Statistical Thinking for Non-Statisticians in Drug Regulation - Specialist Topic Survival Data

Online Training Id: ST-FR-02



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The course objective is to enable participants to better understand statistical methodologies associated with the analysis of time-to-event endpoints in clinical studies undertaken within the regulatory environment.

The course topics subdivided by section are detailed below.

Section 1 (Level 1 Section 9)

  • Time to Event and Censoring
  • Kaplan-Meier Curves
  • Logrank Test
  • Hazard Rate and Hazard Ratio
  • Independent Censoring

Section 2 (Level 2 Section 10)

  • Recap, Level 1, Section 9
  • Proportional Hazards Assumption
  • Restricted Mean Survival Time
  • Accounting for Cross-Over
  • Cumulative Incidence Functions

Related Courses:


  1. Advanced Statistical Thinking for Non-Statisticians in Drug Regulation

    This advanced course follows on from the introductory Level 1 course and covers more advanced topics. The Level 1 course is aimed at non-statisticians working in the pharmaceutical industry and is structured to enable the understanding of statistical concepts and methods used in the design and analysis of clinical studies. The Level 2 course is aimed at the same target audience and has the same primary objective but covers a broader and more in-depth range of topics.

    The advanced course is divided into 14 sections and development follows a logical step-by-step structure. It is assumed that participants will already have completed Level 1. Each of the 14 sections concludes with a workshop-exercise to assess understanding of the material covered and a 70% mark (based on at most 2 attempts) is required on each of those exercises to receive a certificate of completion for the course.

    The course topics subdivided by section are detailed below.

    1. Stratifying the Analysis

    • Stratified Analysis for Continuous Endpoints
    • Cochran-Mantel-Haenszel Test for Binary Endpoints
    • Stratified Logrank Test for Time-to-Event Endpoints
    • Limitations and Extensions

    2. Modelling Dependence

    • Simple Linear Regression
    • Multiple Linear Regression
    • Logistic Regression
    • Proportional Hazards Regression
    • Negative Binomial Regression
    • Univariate versus Multivariate
    • Correlation

    3. Modelling for Treatment Comparisons View Demo

    • Analysis of Covariance (ANCOVA)
    • Regression Towards the Mean
    • Least Squares Mean
    • Treatment x Covariate Interactions
    • Prognostic versus Predictive
    • Modelling
      • Logistic Regression
      • Negative Binomial Regression
      • Cox Proportional Hazards Model

    4. Missing Data and Imputation

    • Avoiding Missing Data
    • Classifying Missing Data
      • Missing Completely at Random
      • Missing at Random
      • Missing Not at Random
    • Multiple Imputation
    • Pattern Mixture Models
    • MMRM for Repeated Measures

    5. Estimands View Demo

    • Estimands:
      • What are they?
      • Why are they interesting?
    • Estimand Strategies
      • Treatment Policy
      • Hypothetical
      • Composite
      • Principal Stratum
      • While On-Treatment
    • Case Study in Diabetes

    6. Multiplicity 2

    • Recap Level 1, Section 8
      • Golden Rule
      • Reporting p-Values
    • Combining Hierarchical and α-Splitting Methodologies
    • Closed Testing with Case Studies
    • Revisiting Workshop-Exercise Level 1, Section 8
    • Exploratory Analyses
    • Revisiting Interim Analyses

    7. Bayesian Statistics

    • Frequentist versus Bayesian Methodologies
    • Bayes Rule
    • Prior and Posterior Distributions
    • Case Study – Ovarian Cancer
    • Practical Applications

    8. Meta-Analysis View Demo

    • Objectives of Meta-Analysis
    • General Aspects of Conduct
    • Fixed and Random Effects
    • Meta-Analysis versus Pooling
    • Assessing Heterogeneity
    • Publication Bias and Funnel Plots

    9. Network Meta-Analysis

    • Network Meta-Analysis (NMA) for Indirect Comparisons
    • Case Study – Psoriasis
    • Network Geometry
    • Bayesian Rank Analysis
    • Surface Under the Cumulative Ranking (SUCRA) Curve
    • Indirect Comparisons and Effect Modifiers
    • NMA versus Meta-Analysis

    10. Survival Data 2

    • Recap, Level 1, Section 9
    • Proportional Hazards Assumption
    • Restricted Mean Survival Time
    • Accounting for Cross-Over
    • Cumulative Incidence Functions

