Statistical Standards And Studies

Healthcare decision makers in search of reliable information that compares health interventions increasingly turn to systematic reviews for the best summary of the evidence. Systematic reviews identify, select, assess, and synthesize the findings of similar but separate studies, and can help clarify what is known and not known about the potential benefits and harms of drugs, devices, and other healthcare services. Systematic reviews can be helpful for clinicians who want to integrate research findings into their daily practices, for patients to make well-informed choices about their own care, for professional medical societies and other organizations that develop clinical practice guidelines. Too often systematic reviews are of uncertain or poor quality. There are no universally accepted standards for developing systematic reviews leading to variability in how conflicts of interest and biases are handled, how evidence is appraised, and the overall scientific rigor of the process. In Finding What Works in Health Care the Institute of Medicine (IOM) recommends 21 standards for developing high-quality systematic reviews of comparative effectiveness research. The standards address the entire systematic review process from the initial steps of formulating the topic and building the review team to producing a detailed final report that synthesizes what the evidence shows and where knowledge gaps remain. Finding What Works in Health Care also proposes a framework for improving the quality of the science underpinning systematic reviews. This book will serve as a vital resource for both sponsors and producers of systematic reviews of comparative effectiveness research.

Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.

This publication shows readers how to design and conduct a census or sample survey. It explains basic survey concepts and provides information on how to create efficient and high quality surveys. It is aimed at those involved in planning, conducting or managing a survey and at students of survey design courses. This book contains the following information: formulating the survey objectives and design a questionnaire; things to consider when designing a survey (choosing between a sample or a census, defining the survey population, choosing which survey frame to use, possible sources of survey error); determining the sample size, allocate the sample across strata and select the sample; appropriate uses of survey data and methods of point and variance estimation in data analysis; data dissemination and disclosure control; using administrative data, particularly during the design and estimation phases; choosing a collection method (self-enumeration, personal interview or telephone interview, computer-assisted versus paper-based questionnaires); organizing and conducting data collection operations; processing data (all data handling activities between collection and estimation) and using quality control and quality assurance measures to minimize and control errors during various survey steps; and planning and managing a survey. This publication also includes a case study that illustrates the steps in developing a household survey, using the methods and principles presented in the book.