Grant Details
Grant Number: |
5R21CA198641-02 Interpret this number |
Primary Investigator: |
Zhu, Liang |
Organization: |
University Of Texas Hlth Sci Ctr Houston |
Project Title: |
New Methods to Address Dilemmas in Mixed Recurrent-Event and Panel-Count Data |
Fiscal Year: |
2017 |
Abstract
Program Description/Abstract:
This proposal addresses the development and application of new analytic strategies for three dilemmas in mixed
recurrent-event and panel-count data (mixed recurrent-event data). While recurrent-event data and panel-count data are
both generated from recurrent-event processes, they have different observation systems. In the former, subjects are
observed continuously and in the latter, subjects are observed only at discrete time points. In many single cohort
studies, subjects are observed continuously during some data collection periods and only periodically during other data
collection points, resulting in mixed recurrent-event data. Meanwhile, the recurrent-event process could be stopped by a
terminal event (e.g., menopause prevents the possibility of further pregnancies), the recurrent event could have a
nonignorable duration (e.g., each pregnancy lasts for up to 40 weeks), and/or the subjects are clustered (e.g., occurring
pregnancies for subjects from the same family will most likely not be independent so the family becomes a cluster).
The current common practice for mixed recurrent-event data is to approximate or simplify these complex data,
resulting in potentially misleading conclusions. To date, no existing statistical methods have been developed that will
allow for comprehensively assessing the three problems in this complex data structure. There is an urgent need to
develop intuitive, efficient, and computationally feasible methods for analyzing complex data in event history studies.
Our preliminary studies have efficiently and flexibly addressed basic regression analysis of mixed recurrent-event data
alone. Based on our previous work, this project proposes to use data from the renowned longitudinal Childhood Cancer
Survivor Study (CCSS) and Pediatric Brain Tumor Consortium (PBTC) to: 1) Develop semiparametric methods for
regression analysis of mixed recurrent-event data with terminal events; 2) Develop semiparametric methods for
regression analysis of mixed recurrent-event data with nonignorable duration; and, 3) Develop semiparametric methods
for regression analysis of mixed recurrent-event data with clustering subjects. In all three aims, the proposed methods
use marginal models and derive their estimating equations similarly, so we can combine the methods proposed in Aim
1, 2 and 3 to simultaneously deal with these problems should they occur within the same data set. These approaches
potentially have strong statistical and clinical relevance for the study of complex event history data.
Publications
Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study.
Authors: Zhu L.
, Choi S.
, Li Y.
, Huang X.
, Sun J.
, Robison L.L.
.
Source: Lifetime Data Analysis, 2020-07-12 00:00:00.0; , .
EPub date: 2020-07-12 00:00:00.0.
PMID: 32656612
Related Citations
An Additive-Multiplicative Mean Model for Panel Count Data with Dependent Observation and Dropout Processes.
Authors: Yu G.
, Li Y.
, Zhu L.
, Zhao H.
, Sun J.
, Robison L.L.
.
Source: Scandinavian Journal Of Statistics, Theory And Applications, 2019 Jun; 46(2), p. 414-431.
EPub date: 2018-11-20 00:00:00.0.
PMID: 31223184
Related Citations
Variable selection for recurrent event data with broken adaptive ridge regression.
Authors: Zhao H.
, Sun D.
, Li G.
, Sun J.
.
Source: The Canadian Journal Of Statistics = Revue Canadienne De Statistique, 2018 Sep; 46(3), p. 416-428.
EPub date: 2018-08-10 00:00:00.0.
PMID: 32999527
Related Citations
Joint analysis of interval-censored failure time data and panel count data.
Authors: Xu D.
, Zhao H.
, Sun J.
.
Source: Lifetime Data Analysis, 2018 Jan; 24(1), p. 94-109.
EPub date: 2017-06-12 00:00:00.0.
PMID: 28608228
Related Citations
Regression analysis of incomplete data from event history studies with the proportional rates model.
Authors: Yu G.
, Zhu L.
, Sun J.
, Robison L.L.
.
Source: Statistics And Its Interface, 2018; 11(1), p. 91-97.
PMID: 29276554
Related Citations
Regression analysis of mixed panel count data with dependent terminal events.
Authors: Yu G.
, Zhu L.
, Li Y.
, Sun J.
, Robison L.L.
.
Source: Statistics In Medicine, 2017-05-10 00:00:00.0; 36(10), p. 1669-1680.
EPub date: 2017-01-18 00:00:00.0.
PMID: 28098397
Related Citations