Treatment Comparisons in Adaptive Platform Trials Adjusting for Temporal Drift

Beibei Guo ¹, Li Wang ², Ying Yuan ³

⁰Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA 70803, USA.

Statistics in Biopharmaceutical Research +

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August 26, 2024

View abstract on PubMed

Summary

Adaptive platform trials (APTs) can now account for temporal drift using two new propensity score weighting methods. These approaches ensure valid treatment comparisons even with unmeasured confounders, improving clinical trial reliability.

Area Of Science

Biostatistics
Clinical Trial Design
Causal Inference

Background

Adaptive platform trials (APTs) allow dynamic entry/exit of treatment arms.
Non-concurrent controls and temporal drift pose challenges in long-term APTs.
Adjusting for temporal changes is crucial for valid treatment comparisons in APTs.

Purpose Of The Study

To propose novel causal inference methods for treatment comparisons in APTs.
To develop approaches that specifically address temporal drift and unmeasured confounders.
To evaluate the performance of proposed methods via simulation.

Main Methods

Propensity score weighting methods are utilized for treatment effect estimation.
Two distinct approaches are proposed, one being doubly robust.
The doubly robust method ensures validity if either propensity score or time effect models are correct.

Main Results

Proposed methods demonstrate well-controlled Type I error rates.
High statistical power is achieved in simulations, even with unmeasured confounders.
The approaches effectively adjust for temporal drift in APTs.

Conclusions

The proposed propensity score weighting methods offer robust solutions for treatment comparisons in APTs.
These methods enhance the validity and reliability of findings from long-duration adaptive trials.
The doubly robust approach provides an added layer of protection against model misspecification.

Keywords:

Temporal drift adaptive platform trial concurrent controls non-concurrent controls propensity score