scholarly journals Semiparametric estimation of time-varying intervention effects using recurrent event data

2017 ◽  
Vol 36 (17) ◽  
pp. 2682-2696 ◽  
Author(s):  
Jiajun Xu ◽  
K. F. Lam ◽  
Feng Chen ◽  
Paul Milligan ◽  
Yin Bun Cheung
Keyword(s):  
Semiparametric Estimation ◽  
Recurrent Event ◽  
Time Varying ◽  
Event Data ◽  
Recurrent Event Data ◽  
Intervention Effects ◽  
Estimation Of Time

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.

scholarly journals Penalized survival models for the analysis of alternating recurrent event data

Biometrics ◽  
2019 ◽  
Vol 76 (2) ◽  
pp. 448-459 ◽  
Author(s):  
Lili Wang ◽  
Kevin He ◽  
Douglas E. Schaubel
Keyword(s):  
Recurrent Event ◽  
Survival Models ◽  
Event Data ◽  
Recurrent Event Data
Sign in / Sign up

Export Citation Format

Share Document