Thomas L. Burr

The impact of response measurement error on the analysis of designed experiments

This study considers the analysis of designed experiments when there is measurement error in the true response or so-called response measurement error. We consider both additive and multiplicative response measurement errors. Through a simulation study, we investigate the impact of ignoring the response measurement error in the analysis, that is, by using a standard analysis based on t-tests. In addition, we examine the role of repeat measurements in improving the quality of estimation and prediction in the presence of response measurement error. We also study a Bayesian approach that accounts for the response measurement error directly through the specification of the model, and allows including additional information about variability in the analysis. We consider the impact on power, prediction, and optimization. Copyright

Multidimensional Nonlinear Descriptive Analysis (MUNDA)

bridge (BB) processes. The first chapter introduces the rationale, the history, and the notions behind the statistical monitoring of clinical trials. In Chapters 2 and 3, the author overviews some probability and statistics concepts that are needed for the rest of the book. In Chapters 4 and 5, the Brownian motion process and fundamentals for monitoring its path via the alpha-spending function approach of Lan and DeMets (1983) are discussed and the connection with boundaries for monitoring clinical trials is made. The important concept of conditional power approach for monitoring a trial is discussed in Chapter 6 by means of the BB process. Chapter 7 explains the concept of monitoring safety endpoints and stopping for futility (i.e., terminating the trial when clinically significant results are unlikely to be observed at the end of the trial). Both concepts are exposed through the conditional power approach. Finally, the author concludes with Chapter 8 concerning the Bayesian group sequential techniques for monitoring clinical trials. The author pushes to the Appendices several unavoidable mathematical derivations concerning the computation of monitoring boundaries for BM and BB processes. As is the case with almost any first edition, this book has its share of minor typos, specially in the bibliographies. Apart from this, there are several important subjects not covered in the book such as sample size calculations and adaptive group sequential monitoring techniques, which recently gained momentum among clinical trial investigators. Also, the book lacks real examples using real-life data sets. These points will, hopefully, be rectified in future editions.

Modeling Financial Time Series With S—Plus

asymmetric queues, approximation algorithms for networks, an introduction to performance analysis software PEPSY (Performance Evaluation Prediction SYstem), SPNP (Stochastic Petri Net Package), MOSEL-2 (MOdeling, Specification, and Evaluation Language), and SHARPE (Symbolic Hierarchical Automated Reliability Performance Evaluator). No queueing book can cover everything, but this book covers most of the important topics within queueing theory. As always, if one is planning on learning or teaching a specific topic, it would be wise to check the Table of