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In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically. The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective. Statistical Methods in Healthcarefocuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success. Statistical methods used in planning, delivering and monitoring health care, as well as selected statistical aspects of the development and/or production of pharmaceuticals and medical devices are also addressed.With a focus on finding solutions to these challenges, this book:Provides a comprehensive, in-depth treatment of statistical methods in healthcare, along with a reference source for practitioners and specialists in health care and drug development.Offers a broad coverage of standards and established methods through leading edge techniques.Uses an integrated, case-study based approach, with focus on applications.Looks at the use of analytical and monitoring schemes to evaluate therapeutic performance.Features the application of modern quality management systems to clinical practice, and to pharmaceutical development and production processes.Addresses the use of modern Statistical methods such as Adaptive Design, Seamless Design, Data Mining, Bayesian networks and Bootstrapping that can be applied to support the challenging new vision.Practitioners in healthcare-related professions, ranging from clinical trials to care delivery to medical device design, as well as statistical researchers in the field, will benefit from this book.

1 Introduction -- 1.1 Examples of random variation -- 1.2 One-dimensional frequency distributions -- 1.3 Summarizing quantities -- 1.4 Frequency distributions in two or more dimensions -- 1.5 Some illustrative examples -- 1.6 Populations, samples and probability -- 2 Probability and Probability Distributions -- 2.1 Probability -- 2.2 Addition law of probability -- 2.3 Conditional probability and statistical independence -- 2.4 Examples -- 2.5 Discrete random variables -- 2.6 Continuous random variables -- 2.7 Several random variables -- 3 Expectation and its Applications -- 3.1 Expectation -- 3.2 Variance -- 3.3 Higher moments -- 3.4 Dependence and covariance -- 3.5 Normal models -- 4 Sampling Distributions and Statistical Inference -- 4.1 Statistical inference -- 4.2 Pseudo random deviates -- 4.3 A sampling experiment -- 4.4 Estimation -- 4.5 Significance tests -- 5 Single Sample Problems -- 5.1 Introduction -- 5.2 Point estimates of µ and ?2 -- 5.3 Interval estimates for µ (?2 unknown) -- 5.4 Interval estimates for ?2 -- 5.5 Significance test for a mean -- 5.6 Significance test for a variance -- 5.7 Departures from assumptions -- 6 Two Sample Problems -- 6.1 Introduction -- 6.2 The comparison of two independent sample means -- 6.3 The comparison of two independent sample variances -- 6.4 Analysis of paired samples -- 6.5 An example -- 6.6 Departures from assumptions -- 7 Non-parametric Tests -- 7.1 Introduction -- 7.2 Normal approximation to the binomial distribution -- 7.3 The sign test -- 7.4 The signed rank (Wilcoxon one sample) test -- 7.5 Two sample rank (Wilcoxon) test -- 7.6 Discussion -- 8 The Analysis of Discrete Data -- 8.1 Introduction -- 8.2 Distributions and approximations -- 8.3 Inference about a single Poisson mean -- 8.4 Inference about a single binomial probability -- 8.5 The comparison of two Poisson variates -- 8.6 The comparison of two binomial variates -- 8.7 Comparison of proportions in matched pairs -- 8.8 Examination of Poisson frequency table -- 8.9 Examination of binomial frequency tables -- 8.10 Comparison of observed and expected frequencies -- 8.11 Contingency tables -- 8.12 A tasting experiment -- 9 Statistical Models and Least Squares -- 9.1 General points -- 9.2 An example -- 9.3 Least squares -- 10 Linear Regression -- 10.1 Introduction -- 10.2 Least squares estimates -- 10.3 Properties of ? and ? -- 10.4 Predictions from regressions -- 10.5 Comparison of two regression lines -- 10.6 Equally spaced x-values -- 10.7 Use of residuals -- 10.8 Discussion of models -- 11 Multiple Regression -- 11.1 Introduction -- 11.2 Theory for two explanatory variables only -- 11.3 Analysis of Example 11.2 -- 11.4 Discussion -- 12 Analysis of Variance -- 12.1 The problem -- 12.2 Theory of one-way analysis of variance -- 12.3 Procedure for analysis -- 12.4 Two-way analysis of variance -- 12.5 Linear contrasts -- 12.6 Randomized blocks -- 12.7 Components of variance -- 12.8 Departures from assumptions -- Miscellaneous Exercises -- Appendix One Notes on calculation and computing 307 -- Appendix Two Statistical tables -- Appendix Three Hints to the solution of selected exercises -- References -- Author Index.