A New Proposal for Multivariable Modelling of Time-Varying Effects in Survival Data Based on Fractional Polynomial Time-Transformation

2007 ◽  
Vol 49 (3) ◽  
pp. 453-473 ◽  
Author(s):  
Willi Sauerbrei ◽  
Patrick Royston ◽  
Maxime Look
Keyword(s):  
Polynomial Time ◽  
Survival Data ◽  
Time Transformation ◽  
Time Varying ◽  
Fractional Polynomial ◽  
Multivariable Modelling

Variable selection for joint models with time-varying coefficients

2019 ◽  
Vol 29 (1) ◽  
pp. 309-322
Author(s):  
Yujing Xie ◽  
Zangdong He ◽  
Wanzhu Tu ◽  
Zhangsheng Yu
Keyword(s):  
Variable Selection ◽  
Survival Data ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Joint Models ◽  
Closed Form Solutions ◽  
Varying Coefficients ◽  
Shared Random Effects ◽  
Time Varying Coefficients ◽  
Time Invariant

Many clinical studies collect longitudinal and survival data concurrently. Joint models combining these two types of outcomes through shared random effects are frequently used in practical data analysis. The standard joint models assume that the coefficients for the longitudinal and survival components are time-invariant. In many applications, the assumption is overly restrictive. In this research, we extend the standard joint model to include time-varying coefficients, in both longitudinal and survival components, and we present a data-driven method for variable selection. Specifically, we use a B-spline decomposition and penalized likelihood with adaptive group LASSO to select the relevant independent variables and to distinguish the time-varying and time-invariant effects for the two model components. We use Gaussian-Legendre and Gaussian-Hermite quadratures to approximate the integrals in the absence of closed-form solutions. Simulation studies show good selection and estimation performance. Finally, we use the proposed procedure to analyze data generated by a study of primary biliary cirrhosis.

Modeling Time-Varying Effects With Large-Scale Survival Data: An Efficient Quasi-Newton Approach

2017 ◽  
Vol 26 (3) ◽  
pp. 635-645
Author(s):  
Kevin He ◽  
Yuan Yang ◽  
Yanming Li ◽  
Ji Zhu ◽  
Yi Li
Keyword(s):  
Survival Data ◽  
Large Scale ◽  
Time Varying ◽  
Quasi Newton

Analysis of multilevel grouped survival data with time-varying regression coefficients

2010 ◽  
Vol 30 (3) ◽  
pp. 250-259 ◽  
Author(s):  
May C. M. Wong ◽  
K. F. Lam ◽  
Edward C. M. Lo
Keyword(s):  
Survival Data ◽  
Regression Coefficients ◽  
Time Varying

scholarly journals Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines

Biometrics ◽  
2017 ◽  
Vol 74 (2) ◽  
pp. 685-693 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
Paul H. C. Eilers ◽  
Johanna J. M. Takkenberg ◽  
Dimitris Rizopoulos
Keyword(s):  
Survival Data ◽  
Time Varying ◽  
Joint Models ◽  
Longitudinal And Survival Data

scholarly journals Propensity score matching for treatment delay effects with observational survival data

2019 ◽  
Vol 29 (3) ◽  
pp. 695-708 ◽  
Author(s):  
Erinn M Hade ◽  
Giovanni Nattino ◽  
Heather A Frey ◽  
Bo Lu
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Survival Data ◽  
Causal Effect ◽  
Treatment Delay ◽  
Cox Proportional Hazards Model ◽  
Time Varying ◽  
Delay Effect ◽  
Delay Effects ◽  
Time Varying Covariates

In observational studies with a survival outcome, treatment initiation may be time dependent, which is likely to be affected by both time-invariant and time-varying covariates. In situations where the treatment is necessary for the study population, all or most subjects may be exposed to the treatment sooner or later. In this scenario, the causal effect of interest is the delay in treatment reception. A simple comparison of those receiving treatment early vs. those receiving treatment late might not be appropriate, as the timing of the treatment reception is not randomized. Extending Lu’s matching design with time-varying covariates, we propose a propensity score matching strategy to estimate the treatment delay effect. The goal is to balance the covariate distribution between on-time treatment and delayed treatment groups at each time point using risk set matching. Our simulation study shows that, in the presence of treatment delay effects, the matching-based analyses clearly outperform the conventional regression analysis using the naive Cox proportional hazards model. We apply this method to study the treatment delay effect of 17 alpha-hydroxyprogesterone caproate (17P) for patients with recurrent preterm birth.

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