{"product_id":"understanding-biostatistics-9780470666364","title":"Understanding Biostatistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eUnderstanding 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Overall, the book is well-written . . . The topics are presented in a logical progression as is the level of their mathematical difficulty. Any biostatistician will find this a valuable complement to his\/her favorite biostatistics textbook.\" (Journal of Biopharmaceutical Statistics, 2012)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Statistics and medical science 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 On the nature of science 3\u003c\/p\u003e \u003cp\u003e1.3 How the scientific method uses statistics 5\u003c\/p\u003e \u003cp\u003e1.4 Finding an outcome variable to assess your hypothesis 7\u003c\/p\u003e \u003cp\u003e1.5 How we draw medical conclusions from statistical results 8\u003c\/p\u003e \u003cp\u003e1.6 A few words about probabilities 13\u003c\/p\u003e \u003cp\u003e1.7 The need for honesty: the multiplicity issue 16\u003c\/p\u003e \u003cp\u003e1.8 Prespecification and p-value history 19\u003c\/p\u003e \u003cp\u003e1.9 Adaptive designs: controlling the risks in an experiment 21\u003c\/p\u003e \u003cp\u003e1.10 The elusive concept of probability 23\u003c\/p\u003e \u003cp\u003e1.11 Comments and further reading 26\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Observational studies and the need for clinical trials 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 29\u003c\/p\u003e \u003cp\u003e2.2 Investigations of medical interventions and risk factors 29\u003c\/p\u003e \u003cp\u003e2.3 Observational studies and confounders 33\u003c\/p\u003e \u003cp\u003e2.4 The experimental study 39\u003c\/p\u003e \u003cp\u003e2.5 Population risks and individual risks 42\u003c\/p\u003e \u003cp\u003e2.6 Confounders, Simpson’s paradox and stratification 44\u003c\/p\u003e \u003cp\u003e2.7 On incidence and prevalence in epidemiology 51\u003c\/p\u003e \u003cp\u003e2.8 Comments and further reading 53\u003c\/p\u003e \u003cp\u003eReferences 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Study design and the bias issue 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 57\u003c\/p\u003e \u003cp\u003e3.2 What bias is all about 58\u003c\/p\u003e \u003cp\u003e3.3 The need for a representative sample: on selection bias 58\u003c\/p\u003e \u003cp\u003e3.4 Group comparability and randomization 61\u003c\/p\u003e \u003cp\u003e3.5 Information bias in a cohort study 65\u003c\/p\u003e \u003cp\u003e3.6 The study, or placebo, effect 68\u003c\/p\u003e \u003cp\u003e3.7 The curse of missing values 70\u003c\/p\u003e \u003cp\u003e3.8 Approaches to data analysis: avoiding self-inflicted bias 75\u003c\/p\u003e \u003cp\u003e3.9 On meta-analysis and publication bias 79\u003c\/p\u003e \u003cp\u003e3.10 Comments and further reading 81\u003c\/p\u003e \u003cp\u003eReferences 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The anatomy of a statistical test 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 85\u003c\/p\u003e \u003cp\u003e4.2 Statistical tests, medical diagnosis and Roman law 85\u003c\/p\u003e \u003cp\u003e4.3 The risks with medical diagnosis 87\u003c\/p\u003e \u003cp\u003e4.3.1 Medical diagnosis based on a single test 87\u003c\/p\u003e \u003cp\u003e4.3.2 Bayes’ theorem and the use and misuse of screening tests 89\u003c\/p\u003e \u003cp\u003e4.4 The law: a non-quantitative analogue 91\u003c\/p\u003e \u003cp\u003e4.5 Risks in statistical testing 93\u003c\/p\u003e \u003cp\u003e4.5.1 Does tonsillectomy increase the risk of Hodgkin’s lymphoma? 