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Statistical Thinking for Non Statisticians in Drug Regulation [Kõva köide]

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  • Formaat: Hardback, 296 pages, kõrgus x laius x paksus: 244x177x22 mm, kaal: 632 g, Illustrations
  • Ilmumisaeg: 07-Sep-2007
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 0470319712
  • ISBN-13: 9780470319710
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Statistical Thinking for Non Statisticians in Drug RegulationSuurem pilt
  • Formaat: Hardback, 296 pages, kõrgus x laius x paksus: 244x177x22 mm, kaal: 632 g, Illustrations
  • Ilmumisaeg: 07-Sep-2007
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 0470319712
  • ISBN-13: 9780470319710
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470319710.html
Märksõnad:
Kay, a statistics consultant to the pharmaceutical industry, explains the statistical methods employed by the pharmaceutical industry in relation to current regulatory requirements, providing insight into their interpretation and the context in which they are used. He does not seek to turn non-statisticians into statisticians, but rather aims to aid communication between statisticians and non-statisticians, and to enable the more effective use of statistical arguments within the regulatory process. The book is designed to be read through from beginning to end, rather than dipped into as a reference book. Readers are assumed to be familiar with general aspects of the drug development process, such as the phase I to phase IV framework, placebos, control groups, and basics of clinical trials. The readership for the book includes physicians, clinical research scientists, medical writers, regulatory personnel, statistical programmers, and those working in quality assurance. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)

Written by a well-known lecturer and consultant to the pharmaceutical industry, this book focuses on the pharmaceutical non-statistician working within a very strict regulatory environment. Statistical Thinking for Clinical Trials in Drug Regulation presents the concepts and statistical thinking behind medical studies with a direct connection to the regulatory environment so that readers can be clear where the statistical methodology fits in with industry requirements. Pharmaceutical-related examples are used throughout to set the information in context. As a result, this book provides a detailed overview of the statistical aspects of the design, conduct, analysis and presentation of data from clinical trials within drug regulation.

Statistical Thinking for Clinical Trials in Drug Regulation:

  • Assists pharmaceutical personnel in communicating effectively with statisticians using statistical language
  • Improves the ability to read and understand statistical methodology in papers and reports and to critically appraise that methodology
  • Helps to understand the statistical aspects of the regulatory framework better quoting extensively from regulatory guidelines issued by the EMEA (European Medicines Evaluation Agency), ICH (International Committee on Harmonization and the FDA (Food and Drug Administration)

Arvustused

"This book is a useful addition to the non-statisticians armoury and should be noted." (Journal of Mental Health, February 2009) "The information throughout this book is immediately useful ... this book should be very useful for anyone involved in the design, analysis, and judgment of clinical trial data." (Drug Information Journal, 2008)

