Inference for Survival Models with Doubly Interval Censored Data and Fixed or Time Dependent Covariate

2019 ◽  
pp. 187-203
Author(s):  
Kaveh Kiani ◽  
Jayanthi Arasan
Keyword(s):  
Censored Data ◽  
Time Dependent ◽  
Survival Models ◽  
Interval Censored Data ◽  
Interval Censored

Estimating trends in the incidence rate with interval censored data and time-dependent covariates

2019 ◽  
Vol 29 (1) ◽  
pp. 272-281 ◽  
Author(s):  
Alain Vandormael ◽  
Frank Tanser ◽  
Diego Cuadros ◽  
Adrian Dobra
Keyword(s):  
Multiple Imputation ◽  
Incidence Rate ◽  
Censored Data ◽  
Transformation Model ◽  
Time Dependent ◽  
Cumulative Distribution ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Time Dependent Covariates ◽  
Imputation Approach

We propose a multiple imputation method for estimating the incidence rate with interval censored data and time-dependent (and/or time-independent) covariates. The method has two stages. First, we use a semi-parametric G-transformation model to estimate the cumulative baseline hazard function and the effects of the time-dependent (and/or time-independent covariates) on the interval censored infection times. Second, we derive the participant's unique cumulative distribution function and impute infection times conditional on the covariate values. To assess performance, we simulated infection times from a Cox proportional hazards model and induced interval censoring by varying the testing rate, e.g., participants test 100%, 75%, 50% of the time, etc. We then compared the incidence rate estimates from our G-imputation approach with single random-point and mid-point imputation. By comparison, our G-imputation approach gave more accurate incidence rate estimates and appropriate standard errors for models with time-independent covariates only, time-dependent covariates only, and a mixture of time-dependent and time-independent covariates across various testing rates. We demonstrate, for the first time, a multiple imputation approach for incidence rate estimation with interval censored data and time-dependent (and/or time-independent) covariates.

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