93\u003c\/p\u003e \u003cp\u003e4.5.2 General discussion about statistical tests 98\u003c\/p\u003e \u003cp\u003e4.6 Making statements about a binomial parameter 101\u003c\/p\u003e \u003cp\u003e4.6.1 The frequentist approach 101\u003c\/p\u003e \u003cp\u003e4.6.2 The Bayesian approach 104\u003c\/p\u003e \u003cp\u003e4.7 The bell-shaped error distribution 109\u003c\/p\u003e \u003cp\u003e4.8 Comments and further reading 112\u003c\/p\u003e \u003cp\u003eReferences 113\u003c\/p\u003e \u003cp\u003e4.A Appendix: The evolution of the central limit theorem 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Learning about parameters, and some notes on planning 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 119\u003c\/p\u003e \u003cp\u003e5.2 Test statistics described by parameters 120\u003c\/p\u003e \u003cp\u003e5.3 How we describe our knowledge about a parameter from an experiment 122\u003c\/p\u003e \u003cp\u003e5.4 Statistical analysis of two proportions 127\u003c\/p\u003e \u003cp\u003e5.4.1 Some ways to compare two proportions 127\u003c\/p\u003e \u003cp\u003e5.4.2 Analysis of the group difference 130\u003c\/p\u003e \u003cp\u003e5.5 Adjusting for confounders in the analysis 133\u003c\/p\u003e \u003cp\u003e5.6 The power curve of an experiment 138\u003c\/p\u003e \u003cp\u003e5.7 Some confusing aspects of power calculations 143\u003c\/p\u003e \u003cp\u003e5.8 Comments and further reading 145\u003c\/p\u003e \u003cp\u003eReferences 145\u003c\/p\u003e \u003cp\u003e5.A Appendix: Some technical comments 146\u003c\/p\u003e \u003cp\u003e5.A.1 The non-central hypergeometric distribution and 2 × 2 tables 146\u003c\/p\u003e \u003cp\u003e5.A.2 The gamma and χ2 distributions 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Empirical distribution functions 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 149\u003c\/p\u003e \u003cp\u003e6.2 How to describe the distribution of a sample 149\u003c\/p\u003e \u003cp\u003e6.3 Describing the sample: descriptive statistics 153\u003c\/p\u003e \u003cp\u003e6.4 Population distribution parameters 156\u003c\/p\u003e \u003cp\u003e6.5 Confidence in the CDF and its parameters 158\u003c\/p\u003e \u003cp\u003e6.6 Analysis of paired data 162\u003c\/p\u003e \u003cp\u003e6.7 Bootstrapping 163\u003c\/p\u003e \u003cp\u003e6.8 Meta-analysis and heterogeneity 166\u003c\/p\u003e \u003cp\u003e6.9 Comments and further reading 169\u003c\/p\u003e \u003cp\u003eReferences 170\u003c\/p\u003e \u003cp\u003e6.A Appendix: Some technical comments 171\u003c\/p\u003e \u003cp\u003e6.A.1 The extended family of the univariate Gaussian distributions 171\u003c\/p\u003e \u003cp\u003e6.A.2 The Wiener process and its bridge 173\u003c\/p\u003e \u003cp\u003e6.A.3 Confidence regions for the CDF and the Kolmogorov–Smirnov test 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Correlation and regression in bivariate distributions 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 177\u003c\/p\u003e \u003cp\u003e7.2 Bivariate distributions and correlation 178\u003c\/p\u003e \u003cp\u003e7.3 On baseline corrections and other covariates 183\u003c\/p\u003e \u003cp\u003e7.4 Bivariate Gaussian distributions 186\u003c\/p\u003e \u003cp\u003e7.5 Regression to the mean 189\u003c\/p\u003e \u003cp\u003e7.6 Statistical analysis of bivariate Gaussian data 195\u003c\/p\u003e \u003cp\u003e7.7 Simultaneous analysis of two binomial proportions 199\u003c\/p\u003e \u003cp\u003e7.8 Comments and further reading 203\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e7.A Appendix: Some technical comments 205\u003c\/p\u003e \u003cp\u003e7.A.1 The regression to the mode equation 205\u003c\/p\u003e \u003cp\u003e7.A.2 Analysis of data from the multivariate Gaussian distribution 206\u003c\/p\u003e \u003cp\u003e7.A.3 On the geometric approach to univariate confidence limits 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 How to compare the outcome in two groups 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 209\u003c\/p\u003e \u003cp\u003e8.2 Simple models that compare two distributions 210\u003c\/p\u003e \u003cp\u003e8.3 Comparison done the horizontal way 212\u003c\/p\u003e \u003cp\u003e8.4 Analysis done the vertical way 216\u003c\/p\u003e \u003cp\u003e8.5 Some ways to compute p-values 224\u003c\/p\u003e \u003cp\u003e8.6 The discrete Wilcoxon test 226\u003c\/p\u003e \u003cp\u003e8.7 The two-period crossover trial 229\u003c\/p\u003e \u003cp\u003e8.8 Multivariate analysis and analysis of covariance 232\u003c\/p\u003e \u003cp\u003e8.9 Comments and further reading 239\u003c\/p\u003e \u003cp\u003eReferences 240\u003c\/p\u003e \u003cp\u003e8.A Appendix: About U-statistics 