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Webinar: On the Validity of Statistical Analyses with Privacy-Preserving Synthetic Data

Webinar: On the Validity of Statistical Analyses with Privacy-Preserving Synthetic Data

This webinar is open to all ISPOR members and non-members and is presented by Replica’s Khaled El Emam and Lucy Mosquera. They will present a brief tutorial on synthetic data generation, an overview of its privacy preserving properties, its advantages over traditional de-identification methods, and then review the results from a simulation of the validity of inference on synthetic oncology datasets. Using multiple imputation principles, they will show that logistic regression parameter estimates on synthetic data have low bias, close to nominal coverage and power, and comparable precision to the original data. These results contribute to the growing evidence that inferences from synthetic datasets are valid. The appropriate parameterizations, strengths and limitations of the approach will be discussed. All materials will be presented with relevant illustrative examples.

There is no cost to attend this event but registration is required. Registration opening soon.