{"title":"Ton J Cleophas","description":null,"products":[{"product_id":"regression-analysis-in-medical-research-book-ton-j-cleophas-9783319719368","title":"Regression Analysis in Medical Research","description":"This edition is a pretty complete textbook and tutorial for medical and health care students, as well as a recollection\/update bench, and help desk for professionals. Novel approaches already applied in published clinical research will be addressed: matrix analyses, alpha spending, gate keeping, kriging, interval censored regressions, causality regressions, canonical regressions, quasi-likelihood regressions, novel non-parametric regressions. Each chapter can be studied as a stand-alone, and covers one field in the fast growing world of regression analyses.       The authors, as professors in statistics and machine learning at European universities, are worried, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis. It is the main incentive for writing this 28 chapter edition, consistent of                   - 28 major fields of regression analysis,                  - their condensed maths,                  - their applications in medical and health research as published so far,                  - step by step analyses for self-assessment,                  - conclusion and reference sections.  Traditional regression analysis is adequate for epidemiology, but lacks the precision required for clinical investigations. However, in the past two decades modern regression methods have proven to be much more precise. And so it is time, that a book described regression analyses for clinicians. The current edition is the first to do so. It is written for a non-mathematical readership. Self-assessment data-files are provided through Springer' s \"Extras Online\".","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":49737318990097,"sku":"NGR9783319719368","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/331971936X.jpg?v=1751126995"},{"product_id":"modern-bayesian-statistics-in-clinical-research-book-ton-j-cleophas-9783319927466","title":"Modern Bayesian Statistics in Clinical Research","description":"The current textbook has been written as a help to medical \/ health professionals and students for the study of modern Bayesian statistics, where posterior and prior odds have been replaced with posterior and prior likelihood distributions. Why may likelihood distributions better than normal distributions estimate uncertainties of statistical test results? Nobody knows for sure, and the use of likelihood distributions instead of normal distributions for the purpose has only just begun, but already everybody is trying and using them. SPSS statistical software version 25 (2017) has started to provide a combined module entitled Bayesian Statistics including almost all of the modern Bayesian tests (Bayesian t-tests, analysis of variance (anova), linear regression, crosstabs etc.).     Modern Bayesian statistics is based on biological likelihoods, and may better fit clinical data than traditional tests based normal distributions do. This is the first edition to systematically implymodern Bayesian statistics in traditional clinical data analysis. This edition also demonstrates that Markov Chain Monte Carlo procedures laid out as Bayesian tests provide more robust correlation coefficients than traditional tests do. It also shows that traditional path statistics are both textually and conceptionally like Bayes theorems, and that structural equations models computed from them are the basis of multistep regressions, as used with causal Bayesian networks.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":49739360174353,"sku":"NGR9783319927466","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52595833962769,"sku":"NLS9783319927466","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3319927469.jpg?v=1750902469"},{"product_id":"statistics-applied-to-clinical-trials-book-ton-j-cleophas-9781402005695","title":"Statistics Applied to Clinical Trials","description":"In 1848 the first randomized controlled trial was published by the English Medical Research Council in the \"British Medical Journal\". Until then, observations had been uncontrolled. Initially, trials frequently did not confirm hypotheses to be tested. This phenomenon was attributed to low sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were recognized and subsequently were better accounted for: carry-over effects due to insufficient washout from previous treatments, time effects due to external factors and the natural history of the condition under study, bias due to asymmetry between treatment groups, lack of sensitivity due to a negative correlation between treatment responses, and so on. Such flaws, mainly of a technical nature, have been largely corrected and led to trials after 1970 being of significantly better quality than before. The past decade has focused, in addition to technical aspects, on the need for circumspection in planning and conducting of clinical trials. As a consequence, prior to approval, clinical trial protocols are now routinely scrutinized by different circumstantial bodies, including ethics committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses. This book not only explains classical statistical analyses of clinical trials, but addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, and meta-analyses, and provides a framework of the best statistical methods available for such purposes.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50372162126097,"sku":"CIN1402005695G","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1402005695.jpg?v=1751403385"},{"product_id":"machine-learning-in-medicine-a-complete-overview-book-ton-j-cleophas-9783319151946","title":"Machine Learning in Medicine - a Complete Overview","description":"The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In eighty chapters eighty different machine learning methodologies are reviewed, in combination with data examples for self-assessment. Each chapter can be studied without the need to consult other chapters.  The amount of data stored in the world's databases doubles every 20 months, and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure \/ outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations.  So far medical professionals have been rather reluctant to use machine learning. Also, in the field of diagnosis making, few doctors may want a computer checking them, are interested in collaboration with a computer or with computer engineers. Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks and other data mining methodologies.  