Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment
Roberto Gomeni, F. Bressolle-Gomeni · 2024
Read Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment by Roberto Gomeni, F. Bressolle-Gomeni (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-11631816.txt.
Description
In depression trials, the measured treatment effect mixes the drug's real action with non-specific responses, the placebo effect patients feel from care itself. Standard analysis ignores that mix, which can inflate false positives or false negatives when many patients show strong or weak non-specific responses. The two authors build a model-informed framework that separates the two, blending data-driven models, nonlinear mixed effects, artificial intelligence, and propensity score weighting.
The weighting approach controls the confounding pull of non-specific response, sharpening signal detection and giving a cleaner estimate of the true treatment effect. The authors also explore how covariates, including dose-response links, shape outcomes in pooled data from two randomized trials. They present the method as a guard against false negative results driven by very high non-specific response.
Catalog record
| CREATOR | Roberto Gomeni, F. Bressolle-Gomeni |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 48 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |