scholarly journals Correction - Estimation of intervention effects using recurrent event time data in the presence of event dependence and a cured fraction

2015 ◽  
Vol 35 (16) ◽  
pp. 2847-2848
Author(s):  
Ying Xu ◽  
K. F. Lam ◽  
Yin Bun Cheung
Keyword(s):  
Recurrent Event ◽  
Time Data ◽  
Intervention Effects ◽  
Event Time ◽  
Event Dependence

Estimation of summary protective efficacy using a frailty mixture model for recurrent event time data

2012 ◽  
Vol 31 (29) ◽  
pp. 4023-4039 ◽  
Author(s):  
Ying Xu ◽  
Yin Bun Cheung ◽  
K. F. Lam ◽  
Paul Milligan
Keyword(s):  
Mixture Model ◽  
Protective Efficacy ◽  
Recurrent Event ◽  
Time Data ◽  
Event Time

Comparison of Statistical Models For Estimating Intervention Effects Based On Time-To-Recurrent-Event In Stepped Wedge Cluster Randomized Trial Using Open Cohort Design

2021 ◽  
Author(s):  
Shunsuke Oyamada ◽  
Shih-Wei Chiu ◽  
Takuhiro Yamaguchi
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Statistical Models ◽  
Randomised Trial ◽  
Recurrent Event ◽  
Cluster Randomised Trial ◽  
Stepped Wedge ◽  
Intervention Effects ◽  
Cohort Design

Abstract Background: There are currently no methodological studies on the performance of the statistical models for estimating intervention effects based on the time-to-recurrent-event (TTRE) in stepped wedge cluster randomised trial (SWCRT) using an open cohort design. This study aims to address this by evaluating the performance of these statistical models using an open cohort design with the Monte Carlo simulation in various settings and their application using an actual example.Methods: Using Monte Carlo simulations, we evaluated the performance of the existing extended Cox proportional hazard models, i.e., the Andersen-Gill (AG), Prentice-Williams-Peterson Total-Time (PWP-TT), and Prentice-Williams-Peterson Gap-time (PWP-GT) models, using the settings of several event generation models and true intervention effects, with and without stratification by clusters. Unidirectional switching in SWCRT was represented using time-dependent covariates.Results: Using Monte Carlo simulations with the various described settings, the PWP-GT model with stratification by clusters showed the best performance in most settings and reasonable performance in the others. The only situation in which the performance of the PWP-TT model with stratification by clusters was not inferior to that of the PWP-GT model with stratification by clusters was when there was a certain amount of follow-up period, and the timing of the trial entry was random within the trial period, including the follow-up period. The AG model performed well only in a specific setting. By analysing actual examples, it was found that almost all the statistical models suggested that the risk of events during the intervention condition may be somewhat higher than in the control, although the difference was not statistically significant.Conclusions: The PWP-GT model with stratification by clusters had the most reasonable performance when estimating intervention effects based on the TTRE in SWCRT in various settings using an open cohort design.

scholarly journals Vine copula based likelihood estimation of dependence patterns in multivariate event time data

2018 ◽  
Vol 117 ◽  
pp. 109-127 ◽  
Author(s):  
Nicole Barthel ◽  
Candida Geerdens ◽  
Matthias Killiches ◽  
Paul Janssen ◽  
Claudia Czado
Keyword(s):  
Likelihood Estimation ◽  
Time Data ◽  
Event Time ◽  
Vine Copula

scholarly journals Copula Link-Based Additive Models for Right-Censored Event Time Data

2019 ◽  
Vol 115 (530) ◽  
pp. 886-895 ◽  
Author(s):  
Giampiero Marra ◽  
Rosalba Radice
Keyword(s):  
Additive Models ◽  
Time Data ◽  
Event Time

Survival curve estimation for informatively coarsened discrete event-time data

2007 ◽  
Vol 26 (10) ◽  
pp. 2184-2202 ◽  
Author(s):  
Michelle Shardell ◽  
Daniel O. Scharfstein ◽  
Samuel A. Bozzette
Keyword(s):  
Survival Curve ◽  
Discrete Event ◽  
Time Data ◽  
Curve Estimation ◽  
Event Time
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