Each chapter starts with purposes and scientific questions. Then, step-by-step analyses, using data examples, are given. Finally, a paragraph with conclusion, and references to the corresponding sites of three introductory textbooks, previously written by the same authors, is given.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50398696472849,"sku":"CIN3319151940G","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3319151940.jpg?v=1751415363"},{"product_id":"understanding-clinical-data-analysis-book-ton-j-cleophas-9783319395852","title":"Understanding Clinical Data Analysis","description":"This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.    In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? Thebook will cover the WHY-SOs.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51062822568209,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51062825124113,"sku":"NIN9783319395852","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52459208081681,"sku":"NLS9783319395852","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3319395858.jpg?v=1751284949"},{"product_id":"modern-survival-analysis-in-clinical-research-book-ton-j-cleophas-9783031316340","title":"Modern Survival Analysis in Clinical Research","description":"An important novel menu for Survival Analysis entitled Accelerated Failure Time (AFT) models has been published by IBM (international Businesss Machines) in its SPSS statistical software update of 2023. Unlike the traditional Cox regressions that work with hazards, which are the ratio of deaths and non-deaths in a sample, it works with risk of death, which is the proportion of deaths in the same sample. The latter approach may provide better sensitivity of testing, but has been seldom applied, because with computers risks are tricky and hazards because they are odds are fine. This was underscored in 1997 by Keiding and colleague statisticians from Copenhagen University who showed better-sensitive goodness of fit and null-hypothesis tests with AFT than with Cox survival tests.  So far, a controlled study of a representative sample of clinical Kaplan Meier assessments, where the sensitivity of Cox regression is systematically tested against that of AFT modeling, hasnot been accomplished. This edition is the first textbook and tutorial of AFT modeling both for medical and healthcare students and for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional Cox regressions. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment.      We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern data analysis methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 25 years and their research can be characterized asa continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51386718159121,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":51386719371537,"sku":"NGR9783031316340","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3031316347.jpg?v=1751415327"},{"product_id":"application-of-regularized-regressions-to-identify-novel-predictors-in-clinical-book-ton-j-cleophas-9783031722462","title":"Application of Regularized Regressions to Identify Novel Predictors in Clinical Research","description":"This textbook is an important novel menu for multiple variables regression entitled \"regularized regression\". It is a must have for identifying unidentified leading factors. Also, you get fitted parameters for your overfitted data. Finally, there is no more need for commonly misunderstood p-values. Instead, the regression coefficient, R-value, as reported from a regression line has been applied as the key predictive estimator of the regression study. With simple one by one variable regression it is no wider than -1 to +1. With multiple variables regression it can easily get \u0026gt; +1 or \u0026lt; -1. This means we have a seriously flawed regression model, mostly due to collinearity or non-linear data. Completing the analysis will lead to overfitting, and thus a meaningless significant study due to data spread wider than compatible with random. In order for the regression coefficients to remain in the right size, fortunately a shrinking procedure has been invented.    In the past two decades regularized regression has become a major topic of research, particularly with high dimensional data. Yet, the method is pretty new and infrequently used in real-data analysis. Its performance as compared to traditional null hypothesis testing has to be confirmed by prospective comparisons. Most studies published to date are of a theoretical nature involving statistical modeling and simulation studies. The journals Nature and Science published 19 and 10 papers of this sort in the past 8 years. The current edition will for the first time systematically test regularized regression against traditional regression analysis in 20 clinical data examples.    The edition is also a textbook and tutorial for medical and healthcare students as well as recollection bench and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regressions. Step by step analyses of 20 data files are included for self-assessment. The authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics and Professor Cleophas is past-president of the American College of Angiology. The authors have been working together for 25 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is a discipline at the interface of biology and mathematics.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51728243949841,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":51728244408593,"sku":"NGR9783031722462","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3031722469.jpg?v=1750806321"},{"product_id":"kernel-ridge-regression-in-clinical-research-book-ton-j-cleophas-9783031107191","title":"Kernel Ridge Regression in Clinical Research","description":"IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the         kernel trick for reduced arithmetic complexity, estimation of uncertainty by Gaussians unlike histograms, corrected data-overfit by ridge regularization, availability of 8 alternative kernel density models for datafit.    A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity \/ dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things)studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection \/ update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52120240587025,"sku":"NLS9783031107191","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783031107191.jpg?v=1757424736"},{"product_id":"modern-meta-analysis-book-ton-j-cleophas-9783319558943","title":"Modern Meta-Analysis","description":"The current edition is the first textbook in the field of meta-analysis entirely written by two clinical scientists, and it consists of many data examples and step by step analyses, mostly from the authors' own clinical research.