Between-group comparison of area under the curve in clinical trials with censored follow-up: Application to HIV therapeutic vaccines
ALEXANDRE, Marie
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
PRAGUE, Melanie
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
ALEXANDRE, Marie
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
PRAGUE, Melanie
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
< Réduire
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Langue
EN
Article de revue
Ce document a été publié dans
Statistical Methods in Medical Research. 2021-07-04, vol. 30, n° 9
Résumé en anglais
In clinical trials, longitudinal data are commonly analyzed and compared between groups using a single summary statistic such as area under the outcome versus time curve (AUC). However, incomplete data, arising from censoring ...Lire la suite >
In clinical trials, longitudinal data are commonly analyzed and compared between groups using a single summary statistic such as area under the outcome versus time curve (AUC). However, incomplete data, arising from censoring due to a limit of detection or missing data, can bias these analyses. In this article, we present a statistical test based on splines-based mixed-model accounting for both the censoring and missingness mechanisms in the AUC estimation. Inferential properties of the proposed method were evaluated and compared to ad hoc approaches and to a non-parametric method through a simulation study based on two-armed trial where trajectories and the proportion of missing data were varied. Simulation results highlight that our approach has significant advantages over the other methods. A real working example from two HIV therapeutic vaccine trials is presented to illustrate the applicability of our approach.< Réduire
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