LONG TERM RESULTS OF PAEDIATRIC ROSS PROCEDURE: A NEW METHODOLOGY OF TIME-TO-EVENT DATA META-ANALYSIS

2016 ◽  
Vol 32 (10) ◽  
pp. S170-S171
Author(s):  
W. Ben Ali ◽  
T. Ducruet ◽  
I. El-Hamamsy ◽  
D. Bouchard ◽  
N.C. Poirier
Keyword(s):  
Meta Analysis ◽  
Long Term Results ◽  
Ross Procedure ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals ODACH: A One-shot Distributed Algorithm for Cox model with Heterogeneous Multi-center Data

2021 ◽  
Author(s):  
Chongliang Luo ◽  
Rui Duan ◽  
Yong Chen
Keyword(s):  
Likelihood Function ◽  
Cox Model ◽  
Meta Analysis ◽  
Event Data ◽  
Time To Event ◽  
Hazard Functions ◽  
Patient Level ◽  
Time To Event Data ◽  
Level Data ◽  
Center Time

Objective: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm for Cox model to analyze multi-center time-to-event data without sharing patient-level information across sites, while accounting for heterogeneity across sites by allowing site-specific baseline hazard functions and feature distributions. Materials and Methods: We constructed a surrogate likelihood function to approximate the Cox log partial likelihood function which is stratified by site, using patient-level data from a single site and aggregated information from other sites. The ODAC estimator was obtained by maximizing the surrogate likelihood function. We evaluated and compare the performance of ODACH with meta-analysis by extensive numerical studies. Results: The simulation study showed that ODACH provided estimates close to the pooled estimator, which is obtained by directly analyzing patient-level data from all sites via a stratified Cox model. The relative bias was <1% across all scenarios. As a comparison, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 12% when the event rate is 1%. Conclusions: ODACH is a privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data, which allows the baseline hazard functions as well as the distribution of covariate variables to vary across sites. It provides estimates that is close to the pooled estimator and substantially outperforms the meta-analysis estimator when the event is rare. It is thus extremely suitable for studying rare events with heterogeneous baseline hazards across sites in a distributed manner.

Assessing treatment-time interaction in clinical trials with time to event data: a meta-analysis of hypertension trials

1998 ◽  
Vol 17 (24) ◽  
pp. 2883-2903 ◽  
Author(s):  
F. Boutitie ◽  
F. Gueyffier ◽  
S. J. Pocock ◽  
J. P. Boissel
Keyword(s):  
Clinical Trials ◽  
Treatment Time ◽  
Meta Analysis ◽  
Event Data ◽  
Time To Event ◽  
Time Interaction ◽  
Time To Event Data

Meta-analysis of time-to-event data: a comparison of two-stage methods

2011 ◽  
Vol 2 (3) ◽  
pp. 139-149 ◽  
Author(s):  
Mark C. Simmonds ◽  
Jayne Tierney ◽  
Jack Bowden ◽  
Julian PT Higgins
Keyword(s):  
Meta Analysis ◽  
Event Data ◽  
Time To Event ◽  
Two Stage ◽  
Time To Event Data

scholarly journals Response to: Practical methods for incorporating summary time-to-event data into meta-analysis

Trials ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 391 ◽  
Author(s):  
Yu Wang ◽  
Tingting Zeng
Keyword(s):  
Meta Analysis ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Correction to: joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Maria Sudell ◽  
Ruwanthi Kolamunnage-Dona ◽  
Catrin Tudur-Smith
Keyword(s):  
Meta Analysis ◽  
Reporting Quality ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Time To Event Data
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