferanten)
(soweit verfügbar beim Lieferanten)
EUR 63,99*
Verlängerter Rückgabezeitraum bis 31. Januar 2025
Alle zur Rückgabe berechtigten Produkte, die zwischen dem 1. bis 31. Dezember 2024 gekauft wurden, können bis zum 31. Januar 2025 zurückgegeben werden.
- Springer International Publishing, 03/2023
- Einband: Kartoniert / Broschiert, Paperback
- Sprache: Englisch
- ISBN-13: 9783030826758
- Bestellnummer: 11443066
- Umfang: 484 Seiten
- Nummer der Auflage: 23002
- Auflage: 2nd ed. 2021
- Gewicht: 727 g
- Maße: 235 x 155 mm
- Stärke: 27 mm
- Erscheinungstermin: 26.3.2023
- Serie: Statistics for Biology and Health
Achtung: Artikel ist nicht in deutscher Sprache!
Weitere Ausgaben von Applying Quantitative Bias Analysis to Epidemiologic Data
Klappentext
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.