scholarly journals Parametric survival models for interval-censored data with time-dependent covariates

Biostatistics ◽  
2006 ◽  
Vol 7 (4) ◽  
pp. 599-614 ◽  
Author(s):  
Y. H. Sparling
Keyword(s):  
Censored Data ◽  
Time Dependent ◽  
Survival Models ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Time Dependent Covariates ◽  
Parametric Survival

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.

scholarly journals Further Development of Flexible Parametric Models for Survival Analysis

2009 ◽  
Vol 9 (2) ◽  
pp. 265-290 ◽  
Author(s):  
Paul C. Lambert ◽  
Patrick Royston
Keyword(s):  
Cancer Survival ◽  
Relative Survival ◽  
Parametric Models ◽  
Time Dependent ◽  
Survival Models ◽  
Hazard Ratios ◽  
Time Dependent Covariates ◽  
Expected Mortality ◽  
Survival Differences ◽  
Parametric Survival

Royston and Parmar (2002, Statistics in Medicine 21: 2175–2197) developed a class of flexible parametric survival models that were programmed in Stata with the stpm command (Royston, 2001, Stata Journal 1: 1–28). In this article, we introduce a new command, stpm2, that extends the methodology. New features for stpm2 include improvement in the way time-dependent covariates are modeled, with these effects far less likely to be over parameterized; the ability to incorporate expected mortality and thus fit relative survival models; and a superior predict command that enables simple quantification of differences between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences. The ideas are illustrated through a study of breast cancer survival and incidence of hip fracture in prostate cancer patients.

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