Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

ABSTRACT:

Background

Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships.

Methods

We present a multi-step data generating process with customizable options and flexible modules to support a var

SUBMITTER: Davis SE 

PROVIDER: S-EPMC10088292 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature