Jie Zhou

Available identifiers

List of research outcomes

Publication	Date of Publication	Type
A joint modeling approach for analyzing marker data in the presence of a terminal event	2023-10-09	Paper
A partially linear single index model for longitudinal data with terminal event	2022-03-21	Paper
Comparison of two frameworks for analyzing longitudinal data	2022-02-15	Paper
Adjusted log-rank test with double inverse weighting under dependent censoring	2021-11-12	Paper
Continuous time hidden Markov model for longitudinal data	2020-08-28	Paper
Joint analysis of panel count data with an informative observation process and a dependent terminal event	2018-06-20	Paper
An efficient risk estimator with external information under additive hazards model	2018-04-05	Paper
A class of Box-Cox transformation models for recurrent event data with a terminal event	2017-09-12	Paper
A general additive-multiplicative rates model for recurrent and terminal events	2016-05-04	Paper
More efficient estimators for marginal additive hazards model in case-cohort studies with multiple outcomes	2016-04-07	Paper
Profile local linear estimation of generalized semiparametric regression model for longitudinal data	2015-10-16	Paper
Nonparametric estimation of current status data with dependent censoring	2015-10-15	Paper
A class of weighted estimators for additive hazards model in case-cohort studies	2014-12-09	Paper
https://portal.mardi4nfdi.de/entity/Q2858333	2013-11-19	Paper
A Marginal Additive Rates Model for Recurrent Event Data with a Terminal Event	2013-11-07	Paper
A New Inference Approach for Joint Models of Longitudinal Data with Informative Observation and Censoring Times	2013-05-13	Paper
A class of mixed models for recurrent event data	2011-12-28	Paper
Semiparametric Transformation Models with Time-Varying Coefficients for Recurrent and Terminal Events	2011-07-19	Paper
Regression analysis of longitudinal data with time-dependent covariates in the presence of informative observation and censoring times	2011-05-23	Paper

Research outcomes over time

Doctoral students

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