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Impact of informative censoring in randomized trials with delayed treatment effect

Abstract Number:

2002 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Jingyi Lin (1), Yujie Zhao (2), Gregory Chen (2), Margarita DONICA (2), Larry León (2), Ludovic Trinquart (3), Sabrina Wan (2)

Institutions:

(1) Boston University, N/A, (2) Merck & Co., Inc., N/A, (3) Tufts Medical Center, N/A

Co-Author(s):

Yujie Zhao  
Merck & Co., Inc.
Gregory Chen  
Merck & Co., Inc.
Margarita DONICA  
Merck & Co., Inc.
Larry León  
Merck & Co., Inc.
Ludovic Trinquart  
Tufts Medical Center
Sabrina Wan  
Merck & Co., Inc.

First Author:

Jingyi Lin  
Boston University

Presenting Author:

Jingyi Lin  
Boston University

Abstract Text:

Time-to-event endpoints like progression-free survival and overall survival in oncology randomized trials sometimes show differential censoring patterns between study arms which can be indicative of informative censoring, depending on censoring reasons. Informative censoring can bias treatment effect estimates but few simulation studies characterized the magnitude of its impact, particularly in the context of delayed treatment effects. We use copula methods to model dependent censoring data and assess the impact of informative censoring. To improve the understanding of copula models in this context, we propose a new measure of the strength of informative censoring, the probability of events being informatively censored. To guide the choice of copula, we further propose a visual tool for examining the underlying correlation pattern. We conduct simulation studies to assess the impact of informative censoring on estimation bias for hazard ratios, as well as on empirical power of unweighted, weighted log-rank tests, and the MaxCombo test.

Keywords:

Informative Censoring|Copula|Randomized clinical trials|Generalized log-rank tests| |

Sponsors:

Biopharmaceutical Section

Tracks:

Statistical issues specific to therapeutic areas

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