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52132242489617,"sku":"NLS9783319558943","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319558943.jpg?v=1757516114"},{"product_id":"machine-learning-in-medicine-book-ton-j-cleophas-9789400793637","title":"Machine Learning in Medicine","description":"Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52133709480209,"sku":"NLS9789400793637","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400793637.jpg?v=1757532708"},{"product_id":"clinical-data-analysis-on-a-pocket-calculator-book-ton-j-cleophas-9783319800745","title":"Clinical Data Analysis on a Pocket Calculator","description":"In medical and health care the scientific method is little used, and statistical software programs are experienced as black box programs producing lots of p-values, but little answers to scientific questions. The pocket calculator analyses appears to be, particularly, appreciated, because they enable medical and health professionals and students for the first time to understand the scientific methods of statistical reasoning and hypothesis testing. So much so, that it can start something like a new dimension in their professional world. In addition, a number of statistical methods like power calculations and required sample size calculations can be performed more easily on a pocket calculator, than using a software program. Also, there are some specific advantages of the pocket calculator method. You better understand what you are doing. The pocket calculator works faster, because far less steps have to be taken, averages can be used. The current nonmathematical book is complementary to the nonmathematical \"SPSS for Starters and 2nd Levelers\" (Springer Heidelberg Germany 2015, from the same authors), and can very well be used as its daily companion.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52136661123345,"sku":"NLS9783319800745","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319800745.jpg?v=1757554494"},{"product_id":"spss-for-starters-and-2nd-levelers-book-ton-j-cleophas-9783319205991","title":"SPSS for Starters and 2nd Levelers","description":"A unique point of this book is its low threshold, textually simple and at the same time full of self-assessment opportunities. Other unique points are the succinctness of the chapters with 3 to 6 pages, the presence of entire-commands-texts of the statistical methodologies reviewed and the fact that dull scientific texts imposing an unnecessary burden on busy and jaded professionals have been left out. For readers requesting more background, theoretical and mathematical information a note section with references is in each chapter.   The first edition in 2010 was the first publication of a complete overview of SPSS methodologies for medical and health statistics. Well over 100,000 copies of various chapters were sold within the first year of publication. Reasons for a rewrite were four.  First, many important comments from readers urged for a rewrite. Second, SPSS has produced many updates and upgrades, with relevant novel and improved methodologies. Third, the authorsfelt that the chapter texts needed some improvements for better readability: chapters have now been classified according the outcome data helpful for choosing your analysis rapidly, a schematic overview of data, and explanatory graphs have been added. Fourth, current data are increasingly complex and many important methods for analysis were missing in the first edition.    For that latter purpose some more advanced methods seemed unavoidable, like hierarchical loglinear methods, gamma and Tweedie regressions and random intercept analyses. In order for the contents of the book to remain covered by the title, the authors renamed the book: SPSS for Starters and 2nd Levelers.  Special care was, nonetheless, taken to keep things as simple as possible, simple menu commands are given. The arithmetic is still of a no-more-than high-school level. Step-by-step analyses of different statistical methodologies are given with the help of 60 SPSS data files available through the internet. Because of the lack of time of this busy group of people, the authors have given every effort to produce a text as succinct as possible.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52139037065489,"sku":"NLS9783319205991","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319205991.jpg?v=1757566618"},{"product_id":"machine-learning-in-medicine-book-ton-j-cleophas-9789400795129","title":"Machine Learning in Medicine","description":"Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other subjects.  Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52147975848209,"sku":"NLS9789400795129","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400795129.jpg?v=1757602219"},{"product_id":"statistics-applied-to-clinical-trials-book-ton-j-cleophas-9781402010965","title":"Statistics Applied to Clinical Trials","description":"The authors have taught statistics and given statistics workshops in France and the Netherlands for almost 4 years by now. Their material, mainly on power point, consists of 12 lectures that have been continuously changed and improved by interaction with various audiences. For the purpose of the current book simple English text has been added to the formulas and figures, and the power points sheets have been rewritten in the format given by Kluwer Academic Publishers. Cartoons have been removed, since this is not so relevant for the transmission of thought through a written text, and at the end of each lecture (chapter) a representative number of questions and exercises for self-assessment have been added. At the end of the book detailed answers to the questions and exercises per lecture are given. The book has been produced with the same size and frontpage as the textbook Statistics Applied To Clinical Trials by the same authors and edited by same publishers ( 2nd Edition, DordrechtiBostonlLondon, 2002), and can be applied together with the current self-assessment book or separately. The current self-assessment book is different from the texbook, because it focuses on the most important aspects rather than trying to be complete. So, it does not deal with all of the subjects assessed in the texbook. Instead, it repeats on and on the principle things that are needed for every analysis, and it gives many examples that are further explained by arrows in the figures.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52148677345553,"sku":"NLS9781402010965","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9781402010965.jpg?v=1757604302"},{"product_id":"machine-learning-in-medicine-book-ton-j-cleophas-9789400778689","title":"Machine Learning in Medicine","description":"Machine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, fuzzy modeling, various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, association rule learning, anomaly detection, and correspondence analysis. This third volume addresses more advanced methods and includes subjects like evolutionary programming, stochastic methods, complex sampling, optional binning, Newton's methods, decision trees, and other subjects. Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52331295146257,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52331295867153,"sku":"NLS9789400778689","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400778689.jpg?v=1758147960"},{"product_id":"spss-for-starters-book-ton-j-cleophas-9789400798793","title":"SPSS for Starters","description":"This small book contains all statistical tests that are relevant for starters on SPS. Each test is explained using a data example from clinical practice, including every step in SPS and the main tables of results with an accompanying text with interpretations of the results and hints convenient for data reporting, i.e., scientific clinical articles and poster presentations. In order to facilitate the use of this cookbook the data files of the examples are made available by the publisher on the Internet. For investigators who wish to perform their own data analyses from the very start the book can be used as a step-by-step guideline. They can enter their separate data or enter their entire data file, e.g., from Excel, simply by opening an Excel file in SPS. SPS statistical software is a user-friendly statistical software with many help and tutor pages. However, for the novices on SPS an even more basic approach is welcome. The book is meant for this very purpose, and can be used without the help of a teacher. The authors are well-aware that this cookbook contains a minimal amount of text and a maximal technical details, but we believe that this property will not refrain students from mastering the SPS software systematic, and that, instead, it will even be a help to that aim. Yet, we recommend that it be used together with the textbook Statistics Applied to Clinical Trials by Cleophas et al, 4th Edition, 2009, Springer Dordrecht.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52334838219025,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52334838939921,"sku":"NLS9789400798793","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400798793.jpg?v=1758158151"},{"product_id":"machine-learning-in-medicine-book-ton-j-cleophas-9789400768857","title":"Machine Learning in Medicine","description":"Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other subjects.  Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52335713583377,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52335714173201,"sku":"NLS9789400768857","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400768857.jpg?v=1758160907"},{"product_id":"machine-learning-in-medicine-cookbook-three-book-ton-j-cleophas-9783319121628","title":"Machine Learning in Medicine - Cookbook Three","description":"Unique features of the book involve the following.  1.This book is the third volume of a three volume series of cookbooks entitled \"Machine Learning in Medicine - Cookbooks One, Two, and Three\". No other self-assessment works for the medical and health care community covering the field of machine learning have been published to date.  2. Each chapter of the book can be studied without the need to consult other chapters, and can, for the readership's convenience, be downloaded from the internet. Self-assessment examples are available at extras.springer.com.  3. An adequate command of machine learning methodologies is a requirement for physicians and other health workers, particularly now, because the amount of medical computer data files currently doubles every 20 months, and, because, soon, it will be impossible for them to take proper data-based health decisions without the help of machine learning.  4. Given the importance of knowledge of machine learning in the medical and health care community, and the current lack of knowledge of it, the readership will consist of any physician and health worker.  5. The book was written in a simple language in order to enhance readability not only for the advanced but also for the novices.  6. The book is multipurpose, it is an introduction for ignorant, a primer for the inexperienced, and a self-assessment handbook for the advanced.  7. The book, was, particularly, written for jaded physicians and any other health care professionals lacking time to read the entire series of three textbooks.  8. Like the other two cookbooks it contains technical descriptions and self-assessment examples of 20 important computer methodologies for medical data analysis, and it, largely, skips the theoretical and mathematical background.  9. Information of theoretical and mathematical background of the methods described are displayed in a \"notes\" section at the end of eachchapter.  10.Unlike traditional statistical methods, the machine learning methodologies are able to analyze big data including thousands of cases and hundreds of variables.  11. The medical and health care community is little aware of the multidimensional nature of current medical data files, and experimental clinical studies are not helpful to that aim either, because these studies, usually, assume that subgroup characteristics are unimportant, as long as the study is randomized. This is, of course, untrue, because any subgroup characteristic may be vital to an individual at risk.  12. To date, except for a three volume introductary series on the subject entitled \"Machine Learning in Medicine Part One, Two, and Thee, 2013, Springer Heidelberg Germany\" from the same authors, and the current cookbook series, no books on machine learning in medicine have been published.  13. Another unique feature of the cookbooks is that it was jointly written by two authors from different disciplines, one being a clinician\/clinical pharmacologist, one being a mathematician\/biostatistician.  14. The authors have also jointly been teaching at universities and institutions throughout Europe and the USA for the past 20 years.  15. The authors have managed to cover the field of medical data analysis in a nonmathematical way for the benefit of medical and health workers.  16. The authors already successfully published many statistics textbooks and self-assessment books, e.g., the 67 chapter textbook entitled \"Statistics Applied to Clinical Studies 5th Edition, 2012, Springer Heidelberg Germany\" with downloads of 62,826 copies.  17. The current cookbook makes use, in addition to SPSS statistical software, of various free calculators from the internet, as well as the Konstanz Information Miner (Knime), a widely approved free machine learning package, and the free Weka Data Mining package from New Zealand.  