    11. Observational Studies and Propensity Scoring

    • Observational Studies and Forms of Bias
    • Regulatory Acceptance
    • Real-World Evidence
    • Selection Bias in Observational Studies
    • Propensity Score Matching
    • Case Study
    • Stratification and Inverse Propensity Score Weighting

    12. Statistical Aspects of Safety Data

    • Primary Endpoint Safety
    • Safety Data at the Trial level
    • Incidence Rates
    • Tables and Graphs for Reporting
    • Data and Safety Monitoring Boards (DSMBs)
    • Signal Detection
      • Proportional Reporting Ratios
      • Bayesian Neural Networks

    13. Equivalence and Non-Inferiority 2

    • Recap, Level 1, Section 10
    • p-Values for Non-Inferiority and Equivalence
    • Assay Sensitivity
    • Sample Size – Non-Inferiority
    • Analysis Sets
    • Bioequivalence
    • Biosimilarity
    • Case Study– Neovascular AMD

    14. Adaptive Designs

    • Definition of Adaptive Designs
    • Restricted and Flexible Adaptations
    • Type I Error Control using a Combination Test
    • Operational Bias
    • Increasing Sample Size
    • Seamless Phase I/Phase II
    • Changing the Primary Endpoint
    • Enrichment

    Learning Objectives: 

    The primary course objective is to enable participants to better understand statistical methodologies used in the design and analysis of clinical studies undertaken within the regulatory environment.

    This increased understanding will help participants:

    • To communicate more effectively with clinical and non-clinical colleagues in relation to statistical methods and analysis,
    • To read, listen and follow statistical arguments used in publications and presentations,
    • To interpret the results of the statistical analysis of data more effectively,
    • To be more effective in responding to statistical discussions and questions arising in interactions with regulatory authorities

    Who Should Enroll:

    Pharmaceutical physicians, investigators, clinical research scientists, medical science liaison (MSL), medical writers, regulatory personnel, statistical programmers and senior data managers.

    The structure and content of this course has been built around a five-day Postgraduate Medical Specialty Training Programme, which is organized and run by RK Statistics in the UK, in association with the Faculty of Pharmaceutical Medicine. The topics covered are consistent with the topics within that programme.

    'Access to the 2nd edition of Richard kay's book 'Statistical Thinking for Non-Statisticians in Drug Regulation'  in eBook format will be included as part of the Advanced level package at no extra cost. The book published by Wiley is widely recognised as a leading text in the field.'

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  2. Statistical Thinking for Non-Statisticians in Drug Regulation

    This course is primarily concerned with statistical methodology for the design and analysis of clinical trials, planned and conducted within the pharmaceutical industry. Much of the methodology presented is in fact applicable on a broader basis and can be used in observational studies and in clinical trials outside of the pharmaceutical sector; nonetheless the primary context is clinical trials and pharmaceuticals. The course is aimed at non-statisticians working in the Pharmaceutical Industry and will be suitable for physicians, investigators, clinical research scientists,medical science liaison (MSL), medical writers, regulatory personnel, statistical programmers and senior data managers. Statisticians moving from other areas of application outside of pharmaceuticals may also find the course useful in that it places the methods that they are familiar with in context for their new environment.

    Course topics:

    • Basic Issues in Clinical Trial Design - View Demo
    • Sampling and Inferential Statistics
    • Confidence Intervals and p-Values
    • Intention to Treat, Analysis Sets and Missing Data
    • Diagnosis
    • Tests for Simple Treatment Comparisons
    • Power and Sample Size - View Demo
    • Multiple Testing; Problems and Solutions
    • The Analysis of Time to Event Data
    • Equivalence and Non-Inferiority - View Demo

    Learning Objectives: 

    The aim of this course is not to turn non-statisticians into statisticians. You should not expect to go away from this course and be able to ‘do’ statistics.
    The aims of the course are four-fold:

    • To help in the critical review of reports and publications
    • To help in the understanding of statistical methods used in presentations at congresses and meetings
    • To aid communication between statisticians and non-statisticians
    • To enable the more effective use of statistical arguments in general discussions and in interactions for example with investigators and regulators

    Who Should Enroll:

    Pharmaceutical physicians, investigators, clinical research scientists, medical science liaison (MSL), medical writers, regulatory personnel, statistical programmers and senior data managers.

