02053nam a2200217 a 4500001001400000003000700014005001700021008004100038020001800079020001500097040000800112100001600120245007400136260003900210300003400249504006300283520139800346650003801744650003001782700002301812vtls000057158KUKTEM20251117150320.0111212t2012    flua   f      001 0 eng d  a9781420099911  a1420099914  aUMP1 aTu, Yu-Kang10aStatistical thinking in epidemiology /cYu-Kang Tu, Mark S. Gilthorpe  aBoca Raton, FL :bCRC Press,c2012  axii, 219 p. :bill. ;c25 cm.  aIncludes bibliographical references (p. 189-202) and index  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 0aEpidemiologyxStatistical methods 0aEpidemiologyxMathematics1 aGilthorpe, Mark S.