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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.
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.
Routine applications of advanced statistical methods on real data
have become possible in the last ten years because desktop
computers have become much more powerful and cheaper. However,
proper understanding of the challenging statistical theory behind
those methods remains essential for correct application and
interpretation, and rarely seen in the medical literature. Modern
Methods for Epidemiology provides a concise introduction to recent
development in statistical methodologies for epidemiological and
biomedical researchers. Many of these methods have become
indispensible tools for researchers working in epidemiology and
medicine but are rarely discussed in details by standard textbooks
of biostatistics or epidemiology. Contributors of this book are
experienced researchers and experts in their respective fields.
This textbook provides a solid starting point for those who are new
to epidemiology, and for those looking for guidance in more modern
statistical approaches to observational epidemiology.
Epidemiological and biomedical researchers who wish to overcome the
mathematical barrier of applying those methods to their research
will find this book an accessible and helpful reference for
self-learning and research. This book is also a good source for
teaching postgraduate students in medical statistics or
epidemiology.
Routine applications of advanced statistical methods on real data
have become possible in the last ten years because desktop
computers have become much more powerful and cheaper. However,
proper understanding of the challenging statistical theory behind
those methods remains essential for correct application and
interpretation, and rarely seen in the medical literature. Modern
Methods for Epidemiology provides a concise introduction to recent
development in statistical methodologies for epidemiological and
biomedical researchers. Many of these methods have become
indispensible tools for researchers working in epidemiology and
medicine but are rarely discussed in details by standard textbooks
of biostatistics or epidemiology. Contributors of this book are
experienced researchers and experts in their respective fields.
This textbook provides a solid starting point for those who are new
to epidemiology, and for those looking for guidance in more modern
statistical approaches to observational epidemiology.
Epidemiological and biomedical researchers who wish to overcome the
mathematical barrier of applying those methods to their research
will find this book an accessible and helpful reference for
self-learning and research. This book is also a good source for
teaching postgraduate students in medical statistics or
epidemiology.
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