Anders Källén

Denna lärobok innehåller det väsentliga i en kurs i flerdimensionell analys med vektoranalys för universitet och högskola. Den består i princip av tre delar: differentialkalkyl, integralkalkyl och vektoranalys. Fokus ligger på de begrepp och de färdigheter som behövs i tillämpningar, vilket speciellt är sant för integralkalkylen vars matematiska teori är mer styvmoderligt behandlad och ersatt med ett intuitivt resonemang. I vektoranalysen är fokus inte enbart på de tre grundläggande satserna, Gauss, Greens och Stokes satser, utan det illustreras också hur dessa används inom viktiga fysikaliska teorier. Det betyder till exempel en diskussion om den fysikaliska innebörden av Maxwells ekvationer.

Being that pharmacokinetics (PK) is the study of how the body handles various substances, it is not surprising that PK plays an important role in the early development of new drugs. However, the clinical research community widely believes that mathematics in some way blurs the true meaning of PK. Demonstrating that quite the opposite is true, Computational Pharmacokinetics outlines the fundamental concepts and models of PK from a mathematical perspective based on clinically relevant parameters. After an introductory chapter, the book presents a noncompartmental approach to PK and discusses the numerical analysis of PK data, including a description of an absorption process through numerical deconvolution. The author then builds a simple physiological model to better understand PK volumes and compares this model to other methods. The book also introduces compartmental models, discusses their limitations, and creates a general-purpose type of model. The final chapter looks at the relationship between drug concentration and effect, known as PK/pharmacodynamics (PD) modeling. With both a solid discussion of theory and the use of practical examples, this book will enable readers to thoroughly grasp the computational factors of PK modeling.

Understanding Biostatistics looks at the fundamentals of biostatistics, using elementary statistics to explore the nature of statistical tests. This book is intended to complement first-year statistics and biostatistics textbooks. The main focus here is on ideas, rather than on methodological details. Basic concepts are illustrated with representations from history, followed by technical discussions on what different statistical methods really mean. Graphics are used extensively throughout the book in order to introduce mathematical formulae in an accessible way. Key features: Discusses confidence intervals and p-values in terms of confidence functions. Explains basic statistical methodology represented in terms of graphics rather than mathematical formulae, whilst highlighting the mathematical basis of biostatistics. Looks at problems of estimating parameters in statistical models and looks at the similarities between different models. Provides an extensive discussion on the position of statistics within the medical scientific process. Discusses distribution functions, including the Guassian distribution and its importance in biostatistics. This book will be useful for biostatisticians with little mathematical background as well as those who want to understand the connections in biostatistics and mathematical issues.

Being that pharmacokinetics (PK) is the study of how the body handles various substances, it is not surprising that PK plays an important role in the early development of new drugs. However, the clinical research community widely believes that mathematics in some way blurs the true meaning of PK. Demonstrating that quite the opposite is true, Computational Pharmacokinetics outlines the fundamental concepts and models of PK from a mathematical perspective based on clinically relevant parameters. After an introductory chapter, the book presents a noncompartmental approach to PK and discusses the numerical analysis of PK data, including a description of an absorption process through numerical deconvolution. The author then builds a simple physiological model to better understand PK volumes and compares this model to other methods. The book also introduces compartmental models, discusses their limitations, and creates a general-purpose type of model. The final chapter looks at the relationship between drug concentration and effect, known as PK/pharmacodynamics (PD) modeling. With both a solid discussion of theory and the use of practical examples, this book will enable readers to thoroughly grasp the computational factors of PK modeling.