Repeated Measurements
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An introduction to state-of-the-art experimental design approaches to better understand and interpret repeated measurement data in cross-over designs.Repeated Measurements and Cross-Over Designs:Features the close tie between the design, analysis, and presentation of resultsPresents principles and rules that apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studiesIncludes many practical examples, such as PK/PD studies in the pharmaceutical industry, k-sample and one sampl...
An introduction to state-of-the-art experimental design approaches to better understand and interpret repeated measurement data in cross-over designs.
Repeated Measurements and Cross-Over Designs:
Features the close tie between the design, analysis, and presentation of results
Presents principles and rules that apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studies
Includes many practical examples, such as PK/PD studies in the pharmaceutical industry, k-sample and one sample repeated measurement designs for psychological studies, and residual effects of different treatments in controlling conditions such as asthma, blood pressure, and diabetes.
Utilizes SAS(R) software to draw necessary inferences. All SAS output and data sets are available via the book's related website.
This book is ideal for a broad audience including statisticians in pre-clinical research, researchers in psychology, sociology, politics, marketing, and engineering.
Repeated Measurements and Cross-Over Designs:
Features the close tie between the design, analysis, and presentation of results
Presents principles and rules that apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studies
Includes many practical examples, such as PK/PD studies in the pharmaceutical industry, k-sample and one sample repeated measurement designs for psychological studies, and residual effects of different treatments in controlling conditions such as asthma, blood pressure, and diabetes.
Utilizes SAS(R) software to draw necessary inferences. All SAS output and data sets are available via the book's related website.
This book is ideal for a broad audience including statisticians in pre-clinical research, researchers in psychology, sociology, politics, marketing, and engineering.
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