Covers the latest research on a sensitive and controversial topic in a professional and well researched manner. Provides practical outlook as well as model guidelines and software tools that should be of interest to people who use the software tools described and those who do not. Related title by Co-author Geert Molenbergh has sold more than 3500 copies world wide. Provides dual viewpoints: from scientists in the industry as well as regulatory authorities.

Valid surrogate endpoints can make clinical trials more efficient, allowing for more trials to be conducted and more rapid development of effective treatments. Identifying useful surrogates is a statistically challenging but extremely valuable endeavor. This work develops statistical methods for the evaluation and comparison of biomarkers as correlates of protection (CoP). Methods herein were developed with a focus on a time-to-event clinical endpoint and possible time-varying effects of treatment, an important and thus far neglected topic is CoP evaluation. We propose a novel Weibull model and three methods of estimation for use in CoP evaluation; simulations and real data examples demonstrate the characteristics of these methods.

Many people naturally assume that the claims made for foods and nutritional supplements have the same degree of scientific grounding as those for medication, but that is not always the case. The IOM recommends that the FDA adopt a consistent scientific framework for biomarker evaluation in order to achieve a rigorous and transparent process.

Recognizing the potential design complexities and ethical issues associated with clinical trials for gene therapies, the Forum on Regenerative Medicine of the National Academies of Sciences, Engineering, and Medicine held a 1-day workshop in Washington, DC, on November 13, 2019. Speakers at the workshop discussed patient recruitment and selection for gene-based clinical trials, explored how the safety of new therapies is assessed, reviewed the challenges involving dose escalation, and spoke about ethical issues such as informed consent and the role of clinicians in recommending trials as options to their patients. The workshop also included discussions of topics related to gene therapies in the context of other available and potentially curative treatments, such as bone marrow transplantation for hemoglobinopathies. This publication summarizes the presentation and discussion of the workshop.

An important factor that affects the duration, complexity and cost of a clinical trial is the endpoint used to study the treatment’s efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost, it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice, using SAS and R.

In randomized clinical trials, the evaluation of potential surrogate endpoints is very important, since a successful surrogate endpoint will reduce follow-up trial time and/or will reduce the number of patients needed to establish a certain treatment effect. We investigate the statistical validation of different types of surrogate endpoints: continuous and binary.

The very rapid pace of advances in biomedical research promises us a wide range of new drugs, medical devices, and clinical procedures. The extent to which these discoveries will benefit the public, however, depends in large part on the methods we choose for developing and testing them. Modern Methods of Clinical Investigation focuses on strategies for clinical evaluation and their role in uncovering the actual benefits and risks of medical innovation. Essays explore differences in our current systems for evaluating drugs, medical devices, and clinical procedures; health insurance databases as a tool for assessing treatment outcomes; the role of the medical profession, the Food and Drug Administration, and industry in stimulating the use of evaluative methods; and more. This book will be of special interest to policymakers, regulators, executives in the medical industry, clinical researchers, and physicians.

Due to injuries sustained in sports and in combat, interest in traumatic brain injury (TBI) has never been greater. This book will fulfill a gap in understanding of what is occurring in the brain following injury that can subsequently be detected in biological fluids and imaging.

The third edition of the bestselling Clinical Trials in Oncology provides a concise, nontechnical, and thoroughly up-to-date review of methods and issues related to cancer clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. In addition, the book has been restructured to have separate chapters and expanded discussions on general clinical trials issues, and issues specific to Phases I, II, and III. New sections cover innovations in Phase I designs, randomized Phase II designs, and overcoming the challenges of array data. Although this book focuses on cancer trials, the same issues and concepts are important in any clinical setting. As always, the authors use clear, lucid prose and a multitude of real-world examples to convey the principles of successful trials without the need for a strong statistics or mathematics background. Armed with Clinical Trials in Oncology, Third Edition, clinicians and statisticians can avoid the many hazards that can jeopardize the success of a trial.

Surrogate endpoints are desirable in clinical trials when primary endpoints are costly to obtain, difficult to measure, or require lengthy follow-up to observe. Despite legitimate concerns, evaluation of potentially beneficial treatments in some settings remains impossible or implausible without the use of surrogates. Furthermore, strong evidence based on a collection of trials, rather than a relationship observed within a single trial, is required to validate a surrogate endpoint for future primary use. We present a Bayesian approach to evaluating surrogacy using patient data from multiple trials with time-to-event endpoints that accounts for estimation error of treatment effects and offers greater computational stability than existing methods. Once a surrogate endpoint has been deemed valid for use in a future trial, a healthy skepticism should remain regarding its ability to reflect the true treatment effect that would have been observed on the primary endpoint. Despite the surrogate's intended role, few (if any) efforts have been made to formalize existing knowledge and uncertainty in the design of such a trial. We propose a Bayesian adaptive design that uses the validated surrogate as the primary endpoint, while acknowledging that this endpoint is really a surrogate, and perhaps only a recently- validated one. At prospectively-defined checkpoints, we assess the performance of the surrogate and decide whether to continue its use or switch consideration to the primary endpoint. Furthermore, our design incorporates other favorable aspects of Bayesian adaptive trials, including the ability to stop a trial early for treatment efficacy, inferiority, or trial futility. Flowgraphs are useful for modeling diseases that are well-described by multi- state models, but for which Markov assumptions are inadequate and returns to previous states are possible. Furthermore, censoring and covariates may influence the distribution of waiting times between any two states, and to a differing degree for separate transitions within the same system. We discuss the construction and advantages of flowgraph models when used to describe cancer progression within two clinical trials, where our goal is improved modeling of treatment effects and prediction of patient outcomes for the purpose of more realistic surrogacy evaluation.