In this introductory chapter, we provide a brief overview of some of the main topics related to dementia communication research that are addressed by the different chapters in this edited volume: Dementia and Diagnostics, Dementia and Conversational Strategies, Dementia and Epistemics, and Communicative Challenges in Everyday Social Life. One of the central aims of this volume is to shed more light on how persons with dementia accomplish relevant goals in interaction and also how changes in an individual’s discursive abilities may impact how conversationalists negotiate a world in common and continue to build their social relationships. All contributions for this edited volume draw on the methods of Conversation Analysis (CA), an approach to social interaction that provides a detailed view of the moment-by-moment accomplishment of social life. By exploring interactional practices through the lens of CA, this volume seeks to explore interactions involving people with dementia in a variety of contexts (everyday and institutional), pointing to both the interactional difficulties that often arise, but also the creativity and collaboration within these interactional encounters. A summary of each of the volume’s chapters is also provided.

Multiple linear regression generalizes straight line regression to allow multiple explanatory (or predictor) variables, in this chapter under the normal errors assumption. The focus may be on accurate prediction. Or it may, alternatively or additionally, be on the regression coefficients themselves. Simplistic interpretations of coefficients can be grossly misleading. Later chapters elaborate on the ideas and methods developed in this chapter, applying them in new contexts. The attaching of causal interpretations to model coefficients must be justified both by reference to subject area knowledge and by careful checks to ensure that they are not artefacts of the correlation structure. There is attention to regression diagnostics, to assessment, and comparison of models. Variable selection strategies can readily over-fit. Hence the importance of training/test approaches and cross-validation. The potential is demonstrated for errors in x to seriously bias regression coefficients. Strong multicollinearity leads to large variance inflation factors.

The COVID-19 pandemic caused unprecedented loss as it swept through humanity wiping out 6.69 million human lives. It is now estimated that 660 million of the world’s population was infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel coronavirus that emerged in Wuhan, China, and then rapidly spread across continents. This is our story of how we came together during an incredibly challenging time to fight a common enemy. Times of crisis can often serve as strong drivers of creativity and innovation. It was during the crisis of the pandemic that the National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADx®) initiative to accelerate the translation of innovation into high-performing diagnostic tests. This book chronicles the power of innovating during a crisis, the compression of product launch timelines from over five years to under a year, the birth of creative financing ventures, the commitment of entrepreneurs, and the agility and resourcefulness of government institutions as they adapted to meet the imminent needs of the pandemic and serve the community. It is our sincere hope that the lessons learned from the trenches will improve our nation’s ability to fight the next pandemic.

Chapter 7 takes a closer look at some of the Sea Around Us fish-landings data that we assessed for Benford agreement in Chapter 5. We chose these data because of the mixed agreement findings among them: while the full dataset and several sets of subgroups indicated that the data exhibited Benford validity, when we analyzed West African countries individually, a number of them were found to have unacceptable Benford agreement and therefore problematic Benford validity. We present ways in which researchers can assess the impact of unacceptable Benford agreement on their analyses.

New product development processes need to be compliant to regulatory requirements, and this chapter highlights the salient processes and quality systems to put into place to achieve success. Project management is made simple with specific tools provided here. Customer feedback is channeled into specific product characteristics, and the right tools are shown in this chapter. The biopharma industry has statistics showing less than 10% of starting compounds succeed in reaching market approval, and this chapter explains what causes these failures. The key issues that have repeatedly caused failure during device and diagnostic product development are also pointed out. Ethical decisions have to be made during product development as shown in this chapter. Outsourcing is a real option due to the availability of many contract research and manufacturing organizations, and judicious use of this option is discussed in this chapter. Key milestones that reduce risk and show transition from early stage to preclinical prototype stages are reviewed here. Does the popular concept of minimum viable product in software development apply in biomedicine prototyping? Other similar questions that help the reader understand pitfalls and best practices are answered here.

How do you read a patent and what subject matter is patentable? What is the purpose of a patent? Who is an inventor on the patent if work is done by many people on the project? What is the process of obtaining a patent in my country and globally? Read this chapter to see how you could lose commercialization rights to your own invention. When exactly does an invention or idea become patentable? Once you own a patent, how can you make money from it? What is the process of licensing and the key terms that should be negotiated in such a license agreement? What is the use of a copyright or a trade secret in biotech? What exactly constitutes patent infringement ? These questions and many others are addressed in this chapter on intellectual property.

From the long path through preclinical development, entering the regulatory field of interactions for human clinical trials can sometimes feel like you are walking into the lion’s den. This chapter guides you through an understanding of how to interact and how to prepare for FDA meetings so that they are on your side rather than fighting you. The common goals of companies and the FDA are highlighted here. Specific issues with identifying the appropriate regulatory approval pathway are discussed here with cautionary case studies. Complex new technologies which combine diagnostics and drugs, or devices and software, or AI-based dynamic software are reviewed here. The best approach to the appropriate regulatory pathway will be clear after reading this chapter. Case studies are used to show successful pathways taken by cutting-edge developments, such as cell-based therapy.

Emerging neurotechnology offers increasingly individualised brain information, enabling researchers to identify mental states and content. When accurate and valid, these brain-reading technologies also provide data that could be useful in criminal legal procedures, such as memory detection with EEG and the prediction of recidivism with fMRI. Yet, unlike in medicine, individuals involved in criminal cases will often be reluctant to undergo brain-reading procedures. This raises the question of whether coercive brain-reading could be permissible in criminal law. Coercive Brain-Reading in Criminal Justice examines this question in view of European human rights: the prohibition of ill treatment, the right to privacy, freedom of thought, freedom of expression, and the privilege against self-incrimination. The book argues that, at present, the established framework of human rights does not exclude coercive brain-reading. It does, however, delimit the permissible use of forensic brain-reading without valid consent. This cautionary, cutting-edge book lays a crucial foundation for understanding the future of criminal legal proceedings in a world of ever-advancing neurotechnology.

A solid QCA does not end with the analytic moment. Researchers must make several analytic decisions at various stages in the analysis, some with more confidence than others. Researchers might also be confronted with data that are structured in analytically relevant ways. For example, cases might group into different geographic, substantive, or temporal clusters, or there might be relevant causal dependencies or sequences among conditions.

This chapter introduces the different robustness and diagnostic tools available in R to assess QCA results. It enables the reader to investigate to what extent their QCA results are robust against equally plausible analytic decisions regarding the selection of calibration anchors or consistency and frequency cut-offs. We present possibilities to assess robustness in R. Moreover, we introduce tools for cluster diagnostics and discuss strategies for dealing with timing and temporality, including ‘coincidence analysis’ (CNA).

Learning goals:

- Basic understanding of different approaches to diagnosing and assessing QCA results.

- Familiarity with how the robustness of QCA results to different analytical decisions can be assessed.

- Familiarity with proposals on how to assess QCA results in the presence of clustered data.

- Familiarity with how to model sequences and causal chains in R.