S41.3: Modelling time-varying effects in survival data

2004 ◽  
Vol 46 (S1) ◽  
pp. 89-89
Author(s):  
Willi Sauerbrei ◽  
Patrick Royston ◽  
Norbert Holländer
Keyword(s):  
Survival Data ◽  
Time Varying

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.

scholarly journals Tree-based identification of subgroups for time-varying covariate survival data

2012 ◽  
Vol 25 (1) ◽  
pp. 488-501 ◽  
Author(s):  
Marnie Bertolet ◽  
Maria M Brooks ◽  
Vera Bittner
Keyword(s):  
Survival Data ◽  
Time Varying

Survival Analysis with Time-varying Relative Risks: A Tree-Based Approach

2001 ◽  
Vol 40 (02) ◽  
pp. 141-147 ◽  
Author(s):  
S. Adak ◽  
R. Xu
Keyword(s):  
Survival Data ◽  
Patient Characteristics ◽  
Type Model ◽  
Time Varying ◽  
Censored Survival Data ◽  
Relative Risks ◽  
Regression Parameters ◽  
Score Statistics ◽  
Parsimonious Models ◽  
Piecewise Constant Model

AbstractA tree-based method for estimating time-varying effects of baseline patient characteristics on survival is introduced. A Cox-type model for censored survival data is used in which the time-varying relative risks are modelled as piecewise constants.The tree method consists of three steps: 1. Growing the tree, in which a fast algorithm using maximized score statistics is utilized to determine the optimal change points; 2. A pruning algorithm is applied to obtain more parsimonious models; 3. Selection of a final tree, which may be either via bootstrap resampling or based on a measure of explained variation.The piecewise constant model is more suitable for clinical interpretation of the regression parameters than the more continuously time-varying models (spline, loess, etc.) that have been proposed previously.

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