Contents
1        Basic ideas in clinical trial design
1.1       Historical perspective
1.2  Control groups
1.3            Placebos and blinding
1.4. Randomisation
1.4.1            Unrestricted randomisation
1.4.2            Block randomisation
1.4.3            Unequal randomisation
1.4.4            Stratified randomisation
1.4.5            Central randomisation
1.4.6            Dynamic allocation and minimisation
1.4.7            Cluster randomisation 
1.5  Bias and precision
1.6            Between- and within-patient designs
1.7  Cross-over trials
1.8  Signal and noise
1.8.1            Signal
1.8.2            Noise
1.8.3            Signal-to-noise ratio
1.9            Confirmatory and exploratory trials
1.10 Superiority, equivalence and non-inferiority trials
1.11 Data types
1.12 Choice of endpoint
1.12.1            Primary variables
1.12.2            Secondary variables
1.12.3            Surrogate variables
1.12.4            Global assessment variables
1.12.5            Composite variables
1.12.6            Categorisation
2       Sampling and inferential statistics
2.1  Sample and population
2.2  Sample statistics and population parameters
2.2.1            Sample and population distributions
 2.2.2            Median and mean
2.2.3            Standard deviation
2.2.4            Notation
2.3  The normal distribution
2.4            Sampling and the standard error of the mean
2.5            Standard errors more generally
2.5.1            The standard error for the difference between two means
2.5.2.             Standard errors for proportions
2.5.3.            The general setting
3        Confidence intervals and p-values
3.1            Confidence interval for a single mean
3.1.1            The 95 per cent confidence interval
3.1.2            Changing the confidence coefficient
3.1.3            Changing the multiplying constant
3.1.4            The role of the standard error
3.2. Confidence intervals for other parameters
3.2.1            Difference between two means
3.2.2            Confidence intervals for proportions
3.2.3            General case
3.3            Hypothesis testing
3.3.1            Interpreting the p-value
3.3.2            Calculating the p-value
3.3.3            A common process
3.3.4            The language of statistical significance
3.3.5            One-tailed and two-tailed tests
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 data
4.4.1            Pearson chi-square
4.4.2            The link to a signal-to-noise ratio
4.5            Measures of treatment benefit
4.5.1            Odds ratio (OR)
4.5.2            Relative risk (RR)
4.5.3            Relative risk reduction (RRR)
4.5.4            Number needed to treat (NNT)
4.5.5            Confidence intervals
4.5.6            Interpretation
4.6 Fisher’s exact test
4.7 The chi-square tests for categorical and ordinal data
4.7.1            Categorical data
4.7.2            Ordered categorical (ordinal) data
4.7.3            Measures of treatment benefit for categorical and ordinal data
4.8            Extensions for multiple treatment groups
4.8.1            Between-patient designs and continuous data
4.8.2            Within-patient designs and continuous data
4.8.3            Binary, categorical and ordinal data
4.8.4            Dose ranging studies
4.8.5            Further discussion
5     Multi-centre trials
5.1            Rationale for multi-centre trials
5.2            Comparing treatments for continuous data
5.3            Evaluating the homogeneity of treatment effect
5.3.1            Treatment-by-centre interactions
5.3.2            Quantitative and qualitative interactions
5.4            Methods for binary, categorical and ordinal data
5.5            Combining centres
6       Adjusted analyses and analysis of covariance
6.1. Adjusting for baseline factors
6.2. Simple linear regression
*6.3            Multiple regression
6.4  Logistic regression
6.5            Analysis of covariance for continuous data
6.5.1            Main effect of treatment
6.5.2            Treatment-by-covariate interactions
*6.5.3            A single model
6.5.4            Connection with adjusted analyses
6.5.5            Advantages of analysis of covariance
6.6  Binary, categorical and ordinal data
6.7            Regulatory aspects of the use of covariates
*6.8            Connection between ANOVA and ANCOVA
6.9            Baseline testing
7       Intention-to-treat and analysis sets
7.1  The principle of intention-to-treat
7.2  The practice of intention-to-treat
7.2.1            Full analysis set
7.2.2            Per-protocol set
7.2.3            Sensitivity
7.3  Missing data
7.3.1            Introduction
7.3.2            Complete cases analysis
7.3.3            Last observation carried forward (LOCF)
7.3.4            Success/failure classification
7.3.5            Worst case/best case imputation
7.3.6            Sensitivity
7.3.7            Avoidance of missing data
7.4            Intention-to-treat and time-to-event data
7.5  General questions and considerations
8     Power and sample size
8.1  Type I and type II errors
8.2  Power
8.3            Calculating sample size
8.4  Impact of changing the parameters
