000			02755cam a2200337 i 4500
999			_c5743 _d5743
001			324643
003			0000000000
005			20180131105903.0
008			150617s2016 nyua b 001 0 eng
010			_a 2015024102
020			_a9780826110251 (pbk)
040			_erda
050	0	0	_aR 853.S7 _b.C360 2016
100	1		_aChan,Bertram Kim-Cheong _919908
245	1	0	_aBiostatistics for epidemiology and public health using R / _cBertram K.C. Chan, PhD, PE.
260			_aNew York : _bSpringer Publishing Company, _c[2016].
264		0	_aNew York : _bSpringer Publishing Company, _c[2016].
265			_aFFB
300			_axii, 446 pages : _billustrations (some color) ; _c26 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aResearch and design in epidemiology and public health -- Data analysis using R programming -- Graphics using R -- Probability and statistics in biostatistics -- Case-control studies and cohort studies in epidemiology -- Randomized trials, phase development, confounding in survival analysis, and logistic regressions.
520			_aThe book is systematically organized into seven chapters, each with a number of main sections covering the spectrum of applicable R codes for biostatistical applications in epidemiology and public health. Chapters 1 and 2 introduce interactional relationships among medicine, preventive medicine, public health, epidemiology, and biostatistics in general, as well as special concepts that have been (and are being) developed to address quantitative problems in epidemiology and public health in particular. A review of the basic elements in the theory of probability is presented to introduce or reinforce readers' ability to handle this important basic concept. Chapter 3 covers simple data handling using R programming, while Chapter 4 presents the graphics capabilities available in R. Following these initial forays into R, Chapter 5 gives an overview of the theory of probability and mathematical statistics, which is necessary because both of these areas have become integral parts of biostatistical applications in epidemiology. Chapter 6 shows how R may be effectively used to handle classical problems in case-control studies and cohort investigations in epidemiology. Similarly, survival analysis, the backbone of much epidemilogic research, finds excellent support in the R environment, as outlined in Chapter 7.
650	1	0	_aBiostatistics _xmethods. _919909
650	2	0	_aEpidemiology. _919910
650	2	0	_aProgramming Languages. _919911
650	2	0	_aPublic Health. _919912
942			_2lcc _cGS
984			_a064528 _blac