Integrating real‐world data and machine learning: A framework to assess covariate importance in real‐world use of alternative intravenous dosing regimens for atezolizumab
Bianca Vora, Ashutosh Jindal, Erick Velasquez, James Lu, Benjamin Wu · 2024
Read Integrating real‐world data and machine learning: A framework to assess covariate importance in real‐world use of alternative intravenous dosing regimens for atezolizumab by Bianca Vora, Ashutosh Jindal, Erick Velasquez, James Lu, Benjamin Wu (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11573720.txt.
Description
A peer-reviewed research article from Europe PMC (2024). The increase in the availability of real‐world data (RWD), in combination with advances in machine learning (ML) methods, provides a unique opportunity for the integration of the two to explore complex clinical pharmacology questions. Here we present a recently developed RWD/ML framework that utilizes ML algorithms to understand the influence and importance of various covariates on the use of a given dose and schedule for drugs that have multiple approved dosing regimens.
Catalog record
| CREATOR | Bianca Vora, Ashutosh Jindal, Erick Velasquez, James Lu, Benjamin Wu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |