This book presents basic tools from probability theory used in algorithmic applications, with concrete examples.

Is adaptive randomization always better than traditional fixed-schedule randomization? Which procedures should be used and under which circumstances? What special considerations are required for adaptive randomized trials? What kind of statistical inference should be used to achieve valid and unbiased treatment comparisons following adaptive random

The central idea of evidence-based education-that education policy and practice ought to be fashioned based on what is known from rigorous research-offers a compelling way to approach reform efforts. Recent federal trends reflect a growing enthusiasm for such change. Most visibly, the 2002 No Child Left Behind Act requires that "scientifically based [education] research" drive the use of federal education funds at the state and local levels. This emphasis is also reflected in a number of government and nongovernment initiatives across the country. As consensus builds around the goals of evidence-based education, consideration of what it will take to make it a reality becomes the crucial next step. In this context, the Center for Education of the National Research Council (NRC) has undertaken a series of activities to address issues related to the quality of scientific education research. In 2002, the NRC released Scientific Research in Education (National Research Council, 2002), a report designed to articulate the nature of scientific education research and to guide efforts aimed at improving its quality. Building on this work, the Committee on Research in Education was convened to advance an improved understanding of a scientific approach to addressing education problems; to engage the field of education research in action-oriented dialogue about how to further the accumulation of scientific knowledge; and to coordinate, support, and promote cross-fertilization among NRC efforts in education research. The main locus of activity undertaken to meet these objectives was a year-long series of workshops. This report is a summary of the third workshop in the series, on the implementation and implications of randomized field trials in education.

Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials. In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials. Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning, monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing, subgroup analysis, competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials, analysis of safety outcomes, non-inferiority trials, incorporating historical data, and validation of surrogate outcomes.

Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits tackles how to gather and analyze data relating to stigmatizing human traits. S.L. Warner invented RRT and published it in JASA, 1965. In the 50 years since, the subject has grown tremendously, with continued growth. This book comprehensively consolidates the literature to commemorate the inception of RR. - Brings together all relevant aspects of randomized response and indirect questioning - Tackles how to gather and analyze data relating to stigmatizing human traits - Gives an encyclopedic coverage of the topic - Covers recent developments and extrapolates to future trends

Pragmatic Randomized Clinical Trials Using Primary Data Collection and Electronic Health Records addresses the practical aspects and challenges of the design, implementation, and dissemination of pragmatic randomized trials, also sometimes referred to as practical or hybrid randomized trials. While less restrictive and more generalizable than traditional randomized controlled trials, such trials have specific challenges which are addressed in this book. The book contains chapters encompassing common designs along with advantages and limitations of such designs, analytic aspects in planning trials and estimating sample size, and how to use patient partners to help design and operationalize pragmatic randomized trials. Pragmatic trials conducted using primary data collection and trials embedded in electronic health records - including electronic medical records and administrative insurance claims - are addressed. This comprehensive resource is valuable not only for pharmacoepidemiologists, biostatisticians and clinical researchers, but also across the biomedical field for those who are interested in applying pragmatic randomized clinical trials in their research. Addresses typical designs and challenges of pragmatic randomized clinical trials (pRCTs) Encompasses analytic aspects of such trials Discusses real cases on operational challenges in launching and conducting pRCTs in electronic health records

The technique of randomization has been employed to solve numerous prob lems of computing both sequentially and in parallel. Examples of randomized algorithms that are asymptotically better than their deterministic counterparts in solving various fundamental problems abound. Randomized algorithms have the advantages of simplicity and better performance both in theory and often in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis of algorithms, at least three different measures of performance can be used: the best case, the worst case, and the average case. Often, the average case run time of an algorithm is much smaller than the worst case. 2 For instance, the worst case run time of Hoare's quicksort is O(n ), whereas its average case run time is only O( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) average run time for quicksort is that each input permutation is equally likely. Clearly, any average case analysis is only as good as how valid the assumption made on the input space is. Randomized algorithms achieve superior performances without making any assumptions on the inputs by making coin flips within the algorithm. Any analysis done of randomized algorithms will be valid for all p0:.sible inputs.

This issue of the Surgical Oncology Clinics will review the important clinical trials from the past eight years in following topics: breast cancer, soft tissue sarcoma, rectal and anal carcinoma, pancreatic adenocarcinoma, hepatocellular carcinoma, gastric cancer, esophageal cancer, colon cancer, advanced and metastatic colorectal carcinoma, gastrointestinal stromal tumors, and melanoma.

Community or group-randomized trials, which are usually done to evaluate the effect of health promotion effors. It reviews the underlying issues, describes the most widely used research design, and presents the many approaches to analysis that are now available.

It is quite common in a randomized clinical trial (RCT) to encounter patients who do not comply with their assigned treatment. Since noncompliance often occurs non-randomly, the commonly-used approaches, including both the as-treated (AT) and as-protocol (AP) analysis, and the intent-to-treat (ITT) (or as-randomized) analysis, are all well known to possibly produce a biased inference of the treatment efficacy. This book provides a systematic and organized approach to analyzing data for RCTs with noncompliance under the most frequently-encountered situations. These include parallel sampling, stratified sampling, cluster sampling, parallel sampling with subsequent missing outcomes, and a series of dependent Bernoulli sampling for repeated measurements. The author provides a comprehensive approach by using contingency tables to illustrate the latent probability structure of observed data. Using real-life examples, computer-simulated data and exercises in each chapter, the book illustrates the underlying theory in an accessible, and easy to understand way. Key features: Consort-flow diagrams and numerical examples are used to illustrate the bias of commonly used approaches, such as, AT analysis, AP analysis and ITT analysis for a RCT with noncompliance. Real-life examples are used throughout the book to explain the practical usefulness of test procedures and estimators. Each chapter is self-contained, allowing the book to be used as a reference source. Includes SAS programs which can be easily modified in calculating the required sample size. Biostatisticians, clinicians, researchers and data analysts working in pharmaceutical industries will benefit from this book. This text can also be used as supplemental material for a course focusing on clinical statistics or experimental trials in epidemiology, psychology and sociology.