18. The above software packages with hundreds of nodes, the basic processing units including virtually all of the statistical and data mining methods, can be used not only for data analyses, but also for appropriate data storage.  19. The current cookbook shows, particularly, for those with little affinity to value tables, that data mining in the form of a visualization process is very well feasible, and often more revealing than traditional statistics.  20.The Knime and Weka data miners uses widely available excel data files.  21. In current clinical research prospective cohort studies are increasingly replacing the costly controlled clinical trials, and modern machine learning methodologies like probit and tobit regressions as well as neural networks, Bayesian networks, and support vector machines prove to better fit their analysis than traditional statistical methods do.  22. The current cookbook not only includes concise descriptions of standard machine learning methods, but also of more recent methods like the linear machine learning models using ordinal and loglinear regression.  23. Machine learning tends to increasingly use evolutionary operation methodologies. Also this subject has been covered.  24. All of the methods described have been applied in the authors' own research prior to this publication.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52336194584849,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52336195240209,"sku":"NLS9783319121628","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319121628.jpg?v=1758162542"},{"product_id":"regression-analysis-in-medical-research-book-ton-j-cleophas-9783030613969","title":"Regression Analysis in Medical Research","description":"Therefore, the editorial art work has now been systematically replaced with original statistical software tables and graphs for the benefit of an improved usage and understanding of the methods.   In the past few years, professionals have been flooded with big data.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52343447585041,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52343451255057,"sku":"NLS9783030613969","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783030613969.jpg?v=1758173733"},{"product_id":"spss-for-starters-and-2nd-levelers-book-ton-j-cleophas-9783319342504","title":"SPSS for Starters and 2nd Levelers","description":"A unique point of this book is its low threshold, textually simple and at the same time full of self-assessment opportunities. Other unique points are the succinctness of the chapters with 3 to 6 pages, the presence of entire-commands-texts of the statistical methodologies reviewed and the fact that dull scientific texts imposing an unnecessary burden on busy and jaded professionals have been left out. For readers requesting more background, theoretical and mathematical information a note section with references is in each chapter.   The first edition in 2010 was the first publication of a complete overview of SPSS methodologies for medical and health statistics. Well over 100,000 copies of various chapters were sold within the first year of publication. Reasons for a rewrite were four.  First, many important comments from readers urged for a rewrite. Second, SPSS has produced many updates and upgrades, with relevant novel and improved methodologies. Third, the authorsfelt that the chapter texts needed some improvements for better readability: chapters have now been classified according the outcome data helpful for choosing your analysis rapidly, a schematic overview of data, and explanatory graphs have been added. Fourth, current data are increasingly complex and many important methods for analysis were missing in the first edition.    For that latter purpose some more advanced methods seemed unavoidable, like hierarchical loglinear methods, gamma and Tweedie regressions and random intercept analyses. In order for the contents of the book to remain covered by the title, the authors renamed the book: SPSS for Starters and 2nd Levelers.  Special care was, nonetheless, taken to keep things as simple as possible, simple menu commands are given. The arithmetic is still of a no-more-than high-school level. Step-by-step analyses of different statistical methodologies are given with the help of 60 SPSS data files available through the internet. Because of the lack of time of this busy group of people, the authors have given every effort to produce a text as succinct as possible.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52405221196049,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52405221654801,"sku":"NLS9783319342504","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319342504.jpg?v=1758766511"},{"product_id":"regression-analysis-in-medical-research-book-ton-j-cleophas-9783319891231","title":"Regression Analysis in Medical Research","description":"This edition is a pretty complete textbook and tutorial for medical and health care students, as well as a recollection\/update bench, and help desk for professionals. Novel approaches already applied in published clinical research will be addressed: matrix analyses, alpha spending, gate keeping, kriging, interval censored regressions, causality regressions, canonical regressions, quasi-likelihood regressions, novel non-parametric regressions. Each chapter can be studied as a stand-alone, and covers one field in the fast growing world of regression analyses.       The authors, as professors in statistics and machine learning at European universities, are worried, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis. It is the main incentive for writingthis 28 chapter edition, consistent of                   - 28 major fields of regression analysis,                  - their condensed maths,                  - their applications in medical and health research as published so far,                  - step by step analyses for self-assessment,                  - conclusion and reference sections.  