scholarly journals A two-stage approach for dynamic prediction of time-to-event distributions

2016 ◽  
Vol 35 (13) ◽  
pp. 2167-2182 ◽  
Author(s):  
Xuelin Huang ◽  
Fangrong Yan ◽  
Jing Ning ◽  
Ziding Feng ◽  
Sangbum Choi ◽  
...  
Keyword(s):  
Time To Event ◽  
Two Stage ◽  
Dynamic Prediction

scholarly journals Phase II clinical trials with time-to-event endpoints: optimal two-stage designs with one-sample log-rank test

2013 ◽  
Vol 33 (12) ◽  
pp. 2004-2016 ◽  
Author(s):  
Minjung Kwak ◽  
Sin-Ho Jung
Keyword(s):  
Clinical Trials ◽  
Phase Ii ◽  
Rank Test ◽  
Time To Event ◽  
Two Stage ◽  
Phase Ii Clinical Trials ◽  
Log Rank Test

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 Point and interval estimation in two‐stage adaptive designs with time to event data and biomarker‐driven subpopulation selection

2020 ◽  
Vol 39 (19) ◽  
pp. 2568-2586
Author(s):  
Peter K. Kimani ◽  
Susan Todd ◽  
Lindsay A. Renfro ◽  
Ekkehard Glimm ◽  
Josephine N. Khan ◽  
...  
Keyword(s):  
Interval Estimation ◽  
Adaptive Designs ◽  
Event Data ◽  
Time To Event ◽  
Two Stage ◽  
Time To Event Data

Analysis of Time-to-Event Data Under a Two-Stage Survival Adaptive Design in Clinical Trials

2010 ◽  
Vol 20 (4) ◽  
pp. 705-719 ◽  
Author(s):  
Qingshu Lu ◽  
Siu Keung Tse ◽  
Shein-Chung Chow
Keyword(s):  
Clinical Trials ◽  
Adaptive Design ◽  
Event Data ◽  
Time To Event ◽  
Two Stage ◽  
Time To Event Data

Individual patient data meta-analysis of time-to-event outcomes: one-stage versus two-stage approaches for estimating the hazard ratio under a random effects model

2011 ◽  
Vol 2 (3) ◽  
pp. 150-162 ◽  
Author(s):  
Jack Bowden ◽  
Jayne F Tierney ◽  
Mark Simmonds ◽  
Andrew J Copas ◽  
Julian PT Higgins
Keyword(s):  
Random Effects ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Hazard Ratio ◽  
Random Effects Model ◽  
Stage Versus ◽  
Time To Event ◽  
Two Stage ◽  
One Stage

scholarly journals Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach

2020 ◽  
Vol 30 (4) ◽  
pp. 999-1014 ◽  
Author(s):  
Katya Mauff ◽  
Ewout Steyerberg ◽  
Isabella Kardys ◽  
Eric Boersma ◽  
Dimitris Rizopoulos
Keyword(s):  
Time To Event ◽  
Joint Models ◽  
Two Stage ◽  
Longitudinal Outcomes

scholarly journals Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kaci L Pickett ◽  
Krithika Suresh ◽  
Kristen R Campbell ◽  
Scott Davis ◽  
Elizabeth Juarez-Colunga
Keyword(s):  
Proportional Hazards ◽  
Prediction Models ◽  
Predictive Performance ◽  
Joint Modeling ◽  
Complex Relationship ◽  
Time To Event ◽  
Dynamic Prediction ◽  
Random Survival Forests ◽  
Survival Forests ◽  
Over Time

Abstract Background Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient’s biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture complex relationships between the predictors and survival without requiring prior specification and has been shown to have superior predictive performance. Methods We propose an alternative approach for dynamic prediction using random survival forests in a landmarking framework. With a simulation study, we compared the predictive performance of our proposed method with Cox landmarking and joint modeling in situations where the proportional hazards assumption does not hold and the longitudinal marker(s) have a complex relationship with the survival outcome. We illustrated the use of the RSF landmark approach in two clinical applications to assess the performance of various RSF model building decisions and to demonstrate its use in obtaining dynamic predictions. Results In simulation studies, RSF landmarking outperformed joint modeling and Cox landmarking when a complex relationship between the survival and longitudinal marker processes was present. It was also useful in application when there were several predictors for which the clinical relevance was unknown and multiple longitudinal biomarkers were present. Individualized dynamic predictions can be obtained from this method and the variable importance metric is useful for examining the changing predictive power of variables over time. In addition, RSF landmarking is easily implementable in standard software and using suggested specifications requires less computation time than joint modeling. Conclusions RSF landmarking is a nonparametric, machine learning alternative to current methods for obtaining dynamic predictions when there are complex or unknown relationships present. It requires little upfront decision-making and has comparable predictive performance and has preferable computational speed.

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