Marginal regression models with time-varying coefficients for recurrent event data

2011 ◽  
Vol 30 (18) ◽  
pp. 2265-2277 ◽  
Author(s):  
Liuquan Sun ◽  
Xian Zhou ◽  
Shaojun Guo
Keyword(s):  
Regression Models ◽  
Recurrent Event ◽  
Time Varying ◽  
Event Data ◽  
Recurrent Event Data ◽  
Marginal Regression ◽  
Varying Coefficients ◽  
Time Varying Coefficients

A class of additive-accelerated means regression models for recurrent event data

2010 ◽  
Vol 53 (12) ◽  
pp. 3139-3151
Author(s):  
Li Liu ◽  
XiaoYun Mu ◽  
LiuQuan Sun
Keyword(s):  
Regression Models ◽  
Recurrent Event ◽  
Event Data ◽  
Recurrent Event Data

scholarly journals Recurrent event data analysis with intermittently observed time-varying covariates

2016 ◽  
Vol 35 (18) ◽  
pp. 3049-3065 ◽  
Author(s):  
Shanshan Li ◽  
Yifei Sun ◽  
Chiung-Yu Huang ◽  
Dean A. Follmann ◽  
Richard Krause
Keyword(s):  
Data Analysis ◽  
Recurrent Event ◽  
Time Varying ◽  
Event Data ◽  
Recurrent Event Data ◽  
Time Varying Covariates ◽  
Event Data Analysis

scholarly journals Mixture cure models with time-varying and multilevel frailties for recurrent event data

2019 ◽  
Vol 29 (5) ◽  
pp. 1368-1385 ◽  
Author(s):  
Richard Tawiah ◽  
Geoffrey J McLachlan ◽  
Shu Kay Ng
Keyword(s):  
Recurrent Events ◽  
General Setting ◽  
Efficient Estimation ◽  
Recurrent Event ◽  
Time Varying ◽  
Event Data ◽  
Recurrent Event Data ◽  
Mixture Cure Model ◽  
Generalised Linear Mixed Model ◽  
Gap Times

Many medical studies yield data on recurrent clinical events from populations which consist of a proportion of cured patients in the presence of those who experience the event at several times (uncured). A frailty mixture cure model has recently been postulated for such data, with an assumption that the random subject effect (frailty) of each uncured patient is constant across successive gap times between recurrent events. We propose two new models in a more general setting, assuming a multivariate time-varying frailty with an AR(1) correlation structure for each uncured patient and addressing multilevel recurrent event data originated from multi-institutional (multi-centre) clinical trials, using extra random effect terms to adjust for institution effect and treatment-by-institution interaction. To solve the difficulties in parameter estimation due to these highly complex correlation structures, we develop an efficient estimation procedure via an EM-type algorithm based on residual maximum likelihood (REML) through the generalised linear mixed model (GLMM) methodology. Simulation studies are presented to assess the performances of the models. Data sets from a colorectal cancer study and rhDNase multi-institutional clinical trial were analyzed to exemplify the proposed models. The results demonstrate a large positive AR(1) correlation among frailties across successive gap times, indicating a constant frailty may not be realistic in some situations. Comparisons of findings with existing frailty models are discussed.

Prediction Using Partly Conditional Time-Varying Coefficients Regression Models

Biometrics ◽  
1999 ◽  
Vol 55 (3) ◽  
pp. 944-950 ◽  
Author(s):  
Margaret Sullivan Pepe ◽  
Patrick Heagerty ◽  
Robert Whitaker
Keyword(s):  
Regression Models ◽  
Time Varying ◽  
Varying Coefficients ◽  
Conditional Time ◽  
Time Varying Coefficients

MALLIAVIN CALCULUS FOR THE ESTIMATION OF TIME-VARYING REGRESSION MODELS USED IN FINANCIAL APPLICATIONS

2007 ◽  
Vol 10 (05) ◽  
pp. 771-800 ◽  
Author(s):  
AHMED ABUTALEB ◽  
MICHAEL G. PAPAIOANNOU
Keyword(s):  
Malliavin Calculus ◽  
Regression Models ◽  
Closed Form Solution ◽  
Form Solution ◽  
Regression Coefficients ◽  
Time Varying ◽  
Linear Regression Models ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Estimation Of Time

The paper introduces a new method for the estimation of time-varying regression coefficients employed in financial modeling. We use Malliavin calculus (stochastic calculus of variations) to estimate the time-varying regression coefficients that appear in linear regression models, and the generalized Clark–Ocone formula to derive a closed-form solution for the estimates of the time-varying coefficients. While this approach can be applied to any signal model, we present its application to signals modeled as a Brownian motion and an Ornstein–Uhlenbeck process. Simulation results prove the superiority of the proposed method, as compared to conventional methods.

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