Traditional regression analysis is adequate for epidemiology, but lacks the precision required for clinical investigations. However, in the past two decades modern regression methods have proven to be much more precise.And so it is time, that a book described regression analyses for clinicians. The current edition is the first to do so. It is written for a non-mathematical readership. Self-assessment data-files are provided through Springer' s \"Extras Online\".","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52418389999889,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":52418390524177,"sku":"NGR9783319891231","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319891231.jpg?v=1758929052"},{"product_id":"human-experimentation-book-ton-j-cleophas-9780792358275","title":"Human Experimentation","description":"Despite their effectiveness in the evaluation of new  pharmacological compounds, controlled clinical trials are sometimes  inadequate. Using data from the literature as well as from the  author's own experience with clinical trials, Human  Experimentation: Methodologic Issues Fundamental to Clinical Trials  addresses such inadequacies and tries to provide solutions. This work  is the first to thoroughly examine these unsolved inadequacies and  problems with the design and the execution of clinical trials and,  more importantly, to provide solutions for these problems. It is  important for anyone who is involved in clinical research: clinicians,  pharmacists, biochemists, statisticians, nurses, sponsors, etc., and  anyone who is involved in applying results of research to patients,  i.e. physicians.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52429409845521,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52429410468113,"sku":"NLS9780792358275","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9780792358275.jpg?v=1759166828"},{"product_id":"machine-learning-in-medicine-book-ton-j-cleophas-9789402402605","title":"Machine Learning in Medicine","description":"Machine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, fuzzy modeling, various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, association rule learning, anomaly detection, and correspondence analysis. This third volume addresses more advanced methods and includes subjects like evolutionary programming, stochastic methods, complex sampling, optional binning, Newton's methods, decision trees, and other subjects. Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52451417227537,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52451417850129,"sku":"NLS9789402402605","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789402402605.jpg?v=1759361500"},{"product_id":"machine-learning-in-medicine-a-complete-overview-book-ton-j-cleophas-9783319386386","title":"Machine Learning in Medicine - a Complete Overview","description":"Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks and other data mining methodologies.Each chapter starts with purposes and scientific questions.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52455940653329,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52455941243153,"sku":"NLS9783319386386","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319386386.jpg?v=1759375815"},{"product_id":"modern-meta-analysis-book-ton-j-cleophas-9783319857756","title":"Modern Meta-Analysis","description":"Modern meta-analyses do more than combine the effect sizes of a series of similar studies. Meta-analyses are currently increasingly applied for any analysis beyond the primary analysis of studies, and for the analysis of big data. This 26-chapter book was written for nonmathematical professionals of medical and health care, in the first place, but, in addition, for anyone involved in any field involving scientific research. The authors have published over twenty innovative meta-analyses from the turn of the century till now. This edition will review the current state of the art, and will use for that purpose the methodological aspects of the authors' own publications, in addition to other relevant methodological issues from the literature.  Are there alternative works in the field? Yes, there are, particularly in the field of psychology. Psychologists have invented meta-analyses in 1970, and have continuously updated methodologies. Although very interesting, their work, just like the whole discipline of psychology, is rather explorative in nature, and so is their focus to meta-analysis. Then, there is the field of epidemiologists. Many of them are from the school of angry young men, who publish shocking news all the time, and JAMA and other publishers are happy to publish it. The reality is, of course, that things are usually not as bad as they seem. Finally, some textbooks, written by professional statisticians, tend to use software programs with miserable menu programs and requiring lots of syntax to be learnt. This is prohibitive to clinical and other health professionals.         The current edition is the first textbook in the field of meta-analysis entirely written by two clinical scientists, and it consists of many data examples and step by step analyses, mostly from the authors' own clinical research.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52481930821905,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52481932329233,"sku":"NLS9783319857756","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319857756.jpg?v=1759852458"},{"product_id":"machine-learning-in-medicine-a-complete-overview-book-ton-j-cleophas-9783030339692","title":"Machine Learning in Medicine  A Complete Overview","description":"Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and it was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care.  In this second edition the authors have removed the textual errors from the first edition. Also, the improved tables from the first edition, have been replaced with the  original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them.  The main purpose of the first edition was, to provide stepwise analyses of the novel  methods from data examples, but background information and clinical relevance information may have been somewhat lacking. Therefore, each chapter now contains a section entitled \"Background Information\".  Machine learning may be more informative, and may provide better sensitivity of testing than traditional analytic methods may do. In the second edition a place has been given for the use of machine learning not only to the analysis of observational clinical data, but also to that of controlled clinical trials.  Unlike the first edition, the second edition has drawings in full color providing a helpful extra dimension to the data analysis.   Several machine learning methodologies not yet covered in the first edition, but increasingly important today, have been included in this updated edition, for example, negative binomial and Poisson regressions, sparse canonical analysis, Firth's bias adjusted logistic analysis, omics research, eigenvalues and eigenvectors.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52537707168017,"sku":"NLS9783030339692","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783030339692.jpg?v=1760679206"},{"product_id":"clinical-data-analysis-on-a-pocket-calculator-book-ton-j-cleophas-9783319271033","title":"Clinical Data Analysis on a Pocket Calculator","description":"Inmedical and health care the scientific method is little used, and statisticalsoftware programs are experienced as black box programs producing lots ofp-values, but little answers to scientific questions.