Real world data typically has a lot of missing information. This often requires imputation of missing values which is not ideal. Staying ahead of the curve in clinical development often requires innovative, out-of-the-box thinking and complex Bayesian methods. Such methods allow for incorporation of prior knowledge without the need to impute missing data. Join Pantelis Vlachos as he discusses information borrowing in a Bayesian framework to form meta-analytic priors in a way that accounts for between-trial heterogeneity. Register here.
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