Course Outline
The recommended duration for this course is 1 online session(s).
The most popular version of this course comprises a 3-h workshop described as follows:
1. Introduction: Scientific & Economic Context
The principles of scientific research and the links with statistical tools
- The experiment: Transition from the sample to the target population
- Representativeness and reproducibility of studies and experiments
- Analysis and interpretation of results: p-value, a scientific and economic holy grail to be demystified
- Scientific principles put to the test of funding and deadlines
- Regulatory constraints
- A long-term vision: building the scientific history of society
- Rigorous test planning
- A predictable, adapted and adequate statistical analysis of results
- Effective reporting and communication of results
2. Best Practices for Planning Experiments & Collecting Data
Clarity of the research question: type of comparison, endpoint(s), factors, etc.
Use the knowledge already acquired to streamline each test
Adequate and efficient representation of variability
- Biological variation
- Replication vs replicate
- Randomization
- Blinding
- Establish and justify the desired clinical effect
- Calculate the chances of detecting the desired effect
- Statistical power and sample size
Choosing a scientifically and economically efficient experimental structure
3. Adequate Data Analysis
Preparing the collected data
- Pre-processing and manipulation of raw data
- The treatment of extreme values “outliers”
- Handling missing data
- Impacts on power and risk of bias
The choice of a statistical data processing method
- Reflect the structure of experience
- Verify the underlying conditions of use of statistical methods
- Control and model sources of variability to maximize the chances of detecting important differences
Controlling multiplicity
- The basic principle: rationalize the analysis of results or risk reaching erroneous conclusions
- Corrective measures
- The statistical approach: effective but expensive tools
- The scientific method: “primary endpoints”, proof of mastery of the research topic
Management of experimental hazards: when everything does not go as planned...
- Adapt to unexpected events
- Anticipate potential problems
- Making the most of an experimental “failure”
4. Reporting of Studies & Results
The research protocol
- A somewhat trying but rewarding task
- A guarantee of quality for investors
The data analysis report
- A realistic assessment
- Update the advancement of the company's scientific knowledge
- Highlight rigor to better convince
- Aim for high-profile journals
- Good statistical practices: binding rules or efficiency gains?
- Find acceptable compromises
- Some possible solutions