scholarly journals Flexible modelling of discrete failure time including time-varying smooth effects

2004 ◽  
Vol 23 (15) ◽  
pp. 2445-2461 ◽  
Author(s):  
G. Tutz ◽  
H. Binder
Keyword(s):  
Failure Time ◽  
Time Varying ◽  
Smooth Effects ◽  
Discrete Failure Time

scholarly journals Gray’s Time-Varying Coefficients Model for Posttransplant Survival of Pediatric Liver Transplant Recipients with a Diagnosis of Cancer

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yi Ren ◽  
Chung-Chou H. Chang ◽  
Gabriel L. Zenarosa ◽  
Heather E. Tomko ◽  
Drew Michael S. Donnell ◽  
...  
Keyword(s):  
Liver Transplant ◽  
Failure Time ◽  
Penalized Splines ◽  
Time Varying ◽  
Piecewise Constant ◽  
Varying Coefficients ◽  
Covariate Effects ◽  
Pediatric Liver ◽  
Time Varying Coefficients ◽  
Pediatric Liver Transplant

Transplantation is often the only viable treatment for pediatric patients with end-stage liver disease. Making well-informed decisions on when to proceed with transplantation requires accurate predictors of transplant survival. The standard Cox proportional hazards (PH) model assumes that covariate effects are time-invariant on right-censored failure time; however, this assumption may not always hold. Gray’s piecewise constant time-varying coefficients (PC-TVC) model offers greater flexibility to capture the temporal changes of covariate effects without losing the mathematical simplicity of Cox PH model. In the present work, we examined the Cox PH and Gray PC-TVC models on the posttransplant survival analysis of 288 pediatric liver transplant patients diagnosed with cancer. We obtained potential predictors through univariable(P<0.15)and multivariable models with forward selection(P<0.05)for the Cox PH and Gray PC-TVC models, which coincide. While the Cox PH model provided reasonable average results in estimating covariate effects on posttransplant survival, the Gray model using piecewise constant penalized splines showed more details of how those effects change over time.

A Diagnostic for Cox Regression with Discrete Failure-Time Models

Biometrics ◽  
2000 ◽  
Vol 56 (4) ◽  
pp. 996-1001 ◽  
Author(s):  
Corette B. Parker ◽  
Elizabeth R. Belong
Keyword(s):  
Failure Time ◽  
Cox Regression ◽  
Discrete Failure Time

G‐estimation of structural nested restricted mean time lost models to estimate effects of time‐varying treatments on a failure time outcome

Biometrics ◽  
2019 ◽  
Vol 76 (3) ◽  
pp. 799-810
Author(s):  
Yasuhiro Hagiwara ◽  
Tomohiro Shinozaki ◽  
Yutaka Matsuyama
Keyword(s):  
Failure Time ◽  
Time Varying ◽  
Mean Time

Modeling Multivariate Discrete Failure Time Data

Biometrics ◽  
1998 ◽  
Vol 54 (3) ◽  
pp. 1115 ◽  
Author(s):  
Joanna H. Shih
Keyword(s):  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Discrete Failure Time

Multistate Models: Accurate and Dynamic Methods to Improve Predictions of Thrombotic Risk in Patients with Cancer

2019 ◽  
Vol 119 (11) ◽  
pp. 1849-1859 ◽  
Author(s):  
Alberto Carmona-Bayonas ◽  
Paula Jimenez-Fonseca ◽  
Marcelo Garrido ◽  
Ana Custodio ◽  
Raquel Hernandez ◽  
...  
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Proportional Hazards ◽  
Failure Time ◽  
Dynamic Effect ◽  
Progression Free Survival ◽  
Time Varying ◽  
Multistate Models ◽  
Thrombotic Risk ◽  
Prognostic Effect

AbstractResearch into cancer-associated thrombosis (CAT) entails managing dynamic data that pose an analytical challenge. Thus, methods that assume proportional hazards to investigate prognosis entail a risk of misinterpreting or overlooking key traits or time-varying effects. We examined the AGAMENON registry, which collects data from 2,129 patients with advanced gastric cancer. An accelerated failure time (AFT) multistate model and flexible competing risks regression were used to scrutinize the time-varying effect of CAT, as well as to estimate how covariates dynamically predict cumulative incidence. The AFT model revealed that thrombosis shortened progression-free survival and overall survival with adjusted time ratios of 0.72 and 0.56, respectively. Nevertheless, its prognostic effect was nonproportional and disappeared over time if the subject managed to survive long enough. CAT that occurred later had a more pronounced prognostic effect. In the flexible competing risks model, multiple covariates were seen to have significant time-varying effects on the cumulative incidence of CAT (Khorana score, secondary thromboprophylaxis, high tumor burden, and cisplatin-containing regimen), whereas other predictors exerted a constant effect (signet ring cells and primary thromboprophylaxis). The model that assumes proportional hazards was incapable of capturing the effect of these covariates and predicted the cumulative incidence in a biased way. This study evinces that flexible and multistate models are a useful and innovative method to describe the dynamic effect of variables associated with CAT and should be more widely used.

Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models

2004 ◽  
Vol 58 (3) ◽  
pp. 271-295 ◽  
Author(s):  
Judith Lok ◽  
Richard Gill ◽  
Aad van der Vaart ◽  
James Robins
Keyword(s):  
Failure Time ◽  
Causal Effect ◽  
Time Varying ◽  
Time To Event
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