000 02324nam a2200277 a 4500
001 vtls000057158
003 KUKTEM
005 20251117150320.0
008 111212t2012 flua f 001 0 eng d
020 _a9781420099911
020 _a1420099914
039 9 _a201204171045
_basmadi
_c201203051041
_dasmadi
_y201112121541
_zshah
040 _aUMP
090 _aRA652.2.M3 T89 2012
100 1 _aTu, Yu-Kang
245 1 0 _aStatistical thinking in epidemiology /
_cYu-Kang Tu, Mark S. Gilthorpe
260 _aBoca Raton, FL :
_bCRC Press,
_c2012
300 _axii, 219 p. :
_bill. ;
_c25 cm.
504 _aIncludes bibliographical references (p. 189-202) and index
520 _a"While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research : mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction. Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking. Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling"--Provided by publisher
650 0 _aEpidemiology
_xStatistical methods
650 0 _aEpidemiology
_xMathematics
700 1 _aGilthorpe, Mark S.
999 _aVIRTUA40
_c55867
_d55873
999 _aVTLSSORT0080*0200*0201*0400*0900*1000*2450*2600*3000*5040*5200*6500*6501*7000*9991
942 0 0 _01