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Least squares, linear models and beyond 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 245\u003c\/p\u003e \u003cp\u003e9.2 The purpose of mathematical models 246\u003c\/p\u003e \u003cp\u003e9.3 Different ways to do least squares 250\u003c\/p\u003e \u003cp\u003e9.4 Logistic regression, with variations 252\u003c\/p\u003e \u003cp\u003e9.5 The two-step modeling approach 257\u003c\/p\u003e \u003cp\u003e9.6 The effect of missing covariates 260\u003c\/p\u003e \u003cp\u003e9.7 The exponential family of distributions 263\u003c\/p\u003e \u003cp\u003e9.8 Generalized linear models 269\u003c\/p\u003e \u003cp\u003e9.9 Comments and further reading 270\u003c\/p\u003e \u003cp\u003eReferences 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Analysis of dose response 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 273\u003c\/p\u003e \u003cp\u003e10.2 Dose–response relationship 274\u003c\/p\u003e \u003cp\u003e10.3 Relative dose potency and therapeutic ratio 278\u003c\/p\u003e \u003cp\u003e10.4 Subject-specific and population averaged dose response 279\u003c\/p\u003e \u003cp\u003e10.5 Estimation of the population averaged dose–response relationship 281\u003c\/p\u003e \u003cp\u003e10.6 Estimating subject-specific dose responses 285\u003c\/p\u003e \u003cp\u003e10.7 Comments and further reading 288\u003c\/p\u003e \u003cp\u003eReferences 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Hazards and censored data 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 289\u003c\/p\u003e \u003cp\u003e11.2 Censored observations: incomplete knowledge 290\u003c\/p\u003e \u003cp\u003e11.3 Hazard models from a population perspective 291\u003c\/p\u003e \u003cp\u003e11.4 The impact of competing risks 296\u003c\/p\u003e \u003cp\u003e11.5 Heterogeneity in survival analysis 300\u003c\/p\u003e \u003cp\u003e11.6 Recurrent events and frailty 304\u003c\/p\u003e \u003cp\u003e11.7 The principles behind the analysis of censored data 306\u003c\/p\u003e \u003cp\u003e11.8 The Kaplan–Meier estimator of the CDF 309\u003c\/p\u003e \u003cp\u003e11.9 Comments and further reading 312\u003c\/p\u003e \u003cp\u003eReferences 313\u003c\/p\u003e \u003cp\u003e11.A Appendix: On the large-sample approximations of counting processes 314\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 From the log-rank test to the Cox proportional hazards model 317\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 317\u003c\/p\u003e \u003cp\u003e12.2 Comparing hazards between two groups 318\u003c\/p\u003e \u003cp\u003e12.3 Nonparametric tests for hazards 319\u003c\/p\u003e \u003cp\u003e12.4 Parameter estimation in hazard models 324\u003c\/p\u003e \u003cp\u003e12.5 The accelerated failure time model 328\u003c\/p\u003e \u003cp\u003e12.6 The Cox proportional hazards model 331\u003c\/p\u003e \u003cp\u003e12.7 On omitted covariates and stratification in the log-rank test 336\u003c\/p\u003e \u003cp\u003e12.8 Comments and further reading 338\u003c\/p\u003e \u003cp\u003eReferences 339\u003c\/p\u003e \u003cp\u003e12.A Appendix: Comments on interval-censored data 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Remarks on some estimation methods 343\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 343\u003c\/p\u003e \u003cp\u003e13.2 Estimating equations and the robust variance estimate 344\u003c\/p\u003e \u003cp\u003e13.3 From maximum likelihood theory to generalized estimating equations 351\u003c\/p\u003e \u003cp\u003e13.4 The analysis of recurrent events 355\u003c\/p\u003e \u003cp\u003e13.5 Defining and estimating mixed effects models 360\u003c\/p\u003e \u003cp\u003e13.6 Comments and further reading 366\u003c\/p\u003e \u003cp\u003eReferences 367\u003c\/p\u003e \u003cp\u003e13.A Appendix: Formulas for first-order bias 368\u003c\/p\u003e \u003cp\u003eIndex 371\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":51037068329303,"sku":"9780470666364","price":67.46,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470666364.jpg?v=1750934247","url":"https:\/\/bookcurl.com\/products\/understanding-biostatistics-9780470666364","provider":"Book Curl","version":"1.0","type":"link"}