Sreedevi, E P; Dr.Sankaran, P G(Cochin University Of Science And Technology, April 9, 2010)
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Abstract:
there has been much research
on analyzing various forms of competing risks data. Nevertheless, there are several
occasions in survival studies, where the existing models and methodologies are
inadequate for the analysis competing risks data. ldentifiabilty problem and various
types of and censoring induce more complications in the analysis of competing risks
data than in classical survival analysis. Parametric models are not adequate for the
analysis of competing risks data since the assumptions about the underlying lifetime
distributions may not hold well. Motivated by this, in the present study. we develop
some new inference procedures, which are completely distribution free for the
analysis of competing risks data.
Description:
Department of Statistics, Cochin University of Science and
Technology
Anisha,P; Dr.Sankaran, P G(Cochin University of Science and Technology, May 4, 2012)
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Abstract:
This thesis Entitled “modelling and analysis of recurrent event data with multiple causes.Survival data is a term used for describing data that measures the time to occurrence of an event.In survival studies, the time to occurrence of an event is generally referred to as lifetime.Recurrent event data are commonly encountered in longitudinal studies when individuals are followed to observe the repeated occurrences of certain events. In many practical situations, individuals under study are exposed to the failure due to more than one causes and the eventual failure can be attributed to exactly one of these causes.The proposed model was useful in real life situations to study the effect of covariates on recurrences of certain events due to different causes.In Chapter 3, an additive hazards model for gap time distributions of recurrent event data with multiple causes was introduced. The parameter estimation and asymptotic properties were discussed .In Chapter 4, a shared frailty model for the analysis of bivariate competing risks data was presented and the estimation procedures for shared gamma frailty model, without covariates and with covariates, using EM algorithm were discussed.
In Chapter 6, two nonparametric estimators for bivariate survivor function of paired recurrent event data were developed. The asymptotic properties of the estimators were studied. The proposed estimators were applied to a real life data set. Simulation studies were carried out to find the efficiency of the proposed estimators.
Description:
Department of Statistics,
Cochin University of Science and Technology