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20162026
most citedModel-based standardization using multiple imputation

5 citations · 11 across the 7 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

stat.ME2020

Conflating marginal and conditional treatment effects: Comments on 'Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study'

Antonio Remiro-Azócar, Anna Heath, Gianluca Baio

In this commentary, we highlight the importance of: (1) carefully considering and clarifying whether a marginal or conditional treatment effect is of interest in a population-adjus…

stat.ME2020

Effect modification in anchored indirect treatment comparisons: Comments on "Matching-adjusted indirect comparisons: Application to time-to-event data"

Antonio Remiro-Azócar, Anna Heath, Gianluca Baio

This commentary regards a recent simulation study conducted by Aouni, Gaudel-Dedieu and Sebastien, evaluating the performance of different versions of matching-adjusted indirect co…

stat.ME2020

Marginalization of Regression-Adjusted Treatment Effects in Indirect Comparisons with Limited Patient-Level Data

Antonio Remiro-Azócar, Anna Heath, Gianluca Baio

Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial difference…

stat.AP2020

A Bayesian response-adaptive dose finding and comparative effectiveness trial

Anna Heath, Maryna Yaskina, Petros Pechlivanoglou +5

Aims: Combinations of treatments can offer additional benefit over the treatments individually. However, trials of these combinations are lower priority than the development of nov…

stat.AP2020

Methods for Population Adjustment with Limited Access to Individual Patient Data: A Review and Simulation Study

Antonio Remiro-Azócar, Anna Heath, Gianluca Baio

Population-adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross-trial differences in effect modifiers. Pop…