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...

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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
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Abstract	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.
AbstractList	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.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. 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.
Author	Roy, Pascal Péron, Julien Giai, Joris Idlhaj, Maryam Collette, Laurence Maucort‐Boulch, Delphine Ozenne, Brice Buyse, Marc
Author_xml	– sequence: 1 givenname: Julien surname: Péron fullname: Péron, Julien email: julien.peron@chu-lyon.fr organization: European Organisation for Research and Treatment of Cancer – sequence: 2 givenname: Maryam surname: Idlhaj fullname: Idlhaj, Maryam organization: Hospices Civils de Lyon – sequence: 3 givenname: Delphine surname: Maucort‐Boulch fullname: Maucort‐Boulch, Delphine organization: Hospices Civils de Lyon – sequence: 4 givenname: Joris surname: Giai fullname: Giai, Joris organization: Hospices Civils de Lyon – sequence: 5 givenname: Pascal surname: Roy fullname: Roy, Pascal organization: Hospices Civils de Lyon – sequence: 6 givenname: Laurence surname: Collette fullname: Collette, Laurence organization: European Organisation for Research and Treatment of Cancer – sequence: 7 givenname: Marc surname: Buyse fullname: Buyse, Marc organization: Hasselt University – sequence: 8 givenname: Brice surname: Ozenne fullname: Ozenne, Brice organization: University of Copenhagen
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SubjectTerms	Bias clinical trial Clinical trials Computer Science Error correction Estimates generalized pairwise comparisons Mathematics multivariate analysis Patients Statistical analysis Statistical methods Statistics survival outcome
Title	Correcting the bias of the net benefit estimator due to right‐censored observations
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