    The structure and content of this course has been built around the two-day Postgraduate Diploma in Pharmaceutical Medicine Medical Statistics module, organized and run jointly by Cardiff University in the UK and the British Association of Pharmaceutical Physicians (BrAPP). The topics covered are consistent with the PharmaTrain Syllabus for Pharmaceutical Medicine/Medicines Development Science. 

    Our Testimonials

    “During education, we sometimes stumble upon topics that give us a ‘brain freeze’. They become something we avoid encountering at all costs. For me, biomedical statistics was one of those topics along with such ‘villains’ as the Krebs cycle and coagulation cascade. Yet Dr. R. Kay’s course, concise and comprehensible, dispelled the curse in a truly painless way. Highly recommended.”

    Migle Janeliuniene, Medical Writer at Biomapas (Lithuania).

    "I have really enjoyed the course as it refreshed my quite older knowledge and helped me be aware some of the newer insights. Good examples were included that were exactly demonstrating the different aspects in the course. Thank you!"

    Linda Rutgrink, Regional Trial Manager at Sanofi (Netherlands)

    "Thank you, Richard!
    Yes, I enjoyed the course - I signed-up for your course because it has been 10 years since I completed a Dutch-language masters in epidemiology (Dutch is a second language; I'm a native English speaker) and I hadn't actively used this knowledge for a while. I now work as a freelance medical writer and I wanted to be sure I would be able to explain and summarise study results correctly. Revisiting the concepts in English through your course was very helpful in solidifying my understanding and I feel I achieved my learning goal. Thanks again."

    Kimi Uegaki - Medical Writer (Netherlands)

    "Thank you, Richard!
    Yes, I enjoyed the course - I signed-up for your course because it has been 10 years since I completed a Dutch-language masters in epidemiology (Dutch is a second language; I'm a native English speaker) and I hadn't actively used this knowledge for a while. I now work as a freelance medical writer and I wanted to be sure I would be able to explain and summarise study results correctly. Revisiting the concepts in English through your course was very helpful in solidifying my understanding and I feel I achieved my learning goal. Thanks again."

    Kimi Uegaki - Medical Writer (Netherlands)

    "I did do most of the online course, I had only missed the 2nd half of Richards course so had all the notes. I found the online course pretty much an exact replica of what he delivers in person as all the notes and examples are the same. Clear, concise, digestible and I liked the modular format being able to take it at my own pace it made it seem more manageable. I would say it is a brilliant substitute for the face to face but not an extra/supplement to the usual course. I used his online modules as my core learning supplemented with his brilliant book if I wanted more detail/ practical examples."

    Emma Smith, Janssen Pharmaceuticals(UK)

    "Thanks, Richard. I found the course very useful. This course is unusual in using words and examples, rather than equations, to explain the implications and applications of statistical tests. It explains how to interpret, not conduct, statistical tests and the quizzes dotted throughout the course refer usefully to the same published article, which further brings the concepts to life."

    Sally Watcham (Swedish Orphan Biovitrum AB)

    "It was a great chance to attend this well designed and informative course.
    Thanks Richard Kay for your effort."

    Mariam Amin - Assistant Lecturer of Public Health at Assiut University ( Egypt)

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Dr Richard Kay PhD 

Richard Kay has spent over forty years working in medical statistics. Following his PhD work at the London School of Hygiene he spent fifteen years in academia undertaking teaching and research at the Universities of Salford, Heidelberg and Sheffield.

Richard moved out of the academic environment in 1989 to set up his own company, S-Cubed, based in Sheffield, offering statistics and data management services to the pharmaceutical industry. After a period of growth he merged his company with PAREXEL in 1997 and, while continuing to help grow the Sheffield facility, he took on the management and direction of the Biostatistics and Programming function worldwide for PAREXEL from 1999. In January, 2005 he left PAREXEL to work as an Independent Statistical Consultant and from 2009 he has headed up his own company RK Statistics Ltd offering statistical consultancy and training services to the pharmaceutical industry.

Richard is Honorary Visiting Professor at the School of Pharmacy and Pharmaceutical Medicine, Cardiff University in the UK and is a member of the Faculty of Pharmaceutical Medicine. He is also an editor of the journal Pharmaceutical Statistics.



Online Training Highlights
Duration: 3 hrs
Released On: 07/06/2021
Downloadable Resources: 
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