Principles of Experimental Design for the Life Sciences
Let this down-to-earth book be your guide to the statistical integrity of your work. Without relying on the detailed and complex mathematical explanations found in many other statistical texts, Principles of Experimental Design for the Life Sciences teaches how to design, conduct, and interpret top-notch life science studies. Learn about the planning of biomedical studies, the principles of statistical design, sample size estimation, common designs in biological experiments, sequential clinical trials, high dimensional designs and process optimization, and the correspondence between objectives, design, and analysis. Each of these important topics is presented in an understandable and non-technical manner, free of statistical jargon and formulas.
Written by a biostatistical consultant with 25 years of experience, Principles of Experimental Design for the Life Sciences is filled with real-life examples from the author's work that you can quickly and easily apply to your own. These examples illustrate the main concepts of experimental design and cover a broad range of application areas in both clinical and nonclinical research. With this one innovative, helpful book you can improve your understanding of statistics, enhance your confidence in your results, and, at long last, shake off those statistical shackles!
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Introduction and Overview
Planning Biomedical Studies
Principles of Statistical Design
Sample Size Estimation
Common Designs in Biological Experimentation
Sequential Clinical Trials
High Dimensional Designs
administered alternative hypothesis animals applied assay assess bias and variability biomedical studies cells Chapter clinical studies designed clinical study clinical trials common comparisons completely randomized design conducted confidence interval confounded consider context contingency table crossover design data analysis data monitoring boards defining contrast design points discussed distribution dose levels drug efficacy example experiment experimental design experimental units f-test factors false positive rate fractional factorial design group sequential designs hypothesis testing identify interim analyses interval estimation investigators levels of sampling main effects multicenter multiple negative control nonparametric normal normally distributed null hypothesis number of treatment parameter particular patients performed pilot studies placebo population possible potential protocol randomized block design response surface methodology sample size estimation Section sequential test specific statistician study design study objectives subjects success rates Suppose Table test statistic total number Treatment A Treatment treatment combinations treatment conditions treatment group utilized values variance