This text is designed for laboratory workers and clinicians but should also be useful for other healthcare workers. The authors assume that the reader has access to a computer with statistical software for inspection and analysis of data. The book has four main parts. Chapter 1 describes the different kinds of data and statistical analyses that can be applied to them. Topics include how to assess the shape of distributions, how to transform data, when to use parametric and non-parametric tests, and how to check data input and deal with outliers. Chapter 2 deals with the inaccuracy, imprecision, detection limits, analytical goals and other aspects of analytical methods used in laboratory medicine, and explains how to compare both quantitative and qualitative analytical methods.

This text provides a comprehensive and practical review of the main statistical methods in pathology and laboratory medicine. It introduces statistical concepts used in pathology and laboratory medicine. The information provided is relevant to pathologists both for their day to day clinical practice as well as in their research and scholarly activities. The text will begins by explaining the fundamentals concepts in statistics. In the later sections, these fundamental concepts are expanded and unique applications of statistical methods in pathology and laboratory medicine practice are introduced. Other sections of the text explain research methodology in pathology covering a broad range of topics from study design to analysis of data. Finally, data-heavy novel concepts that are emerging in pathology and pathology research are presented such as molecular pathology and pathology informatics. Introduction to Statistical Methods in Pathology will be of great value for pathologists, pathology residents, basic and translational researchers, laboratory managers and medical students.

Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.

This handbook provides an authoritative guide to investigation and diagnosis. It describes key symptoms and signs, alongside appropriate tests, and highlights pitfalls in interpreting results. It also describes a clear, rational method of investigation in order to aid quick and efficient diagnosis, and prevent over-investigation of patients.

Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.

Focusing on practical, patient related issues, this volume provides the basic concepts of Evidence Based Medicine (EBM) as they relate to Pathology and Laboratory Medicine and presents various practical applications. It includes EBM concepts for use in the identification of cost-effective panels of immunostains and other laboratory tests and for improvement of diagnostic accuracy based on the identification of selected diagnostic features for particular differential diagnosis. EBM concepts are also put forth for use in Meta-analysis to integrate the results of conflicting literature reports and use of novel analytical tools such as Bayesian belief networks, neural networks, multivariate statistics and decision tree analysis for the development of new diagnostic and prognostic models for the evaluation of patients. This volume will be of great value to pathologists who will benefit from the concepts being promoted by EBM, such as levels of evidence, use of Bayesian statistics to develop diagnostic and other rules and stronger reliance on "hard data" to support therapeutic and diagnostic modalities.