Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A Distinguished Speaker Series Seminar by Miguel Hernán, Dr.P.H.

This seminar will describe a framework for causal inference from real world data. The framework has two steps: 1) specification of the (hypothetical) target trial that would answer the causal question of interest, and 2) emulation of the target trial using the available data. The methods will be presented in the context of the evaluation of the comparative effectiveness of health interventions using databases of administrative and clinical data.

The seminar will consist of three hours of lecture. Each hour will include a mix of examples, methods, and discussion, including responding to your questions. The first hour introduces and motivates the advantages of the framework using well known examples from the clinical and epidemiological literature. The second hour describes the importance of the specification of treatment strategies and the determination of time zero of the target trial. Mishandling of time zero is arguably the primary reason why causal inferences from observational data are incorrect. The third hour gets into ”immortal time,” one of the most prevalent errors in the analysis of observational data, and explains how explicit target trial emulation ensures that it will not happen.

Who should attend: Researchers and decision makers interested in the use of real-world data to assess comparative effectiveness and safety. You are expected to have working experience in the analysis of large databases in academic, industry, or other research settings, but especially in the health sciences. You should also have had prior introductory courses on study design and data analysis.