000			04314nam a22005655i 4500
001			978-0-387-87959-8
003			DE-He213
005			20251006084428.0
007			cr nn 008mamaa
008			110413s2009 xxu| s |||| 0|eng d
020			_a9780387879598
020			_a99780387879598
024	7		_a10.1007/978-0-387-87959-8 _2doi
082	0	4	_a613 _223
082	0	4	_a614 _223
100	1		_aLash, Timothy L. _eauthor.
245	1	0	_aApplying Quantitative Bias Analysis to Epidemiologic Data _h[electronic resource] / _cby Timothy L. Lash, Matthew P. Fox, Aliza K. Fink.
264		1	_aNew York, NY : _bSpringer New York, _c2009.
300			_aXII, 192 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics for Biology and Health, _x1431-8776
505	0		_aIntroduction, Objectives, and an Alternative -- A Guide to Implementing Quantitative Bias Analysis -- Data Sources for Bias Analysis -- Selection Bias -- Unmeasured and Unknown Confounders -- Misclassification -- Multidimensional Bias Analysis -- Probabilistic Bias Analysis -- Multiple Bias Modeling -- Presentation and Inference.
520			_aThis text provides the first-ever compilation of bias analysis methods for use with epidemiologic data. It guides the reader 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 classification errors. Subsequent chapters extend these methods to multidimensional bias analysis, probabilistic bias analysis, and multiple bias analysis. The text concludes with a chapter on presentation and interpretation of bias analysis results. Although techniques for bias analysis have been available for decades, these methods are considered difficult to implement. This text not only gathers the methods into one cohesive and organized presentation, it also explains the methods in a consistent fashion and provides customizable spreadsheets to implement the solutions. By downloading the spreadsheets (available at links provided in the text), readers can follow the examples in the text and then modify the spreadsheet to complete their own bias analyses. Readers without experience using quantitative bias analysis will be able to design, implement, and understand bias analyses that address the major threats to the validity of epidemiologic research. More experienced analysts will value the compilation of bias analysis methods and links to software tools that facilitate their projects. Timothy L. Lash is an Associate Professor of Epidemiology and Matthew P. Fox is an Assistant Professor in the Center for International Health and Development, both at the Boston University School of Public Health. Aliza K. Fink is a Project Manager at Macro International in Bethesda, Maryland. Together they have organized and presented many day-long workshops on the methods of quantitative bias analysis. In addition, they have collaborated on many papers that developed methods of quantitative bias analysis or used the methods in the data analysis.
650		0	_aMEDICINE.
650		0	_aEMERGING INFECTIOUS DISEASES.
650		0	_aEPIDEMIOLOGY.
650		0	_aCOMPUTER SIMULATION.
650		0	_aSTATISTICS.
650		0	_aSOCIAL SCIENCES _xMETHODOLOGY.
650	1	4	_aMEDICINE & PUBLIC HEALTH.
650	2	4	_aPUBLIC HEALTH/GESUNDHEITSWESEN.
650	2	4	_aEPIDEMIOLOGY.
650	2	4	_aSTATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES.
650	2	4	_aMETHODOLOGY OF THE SOCIAL SCIENCES.
650	2	4	_aINFECTIOUS DISEASES.
650	2	4	_aSIMULATION AND MODELING.
700	1		_aFox, Matthew P. _eauthor.
700	1		_aFink, Aliza K. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387879604
830		0	_aStatistics for Biology and Health, _x1431-8776
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-87959-8 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59355 _d59355