02324nam a2200277 a 4500 vtls000057158 KUKTEM 20251117150320.0 111212t2012 flua f 001 0 eng d 9781420099911 1420099914 201204171045 asmadi 201203051041 asmadi 201112121541 shah UMP RA652.2.M3 T89 2012 Tu, Yu-Kang Statistical thinking in epidemiology / Yu-Kang Tu, Mark S. Gilthorpe Boca Raton, FL : CRC Press, 2012 xii, 219 p. : ill. ; 25 cm. Includes bibliographical references (p. 189-202) and index "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 Epidemiology Statistical methods Epidemiology Mathematics Gilthorpe, Mark S. 0 0 0 0 10000 10000 2019-09-04 277.66 1 RA652.2.M3 T89 2012 0000062865 2021-03-26 00:00:00 2020-03-12 1 277.66 2019-09-04 OPEN VIRTUA40 55867 55873 VTLSSORT0080*0200*0201*0400*0900*1000*2450*2600*3000*5040*5200*6500*6501*7000*9991 1