Moving from Many to One: The TADA Method

We’re excited to share that the Target Aggregate Data Adjustment (TADA) Method is now implemented in our {TransportHealth} R package!

The TADA method uses Method of Moments for transportability analysis when you have:

• Individual-level data in the source population, and

• Aggregate data in the target population

Why TADA?

Most existing transportability analysis methods require individual patient-level data (IPD) for both the source and the target population, narrowing its applicability when only target aggregate-level data (AgD) is available.

Besides, accounting for censoring is essential to reduce bias in longitudinal data, yet AgD-based transportability methods in the presence of censoring remain underexplored. TADA was designed to address both of these challenges simultaneously.

The TADA Approach

TADA is designed as a two-stage weighting scheme to simultaneously adjust for both censoring bias and distributional imbalances of effect modifiers (EM), where the final weights are the product of the inverse probability of censoring weights and participation weights derived using the method of moments.

You can read more about TADA in our preprint.