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In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever. The Bayesian methodology is well suited to address the issues arising in the planning, analysis, and conduct of clinical trials. Due to their flexibility, Bayesian design methods based on the accrued data of ongoing trials have been recommended by both the US Food and Drug Administration and the European Medicines Agency for dose-response trials in early clinical development. A distinctive feature of the Bayesian approach is its ability to deal with external information, such as historical data, findings from previous studies and expert opinions, through prior elicitation. In fact, it provides a framework for embedding and handling the variability of auxiliary information within the planning and analysis of the study. A growing body of literature examines the use of historical data to augment newly collected data, especially in clinical trials where patients are difficult to recruit, which is the case for rare diseases, for example. Many works explore how this can be done properly, since using historical data has been recognized as less controversial than eliciting prior information from experts’ opinions. In this book, applications of Bayesian design in the planning and analysis of clinical trials are introduced, along with methodological contributions to specific topics of Bayesian statistics. Finally, two reviews regarding the state-of-the-art of the Bayesian approach in clinical field trials are presented.

Humanities --- Social interaction --- dose-escalation --- combination study --- modelling assumption --- interaction --- adaptive designs --- adaptive randomization --- Bayesian designs --- clinical trials --- predictive power --- target allocation --- Bayesian inference --- highest posterior density intervals --- normal approximation --- predictive analysis --- sample size determination --- bayesian meta-analysis --- clustering --- binary data --- priors --- frequentist validation --- Bayesian --- rare disease --- prior distribution --- meta-analysis --- sample size --- bridging studies --- distribution distance --- oncology --- phase I --- dose-finding --- dose–response --- bayesian inference --- prior elicitation --- latent dirichlet allocation --- clinical trial --- power-prior --- poor accrual --- Bayesian trial --- cisplatin --- doxorubicin --- oxaliplatin --- dose escalation --- PIPAC --- peritoneal carcinomatosis --- randomized controlled trial --- causal inference --- doubly robust estimation --- propensity score --- Bayesian monitoring --- futility rules --- interim analysis --- posterior and predictive probabilities --- stopping boundaries --- Bayesian trial design --- early phase dose finding --- treatment combinations --- optimal dose combination

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In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever. The Bayesian methodology is well suited to address the issues arising in the planning, analysis, and conduct of clinical trials. Due to their flexibility, Bayesian design methods based on the accrued data of ongoing trials have been recommended by both the US Food and Drug Administration and the European Medicines Agency for dose-response trials in early clinical development. A distinctive feature of the Bayesian approach is its ability to deal with external information, such as historical data, findings from previous studies and expert opinions, through prior elicitation. In fact, it provides a framework for embedding and handling the variability of auxiliary information within the planning and analysis of the study. A growing body of literature examines the use of historical data to augment newly collected data, especially in clinical trials where patients are difficult to recruit, which is the case for rare diseases, for example. Many works explore how this can be done properly, since using historical data has been recognized as less controversial than eliciting prior information from experts’ opinions. In this book, applications of Bayesian design in the planning and analysis of clinical trials are introduced, along with methodological contributions to specific topics of Bayesian statistics. Finally, two reviews regarding the state-of-the-art of the Bayesian approach in clinical field trials are presented.

dose-escalation --- combination study --- modelling assumption --- interaction --- adaptive designs --- adaptive randomization --- Bayesian designs --- clinical trials --- predictive power --- target allocation --- Bayesian inference --- highest posterior density intervals --- normal approximation --- predictive analysis --- sample size determination --- bayesian meta-analysis --- clustering --- binary data --- priors --- frequentist validation --- Bayesian --- rare disease --- prior distribution --- meta-analysis --- sample size --- bridging studies --- distribution distance --- oncology --- phase I --- dose-finding --- dose–response --- bayesian inference --- prior elicitation --- latent dirichlet allocation --- clinical trial --- power-prior --- poor accrual --- Bayesian trial --- cisplatin --- doxorubicin --- oxaliplatin --- dose escalation --- PIPAC --- peritoneal carcinomatosis --- randomized controlled trial --- causal inference --- doubly robust estimation --- propensity score --- Bayesian monitoring --- futility rules --- interim analysis --- posterior and predictive probabilities --- stopping boundaries --- Bayesian trial design --- early phase dose finding --- treatment combinations --- optimal dose combination

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At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. 'What Makes Us Smart' makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory - in other words, data and resource limitations. Framing human intelligence in terms of these constraints, Samuel Gershman shows how a deeper computational logic underpins the 'stupid' errors of human cognition.

Cognition --- Cognitive psychology. --- Age factors. --- Psychology, Cognitive --- Cognitive science --- Psychology --- Age factors in cognition --- Ability, Influence of age on --- Cognition. --- Intellect. --- Human intelligence --- Intelligence --- Mind --- Ability --- Thought and thinking --- Accuracy and precision. --- Action potential. --- Ad hoc hypothesis. --- Ad hominem. --- Adaptive bias. --- Almost surely. --- Alternative hypothesis. --- Altruism. --- Ambiguity. --- Analogy. --- Anecdote. --- Approximation. --- Attractiveness. --- Bayes' theorem. --- Bayesian inference. --- Bayesian probability. --- Bayesian. --- Behavior. --- Circular reasoning. --- Cognitive flexibility. --- Cognitive style. --- Commitment device. --- Confidence. --- Confirmation bias. --- Conspiracy theory. --- Controllability. --- Counterintuitive. --- Credibility. --- Decision-making. --- Effectiveness. --- Efficacy. --- Efficiency. --- Efficient coding hypothesis. --- Efficient frontier. --- Estimation. --- Expected value. --- Explanation. --- Fair coin. --- Fair market value. --- Gimmick. --- Guessing. --- Heuristic. --- Hot Hand. --- Human intelligence. --- Hypothesis. --- Illusion of control. --- Inductive bias. --- Inference. --- Intelligent design. --- Learnability. --- Lightness (philosophy). --- Likelihood function. --- Logical extreme. --- Logical reasoning. --- Moral hazard. --- Motivated reasoning. --- Mutual exclusivity. --- Natural approach. --- Normative. --- Observation. --- Observational learning. --- Of Miracles. --- Opportunity cost. --- Optimism bias. --- Optimism. --- Our Choice. --- Pairwise comparison. --- Perfect rationality. --- Physical attractiveness. --- Point estimation. --- Politeness. --- Positive feedback. --- Predictability. --- Prediction. --- Predictive coding. --- Predictive power. --- Principle of rationality. --- Prior probability. --- Probability. --- Prosocial behavior. --- Quantity. --- Rational agent. --- Rational choice theory. --- Rationality. --- Reason. --- Reinforcement learning. --- Result. --- Self-control. --- Sophistication. --- Spontaneous recovery. --- Strong inference. --- Suggestion. --- Theory. --- Thought. --- Truth value. --- Uncertainty. --- Utility. --- Value of information. --- With high probability. --- PSYCHOLOGY / Cognitive Psychology & Cognition --- COMPUTERS / Logic Design

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Quantitative models are crucial to almost every area of ecosystem science. They provide a logical structure that guides and informs empirical observations of ecosystem processes. They play a particularly crucial role in synthesizing and integrating our understanding of the immense diversity of ecosystem structure and function. Increasingly, models are being called on to predict the effects of human actions on natural ecosystems. Despite the widespread use of models, there exists intense debate within the field over a wide range of practical and philosophical issues pertaining to quantitative modeling. This book--which grew out of a gathering of leading experts at the ninth Cary Conference--explores those issues. The book opens with an overview of the status and role of modeling in ecosystem science, including perspectives on the long-running debate over the appropriate level of complexity in models. This is followed by eight chapters that address the critical issue of evaluating ecosystem models, including methods of addressing uncertainty. Next come several case studies of the role of models in environmental policy and management. A section on the future of modeling in ecosystem science focuses on increasing the use of modeling in undergraduate education and the modeling skills of professionals within the field. The benefits and limitations of predictive (versus observational) models are also considered in detail. Written by stellar contributors, this book grants access to the state of the art and science of ecosystem modeling.

Ecology --- Mathematical models. --- 3D modeling. --- Accuracy and precision. --- Adaptive management. --- Addition. --- Agriculture. --- Algorithm. --- Bayesian inference. --- Bayesian. --- Biodiversity. --- Biogeochemical cycle. --- Biogeochemistry. --- Biology. --- Biomass (ecology). --- Calculation. --- Calibration. --- Carbon cycle. --- Case study. --- Chlorophyll. --- Climate change. --- Climate. --- Computer simulation. --- Conceptual model. --- Curriculum. --- Data set. --- Decision-making. --- Differential equation. --- Ecological Society of America. --- Ecological forecasting. --- Ecology. --- Ecosystem ecology. --- Ecosystem management. --- Ecosystem model. --- Ecosystem. --- Empirical relationship. --- Environmental issue. --- Estimation theory. --- Estimation. --- Eutrophication. --- Experiment. --- Fertilizer. --- Food web. --- Forecasting. --- General circulation model. --- Global warming. --- Implementation. --- Inference. --- Initial condition. --- Institute of Ecosystem Studies. --- Learning. --- Likelihood function. --- Mass balance. --- Mathematics. --- Measurement. --- Monte Carlo method. --- National Science Foundation. --- Nitrogen cycle. --- Nitrogen. --- Nutrient. --- Organism. --- Parameter. --- Parametrization. --- Phytoplankton. --- Predation. --- Predictability. --- Prediction. --- Predictive modelling. --- Predictive power. --- Primary production. --- Probability. --- Propagation of uncertainty. --- Proportion (architecture). --- Quantity. --- Regression analysis. --- Remote sensing. --- Requirement. --- Result. --- Risk assessment. --- Scientific method. --- Scientist. --- Sensitivity analysis. --- Simulation. --- Soil organic matter. --- Soil. --- Spatial scale. --- State variable. --- Statistic. --- Statistical hypothesis testing. --- Statistics. --- Suggestion. --- Time series. --- Trade-off. --- Trophic level. --- Uncertainty analysis. --- Uncertainty. --- Variable (mathematics). --- Vegetation. --- Water column. --- Water quality. --- Weather forecasting. --- Zooplankton.