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52583630078225,"sku":"NLS9783319271033","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319271033.jpg?v=1761047537"},{"product_id":"understanding-clinical-data-analysis-book-ton-j-cleophas-9783319819174","title":"Understanding Clinical Data Analysis","description":"This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.    In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? Thebook will cover the WHY-SOs.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52584263123217,"sku":"NLS9783319819174","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319819174.jpg?v=1761049538"},{"product_id":"modern-bayesian-statistics-in-clinical-research-book-ton-j-cleophas-9783030065072","title":"Modern Bayesian Statistics in Clinical Research","description":"The current textbook has been written as a help to medical \/ health professionals and students for the study of modern Bayesian statistics, where posterior and prior odds have been replaced with posterior and prior likelihood distributions. Why may likelihood distributions better than normal distributions estimate uncertainties of statistical test results? Nobody knows for sure, and the use of likelihood distributions instead of normal distributions for the purpose has only just begun, but already everybody is trying and using them. SPSS statistical software version 25 (2017) has started to provide a combined module entitled Bayesian Statistics including almost all of the modern Bayesian tests (Bayesian t-tests, analysis of variance (anova), linear regression, crosstabs etc.).     Modern Bayesian statistics is based on biological likelihoods, and may better fit clinical data than traditional tests based normal distributions do. This is the first edition to systematically implymodern Bayesian statistics in traditional clinical data analysis. This edition also demonstrates that Markov Chain Monte Carlo procedures laid out as Bayesian tests provide more robust correlation coefficients than traditional tests do. It also shows that traditional path statistics are both textually and conceptionally like Bayes theorems, and that structural equations models computed from them are the basis of multistep regressions, as used with causal Bayesian networks.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52599870390545,"sku":"NLS9783030065072","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783030065072.jpg?v=1761087566"},{"product_id":"spss-for-starters-part-2-book-ton-j-cleophas-9789400748033","title":"SPSS for Starters, Part 2","description":"The first part of this title contained all statistical tests that are relevant for starters on SPSS, and included standard parametric and non-parametric tests for continuous and binary variables, regression methods, trend tests, and reliability and validity assessments of diagnostic tests.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52618493034769,"sku":"NLS9789400748033","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400748033.jpg?v=1761533284"},{"product_id":"statistical-analysis-of-clinical-data-on-a-pocket-calculator-part-2-book-ton-j-cleophas-9789400747036","title":"Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2","description":"The first part of this title contained all statistical tests relevant to starting clinical investigations, and included tests for continuous and binary data, power, sample size, multiple testing, variability, confounding, interaction, and reliability.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52619318034705,"sku":"NLS9789400747036","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400747036.jpg?v=1761535573"},{"product_id":"machine-learning-in-medicine-book-ton-j-cleophas-9789400758230","title":"Machine Learning in Medicine","description":"Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52657332355345,"sku":"NLS9789400758230","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400758230.jpg?v=1762228206"},{"product_id":"machine-learning-in-medicine-cookbook-two-book-ton-j-cleophas-9783319074122","title":"Machine Learning in Medicine - Cookbook Two","description":"The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure \/ outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible.  Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main incentive for the authors to complete a series of three textbooks entitled “Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013\", describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details.  For this reason we produced a 100 page cookbook, entitled \"Machine Learning in Medicine - Cookbook One\", with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled \"Machine Learning in Medicine - Cookbook Two\" is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care.  Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machinelearning methods, that are, likewise, based on the three major machine learning methodologies:    Cluster methodologies (Chaps. 1-3)  Linear methodologies (Chaps. 4-11)  Rules methodologies (Chaps. 12-20)  In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8.  We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing \"general purposes\", \"main scientific questions\" and \"conclusions\" are given in place.  Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52662253093137,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52662253814033,"sku":"NLS9783319074122","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319074122.jpg?v=1762269081"},{"product_id":"statistical-analysis-of-clinical-data-on-a-pocket-calculator-book-ton-j-cleophas-9789400712102","title":"Statistical Analysis of Clinical Data on a Pocket Calculator","description":"This book covers all relevant pocket calculator statistical methods, making it an ideal resource for those who wish to understand statistics but have no time for complex mathematics. It helps facilitate data analysis by detailing pocket calculator methods.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52662740713745,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52662741270801,"sku":"NLS9789400712102","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400712102.jpg?v=1762270228"},{"product_id":"machine-learning-in-medicine-cookbook-book-ton-j-cleophas-9783319041803","title":"Machine Learning in Medicine - Cookbook","description":"The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure \/ outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing.  Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled “Machine Learning in Medicine I-III” (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks.  