Course
Sample Size Selection in a Clinical Trial
- 04 June 2024
- Zoom
- 6 hours
- English
- 16:00-22:00 Brussels
- 15:00-21:00 UK
- 10:00am-04:00pm New York
$780
Until 15 May 2024
$900
Until 04 Jun 2024
Lecturer: Anat Sakov
Anat is an applied biostatistician with over 30 years of experience in the industry, research, teaching and consultation. In Teva Pharmaceuticals, Anat led a group of statisticians who supported clinical trials.
Anat is a co-founder at DataSights, providing statistical consulting services to pharma, medical device (including AI-based medical devices) and medical cannabis companies. Anat has vast regulatory experience.
Anat holds a PhD in statistics and a MA in Biostatistics, both from University of California, Berkeley. During 2017-2019 Anat served as the president of the Israel Statistical Association.
Anat is a co-founder at DataSights, providing statistical consulting services to pharma, medical device (including AI-based medical devices) and medical cannabis companies. Anat has vast regulatory experience.
Anat holds a PhD in statistics and a MA in Biostatistics, both from University of California, Berkeley. During 2017-2019 Anat served as the president of the Israel Statistical Association.
A clinical trial plays a central role in the development of a drug or medical device, and statistical considerations like sample size calculations, are critical for the design of the study.
Clinical trials have different objectives, according to the stage of development. Different statistical methodology will be used for different types of endpoints. Those considerations and other impact the sample size calculations.
During this course we will learn what are the statistical considerations and principles that should be taken into account when determining the sample size of the study, while keeping in mind the business constraints.
Topics To be Covered
- General considerations in sample size calculations
- Impact of clinical and statistical parameters on the resulting sample size
- Types of endpoints
- Hypothesis testing principles including type I error and power (type II error)
- Examples from publications will be provided.