8.4.1            Standard deviation
8.4.2            Event rate in the control group
8.4.3            Clinically relevant difference
8.5            Regulatory aspects
8.5.1            Power > 80 per cent?
8.5.2            Powering on the per-protocol set?
8.5.3            Sample size adjustment
8.6            Reporting the sample size calculation
9       Statistical significance and clinical importance
9.1  The link between p-values and confidence intervals
9.2       Confidence intervals for clinical importance
9.3       Misinterpretation of the p-value
9.3.1       Conclusions of similarity
9.3.2       The problem with 0.05
10    Multiple testing
10.1     Inflation of the type I error
10.2            How does multiplicity arise?
10.3            Regulatory view
10.4              Multiple primary endpoints
10.4.1            Avoiding adjustment
10.4.2            Significance needed on all endpoints
10.4.3            Composite endpoints
10.4.4            Variables ranked according to clinical importance
10.5  Methods for adjustment
10.6  Multiple comparisons
10.7  Repeated evaluation over time
10.8  Subgroup testing
10.9  Other areas for multiplicity
10.9.1            Using different statistical tests
10.9.2            Different analysis sets
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              Transformations
11.5              Non-parametric tests
11.5.1            The Mann–Whitney U-test
11.5.2            The Wilcoxon signed rank test
11.5.3            General comments
11.6. Advantages and disadvantages of non-parametric methods
11.7. Outliers
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 D
12.7.1            Bioequivalence
12.7.2            Therapeutic equivalence
12.7.3            Non-inferiority
12.7.4            The 10 per cent rule for cure rates
12.7.5            Biocreep and constancy
12.8              Sample size calculations
12.9              Switching between non-inferiority and superiority
13    The analysis of survival data
13.1              Time-to-event data and censoring
13.2  Kaplan–Meier (KM) curves
13.2.1            Plotting KM curves
13.2.2            Event rates and relative risk
13.2.3            Median event times
13.3  Treatment comparisons
13.4  The hazard ratio
13.4.1            Hazard rate
13.4.2            Constant hazard ratio
13.4.3            Non-constant hazard ratio
13.4.4            Link to survival curves
*13.4.5  Calculating KM curves
*13.            5  Adjusted analyses
13.5.1            Stratified methods
13.5.2            Proportional hazards regression
13.5.3            Accelerated failure time model
13.6              Independent censoring
13.7  Sample size calculations
14    Interim analysis and data monitoring committees
14.1              Stopping rules for interim analysis
14.2  Stopping for efficacy and futility
14.2.1            Efficacy
14.2.2            Futility and conditional power
14.2.3            Some practical issues
14.2.4            Analyses following completion of recruitment
14.3  Monitoring safety
14.4  Data monitoring committees
14.4.1            Introduction and responsibilities
14.4.2            Structure
14.4.3            Meetings and recommendations
14.5  Adaptive designs
14.5.1            Sample size re-evaluation
14.5.2            Flexible designs
15    Meta-analysis
15.1              Definition
15.2  Objectives
15.3  Statistical methodology
15.3.1            Methods for combination
15.3.2            Confidence intervals
15.3.3            Fixed and random effects
15.3.4            Graphical methods
15.3.5            Detecting heterogeneity
15.3.6            Robustness
15.4  Ensuring scientific validity
15.4.1 Planning
15.4.2 Publication bias and funnel plots
15.5  Meta-analysis in a regulatory setting
15.5.1            Retrospective analyses
15.5.2            One pivotal study
16    The role of statistics and statisticians
16.1              The importance of statistical thinking at the design stage
16.2              Regulatory guidelines
16.3              The statistics process
16.3.1            The statistical methods section of the protocol
16.3.2            The statistical analysis plan
16.3.3            The data validation plan
16.3.4            The blind review
16.3.5            Statistical analysis
16.3.6            Reporting the analysis
16.3.7            Pre-planning
16.3.8            Sensitivity and robustness
16.4              The regulatory submission
16.5              Publications and presentations


Richard Kay. PhD Medical Statistics. The author worked within academia until 1989 when he set up his own company offering statistics and data management services to the pharmaceutical industry. Since 2005, he works as an Independent Statistical Consultant providing full consultancy and training course service to the Pharmaceutical and Medical Device industries. The author currently lectures and runs courses for the pharmaceutical industry in all major European countries: UK, France, Ireland, Belgium, The Netherlands, Germany, Spain, Italy, Switzerland, Sweden, Denmark, Russia and South Africa and US. His lectures are predominantly given within Pharma Cos or CROs.