General purposes and scientific questions of the methods are only briefly mentioned, but full attention is given to the technical details. The two authors, a statistician and current president of the International Association of Biostatistics and a clinician and past-president of the American College of Angiology, provide plenty of step-by-step analyses from their own research and data files for self-assessment are available at extras.springer.com.  From their experience the authors demonstrate that machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52683137909009,"sku":"NLS9783319041803","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319041803.jpg?v=1762319877"},{"product_id":"statistics-applied-to-clinical-studies-book-ton-j-cleophas-9789400728622","title":"Statistics Applied to Clinical Studies","description":"Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in less than seconds. This development, however fascinating, generally does not lead to simple results. We should not forget that clinical studies are, mostly, for confirming prior hypotheses based on sound arguments, and the simplest tests provide the best power and are adequate for such studies. In the past few years the authors of this 5th edition, as teachers and research supervisors in academic and top-clinical facilities, have been able to closely observe the latest developments in the field of clinical data analysis, and they have been able to assess their performance. In this 5th edition the 47 chapters of the previous edition have been maintained and upgraded according to the current state of the art, and 20 novel chapters have been added after strict selection of the most valuable and promising novel methods. The novel methods are explained using practical examples and step-by-step analyses readily accessible for non-mathematicians. All of the novel chapters have been internationally published by the authors in peer-reviewed journal, including the American Journal of Therapeutics, the European Journal of Clinical Investigation, The International journal of Clinical Pharmacology and therapeutics, and other journals, and permission is granted by all of them to use this material in the current book. We should add that the authors are well-qualified in their fields of knowledge. Professor Zwinderman is president-elect of the International Society of Biostatistics, and Professor Cleophas is past-president of the American College of Angiology. From their expertise they should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors, although from a different discipline, one clinician and one statistician, have been working and publishing together for over 10 years, and their research of statistical methodology can be characterized as a continued effort to demonstrate that statistics is not mathematics but rather a discipline at the interface of biology and mathematics. They firmly believe that any reader can benefit from this clinical approach to statistical data analysis.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":53522465816849,"sku":"NLS9789400728622","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400728622.jpg?v=1778458442"},{"product_id":"statistics-applied-to-clinical-studies-book-ton-j-cleophas-9789400794054","title":"Statistics Applied to Clinical Studies","description":"Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in less than seconds. This development, however fascinating, generally does not lead to simple results. We should not forget that clinical studies are, mostly, for confirming prior hypotheses based on sound arguments, and the simplest tests provide the best power and are adequate for such studies. In the past few years the authors of this 5th edition, as teachers and research supervisors in academic and top-clinical facilities, have been able to closely observe the latest developments in the field of clinical data analysis, and they have been able to assess their performance. In this 5th edition the 47 chapters of the previous edition have been maintained and upgraded according to the current state of the art, and 20 novel chapters have been added after strict selection of the most valuable and promising novel methods. The novel methods are explained using practical examples and step-by-step analyses readily accessible for non-mathematicians. All of the novel chapters have been internationally published by the authors in peer-reviewed journal, including the American Journal of Therapeutics, the European Journal of Clinical Investigation, The International journal of Clinical Pharmacology and therapeutics, and other journals, and permission is granted by all of them to use this material in the current book. We should add that the authors are well-qualified in their fields of knowledge. Professor Zwinderman is president-elect of the International Society of Biostatistics, and Professor Cleophas is past-president of the American College of Angiology. From their expertise they should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors, although from a different discipline, one clinician and one statistician, have been working and publishing together for over 10 years, and their research of statistical methodology can be characterized as a continued effort to demonstrate that statistics is not mathematics but rather a discipline at the interface of biology and mathematics. They firmly believe that any reader can benefit from this clinical approach to statistical data analysis.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":53534158291217,"sku":"NLS9789400794054","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9789400794054.jpg?v=1778541844"},{"product_id":"kernel-ridge-regression-in-clinical-research-book-ton-j-cleophas-9783031107160","title":"Kernel Ridge Regression in Clinical Research","description":"IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the         kernel trick for reduced arithmetic complexity, estimation of uncertainty by Gaussians unlike histograms, corrected data-overfit by ridge regularization, availability of 8 alternative kernel density models for datafit.    A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity \/ dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things)studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection \/ update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":53565318136081,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":53565318168849,"sku":"NIN9783031107160","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783031107160.jpg?v=1778977767"}],"url":"https:\/\/www.worldofbooks.com\/collections\/author-books-by-ton-j-cleophas.oembed?page=2","provider":"World of Books ","version":"1.0","type":"link"}