國立虎尾科技大學 |

Applying Quantitative Bias Analysis to Epidemiologic Data

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Applying Quantitative Bias Analysis to Epidemiologic Data/ by Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash.
作者:	Fox, Matthew P.
其他作者:	Lash, Timothy L.
面頁冊數:	XVI, 467 p. 76 illus., 39 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Health Informatics. -
電子資源:	https://doi.org/10.1007/978-3-030-82673-4
ISBN:	9783030826734

Applying Quantitative Bias Analysis to Epidemiologic Data
Fox, Matthew P.

Applying Quantitative Bias Analysis to Epidemiologic Data[electronic resource] /by Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash. - 2nd ed. 2021. - XVI, 467 p. 76 illus., 39 illus. in color.online resource. - Statistics for Biology and Health,2197-5671. - Statistics for Biology and Health,.

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.

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.

ISBN: 9783030826734

Standard No.: 10.1007/978-3-030-82673-4doiSubjects--Topical Terms:

593963
Health Informatics.

LC Class. No.: QH323.5

Dewey Class. No.: 570.15195

Applying Quantitative Bias Analysis to Epidemiologic Data
LDR:03472nam a22004095i 4500 001 1059456
003 DE-He213
005 20220324154124.0
007 cr nn 008mamaa
008 220414s2021 sz | s |||| 0|eng d
020 $a 9783030826734 $9 978-3-030-82673-4
024 7 $a 10.1007/978-3-030-82673-4 $2 doi
035 $a 978-3-030-82673-4
050 4 $a QH323.5
072 7 $a PBT $2 bicssc
072 7 $a MED090000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 570.15195 $2 23
100 1 $a Fox, Matthew P. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 768935
245 1 0 $a Applying Quantitative Bias Analysis to Epidemiologic Data $h [electronic resource] / $c by Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash.
250 $a 2nd ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XVI, 467 p. 76 illus., 39 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Statistics for Biology and Health, $x 2197-5671
505 0 $a 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.
520 $a 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.
650 2 4 $a Health Informatics. $3 593963
650 2 4 $a Public Health. $3 592982
650 1 4 $a Biostatistics. $3 783654
650 0 $a Biotechnology. $3 554955
650 0 $a Medical informatics. $3 583858
650 0 $a Public health. $3 560998
650 0 $a Bioinformatics. $3 583857
650 0 $a Epidemiology. $3 635923
650 0 $a Biometry. $3 598268
700 1 $a Lash, Timothy L. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 768934
700 1 $a MacLehose, Richard F. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1366166
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030826727
776 0 8 $i Printed edition: $z 9783030826741
776 0 8 $i Printed edition: $z 9783030826758
830 0 $a Statistics for Biology and Health, $x 1431-8776 $3 1254965
856 4 0 $u https://doi.org/10.1007/978-3-030-82673-4
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)