A unified approach to analyze interval-censored data based on a class of semiparametric random effects transformation models
Grant Data
Project Title
A unified approach to analyze interval-censored data based on a class of semiparametric random effects transformation models
Principal Investigator
Dr Lam, Kwok Fai
(Principal Investigator (PI))
Duration
24
Start Date
2019-09-01
Amount
218806
Conference Title
A unified approach to analyze interval-censored data based on a class of semiparametric random effects transformation models
Presentation Title
Keywords
Asymptotic properties, Cure model, Interval censored, Sieve maximum likelihood, Transformation model
Discipline
Probability & Statistics,Population Health
Panel
Physical Sciences (P)
HKU Project Code
17305819
Grant Type
General Research Fund (GRF)
Funding Year
2019
Status
Completed
Objectives
1 Estimation of the cumulative intensity function and hence the transformation parameter in the basic semiparametric transformation model based on interval censored data. 2 To derive the asymptotic properties of the proposed estimator. 3 Extension of the basic semiparametric transformation model to include the partially linear component in the regression part. 4 Extension of the basic model to accommodate clustered interval censored data based on a positive stable or a gamma frailty distribution. 5 Extension of the basic model to accommodate interval censored data with a cure group, both in the univariate and clustered cases. 6 A unified approach to the estimation of the frailty semiparametric transformation model will be considered and its asymptotic properties will be derived. 7 Finite sample performance of the proposed estimators will be studied by simulation.