In the world of statistics, information can sometimes be missing in data sets, leading to a challenge in understanding how well treatments may work. Policy estimands are used to understand efficacy based on early treatment decisions. Various approaches, like reference-based imputation and delta adjustment, exist to speculate what may have happened after treatment was discontinued. However, these methods are often inconsistent, and more efficient methods are required.

In this episode, Alberto and I discuss how his new approach can handle different scenarios with missing data and can estimate policy estimands for faster results. As a 26-year veteran of the pharma statistics industry that recently completed his PhD research, Garcia brings a wealth of knowledge and experience to this topic.

So, let's dive into the details of this innovative framework for estimating policy estimands such as the following:

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