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Statistical Thinking for Non-Statisticians in Drug Regulation

Statistical Thinking for Non-Statisticians in Drug Regulation

Autorzy
Wydawnictwo Wiley & Sons
Data wydania
Liczba stron 368
Forma publikacji książka w twardej oprawie
Język angielski
ISBN 9781118470947
Kategorie Farmakologia
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Opis książki

Statistical Thinking for Non-Statisticians in Drug Regulation, Second Edition, is a need-to-know guide to understanding statistical methodology, statistical data and results within drug development and clinical trials.It provides non-statisticians working in the pharmaceutical and medical device industries with an accessible introduction to the knowledge they need when working with statistical information and communicating with statisticians. It covers the statistical aspects of design, conduct, analysis and presentation of data from clinical trials in drug regulation and improves the ability to read, understand and critically appraise statistical methodology in papers and reports. As such, it is directly concerned with the day-to-day practice and the regulatory requirements of drug development and clinical trials.Fully conversant with current regulatory requirements, this second edition includes five new chapters covering Bayesian statistics, adaptive designs, observational studies, methods for safety analysis and monitoring and statistics for diagnosis.Authored by a respected lecturer and consultant to the pharmaceutical industry, Statistical Thinking for Non-Statisticians in Drug Regulation is an ideal guide for physicians, clinical research scientists, managers and associates, data managers, medical writers, regulatory personnel and for all non-statisticians working and learning within the pharmaceutical industry.

Statistical Thinking for Non-Statisticians in Drug Regulation

Spis treści

Preface to the second edition xvPreface to the first edition xviiAbbreviations xxi1 Basic ideas in clinical trial design 11.1 Historical perspective 11.2 Control groups 21.3 Placebos and blinding 31.4 Randomisation 31.5 Bias and precision 91.6 Between- and within-patient designs 111.7 Crossover trials 121.8 Signal noise and evidence 131.9 Confirmatory and exploratory trials 151.10 Superiority equivalence and non-inferiority trials 161.11 Data and endpoint types 171.12 Choice of endpoint 182 Sampling and inferential statistics 232.1 Sample and population 232.2 Sample statistics and population parameters 242.3 The normal distribution 282.4 Sampling and the standard error of the mean 312.5 Standard errors more generally 343 Confidence intervals and p-values 383.1 Confidence intervals for a single mean 383.2 Confidence interval for other parameters 423.3 Hypothesis testing 454 Tests for simple treatment comparisons 564.1 The unpaired t-test 564.2 The paired t-test 574.3 Interpreting the t-tests 604.4 The chi-square test for binary data 614.5 Measures of treatment benefit 644.6 Fisher's exact test 694.7 Tests for categorical and ordinal data 714.8 Extensions for multiple treatment groups 755 Adjusting the analysis 785.1 Objectives for adjusted analysis 785.2 Comparing treatments for continuous data 785.3 Least squares means 825.4 Evaluating the homogeneity of the treatment effect 835.5 Methods for binary categorical and ordinal data 865.6 Multi-centre trials 876 Regression and analysis of covariance 896.1 Adjusting for baseline factors 896.2 Simple linear regression 896.3 Multiple regression 916.4 Logistic regression 946.5 Analysis of covariance for continuous data 946.6 Binary categorical and ordinal data 1016.7 Regulatory aspects of the use of covariates 1036.8 Baseline testing 1057 Intention-to-treat and analysis sets 1077.1 The principle of intention-to-treat 1077.2 The practice of intention-to-treat 1107.3 Missing data 1137.4 Intention-to-treat and time-to-event data 1187.5 General questions and considerations 1208 Power and sample size 1238.1 Type I and type II errors 1238.2 Power 1248.3 Calculating sample size 1278.4 Impact of changing the parameters 1308.5 Regulatory aspects 1328.6 Reporting the sample size calculation 1349 Statistical significance and clinical importance 1369.1 Link between p-values and Confidence intervals 1369.2 Confidence intervals for clinical importance 1379.3 Misinterpretation of the p-value 1399.4 Single pivotal trial and 0.05 14010 Multiple testing 14210.1 Inflation of the type I error 14210.2 How does multiplicity arise? 14310.3 Regulatory view 14410.4 Multiple primary endpoints 14510.5 Methods for adjustment 14910.6 Multiple comparisons 15210.7 Repeated evaluation over time 15310.8 Subgroup testing 15410.9 Other areas for multiplicity 15611 Non-parametric and related methods 15811.1 Assumptions underlying the t-tests and their extensions 15811.2 Homogeneity of variance 15811.3 The assumption of normality 15911.4 Non-normality and transformations 16111.5 Non-parametric tests 16411.6 Advantages and disadvantages of non-parametric methods 16811.7 Outliers 16912 Equivalence and non-inferiority 17012.1 Demonstrating similarity 17012.2 Confidence intervals for equivalence 17212.3 Confidence intervals for non-inferiority 17312.4 A p-value approach 17412.5 Assay sensitivity 17612.6 Analysis sets 17812.7 The choice of " 17912.8 Biocreep and constancy 18412.9 Sample size calculations 18412.10 Switching between non-inferiority and superiority 18613 The analysis of survival data 18913.1 Time-to-event data and censoring 18913.2 Kaplan-Meier curves 19013.3 Treatment comparisons 19313.4 The hazard ratio 19613.5 Adjusted analyses 19913.6 Independent censoring 20213.7 Sample size calculations 20314 Interim analysis and data monitoring committees 20514.1 Stopping rules for interim analysis 20514.2 Stopping for efficacy and futility 20614.3 Monitoring safety 21014.4 Data monitoring committees 21115 Bayesian statistics 21515.1 Introduction 21515.2 Prior and posterior distributions 21515.3 Bayesian inference 21915.4 Case study 22115.5 History and regulatory acceptance 22215.6 Discussion 22416 Adaptive designs 22516.1 What are adaptive designs? 22516.2 Minimising bias 22816.3 Unblinded sample size re-estimation 23216.4 Seamless phase II/III studies 23416.5 Other types of adaptation 23616.6 Further regulatory considerations 23817 Observational studies 24117.1 Introduction 24117.2 Guidance on design conduct and analysis 24717.3 Evaluating and adjusting for selection bias 24917.4 Case-control studies 25718 Meta-analysis 26118.1 Definition 26118.2 Objectives 26318.3 Statistical methodology 26418.4 Case study 27018.5 Ensuring scientific validity 27118.6 Further regulatory aspects 27519 Methods for the safety analysis and safety monitoring 27719.1 Introduction 27719.2 Routine evaluation in clinical studies 27919.3 Data monitoring committees 28919.4 Assessing benefit-risk 29019.5 Pharmacovigilance 29920 Diagnosis 30420.1 Introduction 30420.2 Measures of diagnostic performance 30420.3 Receiver operating characteristic curves 30820.4 Diagnostic performance using regression models 31020.5 Aspects of trial design for diagnostic agents 31220.6 Assessing agreement 31321 The role of statistics and statisticians 31621.1 The importance of statistical thinking at the design stage 31621.2 Regulatory guidelines 31721.3 The statistics process 32121.4 The regulatory submission 32721.5 Publications and presentations 328References 331Index 339

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