(Ebook) Statistical Thinking for Non Statisticians in Drug Regulation 3rd Edition by Richard Kay ISBN 111986738X 9781119867388

(Ebook) Statistical Thinking for Non Statisticians in Drug Regulation 3rd Edition by Richard Kay ISBN 111986738X 9781119867388

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ISBN 10: 111986738X 
ISBN 13: 9781119867388
Author: Richard Kay

STATISTICAL THINKING FOR NON-STATISTICIANS IN DRUG REGULATION

Statistical methods in the pharmaceutical industry are accepted as a key element in the design and analysis of clinical studies. Increasingly, the medical and scientific community are aligning with the regulatory authorities and recognizing that correct statistical methodology is essential as the basis for valid conclusions. In order for those correct and robust methods to be successfully employed there needs to be effective communication across disciplines at all stages of the planning, conducting, analyzing and reporting of clinical studies associated with the development and evaluation of new drugs and devices.

Statistical Thinking for Non-Statisticians in Drug Regulation provides a comprehensive in-depth guide to statistical methodology for pharmaceutical industry professionals, including physicians, investigators, medical science liaisons, clinical research scientists, medical writers, regulatory personnel, statistical programmers, senior data managers and those working in pharmacovigilance. The author’s years of experience and up-to-date familiarity with pharmaceutical regulations and statistical practice within the wider clinical community make this an essential guide for the those working in and with the industry.

The third edition of Statistical Thinking for Non-Statisticians in Drug Regulation includes:

A detailed new chapter on Estimands in line with the 2019 Addendum to ICH E9
Major new sections on topics including Combining Hierarchical Testing and Alpha Adjustment, Biosimilars, Restricted Mean Survival Time, Composite Endpoints and Cumulative Incidence Functions, Adjusting for Cross-Over in Oncology, Inverse Propensity Score Weighting, and Network Meta-Analysis
Updated coverage of many existing topics to reflect new and revised guidance from regulatory authorities and author experience
Statistical Thinking for Non-Statisticians in Drug Regulation is a valuable guide for pharmaceutical and medical device industry professionals, as well as statisticians joining the pharmaceutical industry and students and teachers of drug development.

(Ebook) Statistical Thinking for Non Statisticians in Drug Regulation 3rd Table of contents:

CHAPTER 1: Basic ideas in clinical trial design
1.1 Historical perspective
1.2 Control groups
1.3 Placebos and blinding
1.4 Randomisation
1.5 Bias and precision
1.6 Between‐ and within‐patient designs
1.7 Crossover trials
1.8 Signal, noise and evidence
1.9 Confirmatory and exploratory trials
1.10 Superiority, equivalence and non‐inferiority trials
1.11 Endpoint types
1.12 Choice of endpoint
CHAPTER 2: Sampling and inferential statistics
2.1 Sample and population
2.2 Sample statistics and population parameters
2.3 The normal distribution
2.4 Sampling and the standard error of the mean
2.5 Standard errors more generally
CHAPTER 3: Confidence intervals and p‐values
3.1 Confidence intervals for a single mean
3.2 Confidence intervals for other parameters
3.3 Hypothesis testing
CHAPTER 4: Tests for simple treatment comparisons
4.1 The unpaired t‐test
4.2 The paired t‐test
4.3 Interpreting the t‐tests
4.4 The chi‐square test for binary endpoints
4.5 Measures of treatment benefit
4.6 Fisher’s exact test
4.7 Tests for categorical and ordered categorical endpoints
4.8 Count endpoints
4.9 Extensions for multiple treatment groups
CHAPTER 5: Adjusting the analysis
5.1 Objectives for adjusted analysis
5.2 Comparing treatments for continuous endpoints
5.3 Least squares means
5.4 Evaluating the homogeneity of the treatment effect
5.5 Methods for binary and ordered categorical endpoints
5.6 Multi‐centre trials
CHAPTER 6: Regression and analysis of covariance
6.1 Adjusting for baseline factors
6.2 Simple linear regression
6.3 Multiple regression
6.4 Logistic regression for binary endpoints
6.5 Analysis of covariance for continuous outcomes
6.6 Other endpoint types
6.7 Mixed models
6.8 Regulatory aspects of the use of covariates
6.9 Baseline testing
6.10 Correlation and regression
CHAPTER 7: Intention‐to‐treat, analysis sets and missing data
7.1 The principle of intention‐to‐treat
7.2 The practice of intention‐to‐treat
7.3 Missing data
7.4 Intention‐to‐treat and time‐to‐event data
7.5 General questions and considerations
CHAPTER 8: Estimands
8.1 ICH E9 (R1)
8.2 Attributes of an estimand
8.3 Estimand strategies
8.4 Sensitivity and supplementary analyses
CHAPTER 9: Power, sample size and clinical relevance
9.1 Type I and type II errors
9.2 Power
9.3 Calculating sample size
9.4 Impact of changing the parameters
9.5 Regulatory aspects
9.6 Reporting the sample size calculation
9.7 Post hoc power
9.8 Link between p‐values and confidence intervals
9.9 Confidence intervals for clinical importance
9.10 Misinterpretation of the p‐value
9.11 Single pivotal trial and 0.05
CHAPTER 10: Multiple testing
10.1 Inflation of the type I error
10.2 How does multiplicity arise?
10.3 Regulatory and scientific view
10.4 Methods for adjustment
10.5 Avoiding adjustment
10.6 Fallback procedure
10.7 Multiple comparisons of treatments
10.8 Subgroup testing
10.9 Other aspects of multiplicity
CHAPTER 11: Non‐parametric and related methods
11.1 Assumptions underlying the t‐tests and their extensions
11.2 Homogeneity of variance
11.3 The assumption of normality
11.4 Non‐normality and transformations
11.5 Non‐parametric tests
11.6 Advantages and disadvantages of non‐parametric methods
11.7 Outliers
CHAPTER 12: Equivalence and non‐inferiority
12.1 Demonstrating similarity
12.2 Confidence intervals for equivalence
12.3 Confidence intervals for non‐inferiority
12.4 A p‐value approach
12.5 Assay sensitivity
12.6 Analysis sets
12.7 The choice of Δ
12.8 Biocreep and constancy
12.9 Sample size calculations
12.10 Switching between non‐inferiority and superiority
12.11 Biosimilars
CHAPTER 13: The analysis of survival data
13.1 Time‐to‐event data and censoring
13.2 Kaplan‐Meier curves
13.3 Treatment comparisons
13.4 The hazard ratio
13.5 Restricted mean survival time
13.6 Adjusted analyses
13.7 Independent censoring
13.8 Crossover
13.9 Composite time‐to‐event endpoints
13.10 Sample size calculations
CHAPTER 14: Interim analysis and data monitoring committees
14.1 Stopping rules for interim analysis
14.2 Stopping for efficacy and futility
14.3 Monitoring safety
14.4 Data monitoring committees
CHAPTER 15: Bayesian statistics
15.1 Introduction
15.2 Prior and posterior distributions
15.3 Bayesian inference
15.4 Case study
15.5 History and regulatory acceptance
15.6 Discussion
CHAPTER 16: Adaptive designs
16.1 What are adaptive designs?
16.2 Minimising bias
16.3 Unblinded sample size re‐estimation
16.4 Seamless phase II/III studies
16.5 Other types of adaptation
16.6 Further regulatory considerations
CHAPTER 17: Observational studies
17.1 Introduction
17.2 Guidance on design, conduct and analysis
17.3 Assessing the presence of baseline balance
17.4 Adjusting for selection bias: stratification and regression
17.5 Adjusting for selection bias: propensity scoring
17.6 Comparing methods that correct for selection bias
17.7 Inverse propensity score weighting
17.8 Case–control studies
CHAPTER 18: Meta‐analysis and network meta‐analysis
18.1 Definition
18.2 Objectives
18.3 Statistical methodology
18.4 Case study
18.5 Ensuring scientific validity
18.6 Regulatory aspects of meta‐analysis
18.7 Introduction to network meta‐analysis
18.8 Case Study
18.9 Indirect treatment comparisons
18.10 Bayesian rank analysis
CHAPTER 19: Methods for safety analysis, safety monitoring and assessment of benefit‐risk
19.1 Introduction
19.2 Routine evaluation in clinical studies
19.3 Data monitoring committees
19.4 Assessing benefit–risk
19.5 Pharmacovigilance
CHAPTER 20: Diagnosis
20.1 Introduction
20.2 Measures of diagnostic performance
20.3 Receiver operating characteristic curves
20.4 Diagnostic performance using regression models
20.5 Aspects of trial design for diagnostic agents
20.6 Assessing agreement
20.7 Companion diagnostics
CHAPTER 21: The role of statistics and statisticians
21.1 The importance of statistical thinking at the design stage
21.2 Regulatory guidelines
21.3 The statistics process
21.4 The regulatory submission
21.5 Publications and presentations

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