Statistical Analysis of Human Growth and Development is an accessible and practical guide to a wide range of basic and advanced statistical methods that are useful for studying human growth and development. Designed for nonstatisticians and statisticians new to the analysis of growth and development data, the book collects methods scattered throughout the literature and explains how to use them to solve common research problems. It also discusses how well a method addresses a specific scientific question and how to interpret and present the analytic results. Stata is used to implement the analyses, with Stata codes and macros for generating example data sets, a detrended Q-Q plot, and weighted maximum likelihood estimation of binary items available on the book’s CRC Press web page. After reviewing research designs and basic statistical tools, the author discusses the use of existing tools to transform raw data into analyzable variables and back-transform them to raw data. He covers regression analysis of quantitative, binary, and censored data as well as the analysis of repeated measurements and clustered data. He also describes the development of new growth references and developmental indices, the generation of key variables based on longitudinal data, and the processes to verify the validity and reliability of measurement tools. Looking at the larger picture of research practice, the book concludes with coverage of missing values, multiplicity problems, and multivariable regression. Along with two simulated data sets, numerous examples from real experimental and observational studies illustrate the concepts and methods. Although the book focuses on examples of anthropometric measurements and changes in cognitive, social-emotional, locomotor, and other abilities, the ideas are applicable to many other physical and psychosocial phenomena, such as lung function and depressive symptoms.

Drug development is the process of finding and producingtherapeutically useful pharmaceuticals, turning them into safe andeffective medicine, and producing reliable information regardingthe appropriate dosage and dosing intervals. With regulatoryauthorities demanding increasingly higher standards in suchdevelopments, statistics has become an intrinsic and criticalelement in the design and conduct of drug development programmes. Statistical Issues in Drug Development presents anessential and thought provoking guide to the statistical issues andcontroversies involved in drug development. This highly readable second edition has been updated toinclude: Comprehensive coverage of the design and interpretation ofclinical trials. Expanded sections on missing data, equivalence, meta-analysisand dose finding. An examination of both Bayesian and frequentist methods. A new chapter on pharmacogenomics and expanded coverage ofpharmaco-epidemiology and pharmaco-economics. Coverage of the ICH guidelines, in particular ICH E9,Statistical Principles for Clinical Trials. It is hoped that the book will stimulate dialogue betweenstatisticians and life scientists working within the pharmaceuticalindustry. The accessible and wide-ranging coverage make itessential reading for both statisticians and non-statisticiansworking in the pharmaceutical industry, regulatory bodies andmedical research institutes. There is also much to benefitundergraduate and postgraduate students whose courses include amedical statistics component.

One of the most urgent challenges in African economic development is to devise a strategy for improving statistical capacity. Reliable statistics, including estimates of economic growth rates and per-capita income, are basic to the operation of governments in developing countries and vital to nongovernmental organizations and other entities that provide financial aid to them. Rich countries and international financial institutions such as the World Bank allocate their development resources on the basis of such data. The paucity of accurate statistics is not merely a technical problem; it has a massive impact on the welfare of citizens in developing countries. Where do these statistics originate? How accurate are they? Poor Numbers is the first analysis of the production and use of African economic development statistics. Morten Jerven's research shows how the statistical capacities of sub-Saharan African economies have fallen into disarray. The numbers substantially misstate the actual state of affairs. As a result, scarce resources are misapplied. Development policy does not deliver the benefits expected. Policymakers' attempts to improve the lot of the citizenry are frustrated. Donors have no accurate sense of the impact of the aid they supply. Jerven's findings from sub-Saharan Africa have far-reaching implications for aid and development policy. As Jerven notes, the current catchphrase in the development community is "evidence-based policy," and scholars are applying increasingly sophisticated econometric methods-but no statistical techniques can substitute for partial and unreliable data.

The internationally recognised methodology for collecting and using R&D statistics, the OECD's Frascati Manual is an essential tool for statisticians and science and innovation policy makers worldwide. It includes definitions of basic concepts, data collection guidelines, and classifications ...