Course Description - Course runs 9:00 - 5:00 on Day 1 & Day 2 -- 9:00 - 3:00 on Day 3 (Breakfast & Lunch Included)
The first two days of this biostatistics training course will introduce and detail the basic and intermediate statistical concepts that are essential for professionals in a biological, public health or medical environment. The first day will emphasize the principles of descriptive and inferential statistical applications while the second day will focus on actual study examples, problem solving and interpretation of clinical (efficacy and adverse events) results. Throughout the biostatistics training course, participants are encouraged to ask questions and discuss examples relevant to their own work.
The following include but are not limited to topic areas to be discussed:
- Basic statistical terminology needed to effectively communicate with and understand your statistical colleagues
- The statistical essentials required to initiate a research investigation and plan a clinical trial
- Research questions in statistical terms and bias reducing techniques in planning a clinical trial
- Sample size considerations to insure accuracy of conclusions in clinical trials to determine treatment efficacy. A discussion of ethical considerations in sample size planning
- Examination of Phase I (adverse events) and dose response studies
- Discussion of statistical techniques to compare experimental approaches or treatment efficacy with a focus on superiority outcomes
- An introduction to interim and group sequential designs as well as futility analysis
The third day of course will cover more complex issues in research investigations and clinical trials. Topics will include:
- Association studies including correlation and regression analysis with clinical applications to multiple intervention strategies
- Examination of Phase II and III clinical trials analysis. Comparative studies will contrast superiority, equivalence and non -inferiority approaches to design and analysis
- Survival analysis and discussion of related techniques (hazard ratio, multivariate Cox modeling)
- Gaining information from multiple studies by meta-analysis and the challenges of combining information
Who Should Attend
This three-day biostatistics certification course is designed as an introduction to the statistical principles that form the basis for the design and analysis of research investigations in pharmaceutical and medical device studies. The focus of topics will benefit individuals within the pharmaceutical, biotech and device industries including R&D managers, medical investigators, basic and clinical research scientists, clinical research associates and those involved in regulatory affairs.
The course will concentrate on the philosophy and understanding of the statistical principles required in conducting sound scientific investigations with an interdisciplinary approach to trial design and analysis. It includes discussion of the topics one considers in the Statistical Analysis Plan (SAP). It will not simply present statistical formulae. Thus, the lectures are oriented toward professionals having little or no formal training in statistics or mathematics.
Course Agenda
First Day
Statistical Concepts and Terminology: Population, sample, nominal, ordinal, continuous data
Statistical Measures and Descriptive Statistics: Central tendency (average or mean, median, mode), dispersion measures such as range, variance, standard deviation, coefficient of variation, unbiased estimate
Graphical Techniques: Histograms, bar charts, box plots.
Distributions: Normal, t-distribution, skewed distribution
Inferential Statistics: Point and interval estimates of the mean and variance of a population. Hypothesis testing for the mean and variance of a population.
Risk Assessment: Relative risk, odds ratio, Bayes risk.
Second Day
Defining a Sound Scientific Study: Selection criteria to statistical consideration
Single Therapy Protocols: Phase I and Phase II clinical trials, sample size and analyses, simple regression technique
Comparative Studies: Defining appropriate study hypotheses, study objectives, defining efficacy measures and endpoints (response), sample size considerations, quantitative measures, analyses (continuous and discrete data), case control studie
Data Presentation: Interpretation and discussion of results from actual clinical data computer output for categorical and continuous endpoints, p-values, statistical significance, risk measures.
Third Day
Multiple Treatment Studies: Analysis of Variance (ANOVA), multiple regression Multiple Treatment Clinical Protocols: Phase III protocol sample size and comparative analyses (response and survival techniques)
Equivalence and Non-Inferiority Studies: Point and interval testing for equivalence, Non-inferiority and superiority graphical technique
Meta-Analytic Techniques: Presentation of individual patient vs. literature based meta-analyses, statistical tests of homogeneity and pooled effect size
Learning Objectives
Those completing this course will have an understanding of the concepts and statistical methods required in biological and health science research. They will be able to interpret results related to design and analysis issues as routinely presented in the scientific literature and clinical trials.
Frequently Asked Questions
Does the course cover sample size considerations for all types of clinical trials?
Yes. The course offers an overview of what the researcher needs to know to discuss sample size needs for any type of clinical trial whether it be superiority, equivalence or a non-inferiority trial.
Do the course topics apply to observational studies or is it strictly a randomized format that is discussed?
All the statistical techniques apply to observational and non-randomized studies as well. This is especially evident when discussing the concepts of prevalence, incidence, retrospective, cross sectional, and prospective and longitudinal studies.
What specific areas of multivariate statistics are discussed in the context of clinical trials?
Topics such as multiple and logistic regression, analysis of variance and Cox proportional hazards are introduced. Interpretation of the results are presented in detail. Some prediction procedures are introduced.
What are the specific topics in interim analyses?
There is an extensive section on the types of interim analyses performed with respect to p-value adjustment. A new section on Futility analysis has recently been introduced. Statistical motivation to continue or stop a study is presented. These are all part of the sequential design of trials.
