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A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis—the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods. A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include: * Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods * Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application * Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice * Integration of practical data analysis exercises to develop skills and confidence * Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference. 

Biofoundries provide an integrated infrastructure to enable the rapid design, construction, and testing of genetically reprogrammed organisms for biotechnology applications and research. Many biofoundries are being built and a Global Biofoundry Alliance has recently been established to coordinate activities worldwide.

Background Randomised controlled trials (RCTs) are the gold standard assessment for health technologies. A key aspect of the design of any clinical trial is the target sample size. However, many publicly-funded trials fail to reach their target sample size. This study seeks to assess the current state of recruitment success and grant extensions in trials funded by the Health Technology Assessment (HTA) program and the UK Medical Research Council (MRC). Methods Data were gathered from two sources: the National Institute for Health Research (NIHR) HTA Journal Archive and the MRC subset of the International Standard Randomised Controlled Trial Number (ISRCTN) register. A total of 440 trials recruiting between 2002 and 2008 were assessed for eligibility, of which 73 met the inclusion criteria. Where data were unavailable from the reports, members of the trial team were contacted to ensure completeness. Results Over half (55%) of trials recruited their originally specified target sample size, with over three-quarters (78%) recruiting 80% of their target. There was no evidence of this improving over the time of the assessment. Nearly half (45%) of trials received an extension of some kind. Those that did were no more likely to successfully recruit. Trials with 80% power were less likely to successfully recruit compared to studies with 90% power. Conclusions While recruitment appears to have improved since 1994 to 2002, publicly-funded trials in the UK still struggle to recruit to their target sample size, and both time and financial extensions are often requested. Strategies to cope with such problems should be more widely applied. It is recommended that where possible studies are planned with 90% power.

Economic evaluations of health interventions pose a particular challenge for reporting. There is also a need to consolidate and update existing guidelines and promote their use in a user friendly manner. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement is an attempt to consolidate and update previous health economic evaluation guidelines efforts into one current, useful reporting guidance. The primary audiences for the CHEERS statement are researchers reporting economic evaluations and the editors and peer reviewers assessing them for publication. The need for new reporting guidance was identified by a survey of medical editors. A list of possible items based on a systematic review was created. A two round, modified Delphi panel consisting of representatives from academia, clinical practice, industry, government, and the editorial community was conducted. Out of 44 candidate items, 24 items and accompanying recommendations were developed. The recommendations are contained in a user friendly, 24 item checklist. A copy of the statement, accompanying checklist, and this report can be found on the ISPOR Health Economic Evaluations Publication Guidelines Task Force website (www.ispor.org/TaskForces/EconomicPubGuidelines.asp). We hope CHEERS will lead to better reporting, and ultimately, better health decisions. To facilitate dissemination and uptake, the CHEERS statement is being co-published across 10 health economics and medical journals. We encourage other journals and groups, to endorse CHEERS. The author team plans to review the checklist for an update in five years.

A practical guide to analysing partially observed data. Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete  data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods. This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures. Multiple Imputation and its Application: * Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest. * Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials. * Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics. * Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation. Multiple Imputation and its Application is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application.

Recent advances in multimodality imaging in cancer have involved the integration of multiple quantitative, functional measurements that provide a more-comprehensive characterization of tumours. In this Review, Yankeelov and colleagues discuss how, although some of these approaches still need some adjusting, they can already be applied informatively in clinical trials of cancer therapeutics using existing tools. Advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET–CT and single-photon emission CT (SPECT–CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET–MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools. These concepts are discussed herein.

Two views exist of medical science, says the author, one emphasizing discovery and explanation, the other emphasizing evaluation of interventions.

Background Integrating patient and community input is essential to the relevance and impact of patient-focused research. However, specific techniques for generating patient and community-informed research decisions remain limited. This manuscript describes a novel CASCADE method (Community-Engaged Approach for Scientific Collaborations and Decisions) that was developed and implemented to make actionable, patient-centered research decisions during a federally funded clinical trial. Methods The CASCADE method was developed to facilitate decision-making, combining techniques from a variety of past methodologies with new approaches that aligned with project constraints and goals. The final result was a series of procedures that spanned seven thematic pillars (1) identifying a shared, specific, and actionable goal; (2) centering community input; (3) integrating both pre-registered statistical analyses and exploratory “quests”; (4) fixed-pace scheduling, supported by technology; (5) minimizing opportunities for cognitive biases typical to group decision making; (6) centering diversity experiences and perspectives, including those of individual patients; (7) making decisions that are community-relevant, rigorous, and feasible. The final approach was piloted within an active clinical trial, with the primary goal of describing feasibility (participation, discussion topics, timing, quantity of outputs). Results The inaugural CASCADE panel aimed to identify ways to improve an algorithm for matching patients to specific types of telehealth programs within an active, federally funded clinical trial. The panel was attended by 27 participants, including 5 community interest-holders. Data reviewed to generate hypotheses and make decisions included (1) pre-registered statistical analyses, (2) results of 12 “quests” that were launched during the panel to answer specific panelist questions via exploratory analyses or literature review, (3) qualitative and quantitative patient input, and (4) team member input, including by staff who represented the focal patient population for the clinical trial. CASCADE pillars were successfully integrated to generate 18 initial and 6 final hypotheses, which were translated to 19 decisional changes. Conclusions The CASCADE approach was an effective tool for rapidly, efficiently making patient-centered decisions during an ongoing, federally funded clinical trial. Opportunities for further development will include exploring best-practice structural procedures, enhancing greater opportunities for pre-panel input by community interest-holders, and determining how to best standardize CASCADE outputs. Trial registration The CASCADE procedure was developed in the context of NCT05999448.

In this review article, we provide a comprehensive overview of current practices and challenges associated with research synthesis in preclinical biomedical research. We identify critical barriers and roadblocks that impede effective identification, utilisation, and integration of research findings to inform decision making in research translation. We examine practices at each stage of the research lifecycle, including study design, conduct, and publishing, that can be optimised to facilitate the conduct of timely, accurate, and comprehensive evidence synthesis. These practices are anchored in open science and engaging with the broader research community to ensure evidence is accessible and useful to all stakeholders. We underscore the need for collective action from researchers, synthesis specialists, institutions, publishers and journals, funders, infrastructure providers, and policymakers, who all play a key role in fostering an open, robust and synthesis-ready research environment, for an accelerated trajectory towards integrated biomedical research and translation.