## Graduate Students Seminar

Wednesday, April 8, 2020 · 11 AM - 12 PM

Online

Session Chair: | Nadeesri Wijekoon |

Discussant: | Dr. Sinha |

###### Speaker 1: Jing Wang

**Title***Generalizing Evidence from Randomized Clinical Trials to Target Populations***Abstract**- Properly planned and conducted randomized clinical trials still remain the most accepted design for estimating the effects of interventions. However, while accurate estimates of the effect of the intervention for the trial participants can be obtained, relevant information about the effects in a particular target population might not be available, i.e. “internal validity” does not guarantee “external validity”. The inconsistency between the “internal validity” and “external validity” of those trials could be due to a lack of specification of a target population when designing the trial, difficulties with recruiting a representative sample, or the interest in considering a different target population compared to the originally designed one.
- We will provide an overview of existing design and analysis methods for assessing and enhancing the ability of a randomized trial to estimate treatment effects in a target population. And by providing a case study, more discussion will be given about one particular method, which weights the subjects in one trial to match the population on a set of observed characteristics.

###### Speaker 2: Theodore Weinberg

**Title***Introduction to the Quasi-Monte Carlo Method***Abstract**- The regular Monte Carlo method is a powerful tool for handling integrals in high dimensions. However, the convergence rate is only approximately 1/sqrt(N), where N is the number of points used. By substituting low-discrepancy deterministic sequences for the random points used in the regular Monte Carlo method, the convergence rate can be increased to nearly 1/N. We will discuss the advantages and drawbacks of this quasi-Monte Carlo method, show numerical examples, and briefly consider applications.