Correcting the bias of the net benefit estimator due to right‐censored observations

Generalized pairwise comparisons (GPCs) are a statistical method used in randomized clinical trials to simultaneously analyze several prioritized outcomes. This procedure estimates the net benefit (Δ). Δ may be interpreted as the probability for a random patient in the treatment group to have a bett...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Biometrical journal Ročník 63; číslo 4; s. 893 - 906
Hlavní autoři: Péron, Julien, Idlhaj, Maryam, Maucort‐Boulch, Delphine, Giai, Joris, Roy, Pascal, Collette, Laurence, Buyse, Marc, Ozenne, Brice
Médium: Journal Article
Jazyk:angličtina
Vydáno: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.04.2021
Wiley-VCH Verlag
Témata:
Bias
clinical trial
Clinical trials
Computer Science
Error correction
Estimates
generalized pairwise comparisons
Mathematics
multivariate analysis
Patients
Statistical analysis
Statistical methods
Statistics
survival outcome
clinical trial
survival outcome
generalized pairwise comparisons
multivariate analysis
ISSN:0323-3847, 1521-4036, 1521-4036
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Generalized pairwise comparisons (GPCs) are a statistical method used in randomized clinical trials to simultaneously analyze several prioritized outcomes. This procedure estimates the net benefit (Δ). Δ may be interpreted as the probability for a random patient in the treatment group to have a better overall outcome than a random patient in the control group, minus the probability of the opposite situation. However, the presence of right censoring introduces uninformative pairs that will typically bias the estimate of Δ toward 0. We propose a correction to GPCs that estimates the contribution of each uninformative pair based on the average contribution of the informative pairs. The correction can be applied to the analysis of several prioritized outcomes. We perform a simulation study to evaluate the bias associated with this correction. When only one time‐to‐event outcome was generated, the corrected estimates were unbiased except in the presence of very heavy censoring. The correction had no effect on the power or type‐1 error of the tests based on the Δ. Finally, we illustrate the impact of the correction using data from two randomized trials. The illustrative datasets showed that the correction had limited impact when the proportion of censored observations was around 20% and was most useful when this proportion was close to 70%. Overall, we propose an estimator for the net benefit that is minimally affected by censoring under the assumption that uninformative pairs are exchangeable with informative pairs.
Bibliografie:Supporting Information A, B, and C for this article is available from the author or on the WWW under
https://doi.org/10.1002/bimj.202000001
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0323-3847
1521-4036
1521-4036
DOI:10.1002/bimj.202000001