scholarly journals Estimating cumulative incidence rate on interval censored data in an illness-death model.

2021 ◽  
Author(s):  
Chen Qian
Keyword(s):  
Incidence Rate ◽  
Censored Data ◽  
Cumulative Incidence ◽  
Interval Censored Data ◽  
Cumulative Incidence Rate ◽  
Interval Censored

scholarly journals Imputing Partial Status and Estimating Incidence Rate in an Illness-death Model with Application to a Phase IV Cancer Trial

2020 ◽  
Author(s):  
Deo Srivast ◽  
Jianmin Pan ◽  
Chen Qian ◽  
Melissa Hudson ◽  
Shesh Rai
Keyword(s):  
Cancer Survivors ◽  
Childhood Cancer ◽  
Censored Data ◽  
Cumulative Incidence ◽  
Incidence Rates ◽  
Interval Censored Data ◽  
Regression Methods ◽  
Interval Censored ◽  
Phase Iv

Abstract BackgroundPhase IV clinical trials are designed to monitor the long-term toxic effects of drugs in cancer survivors. Evaluations to study the long-term effects of the cancer treatment are often made with cross-sectional surveys. This leads to interval censored data since the exact time of the onset of toxicity is not known. In addition to finding prognostic factors for log-term survival outcome, estimating and comparing the cumulative incidence rates for adverse outcomes of interest for interval censored data is also desired. However, the analysis of such data is further complicated by many issues, such as incomplete data, competing risks and selection bias. For example, one such study was designed by Hudson et al. to study the effect of anthracyclines exposure, received as part of treatment for childhood cancer, to cardiotoxicity. Rai et al. had utilized a parametric approach for assessing the effect of anthracycline on the cumulative incidence of cardiotoxicity but excluded the patients with missing information on the parameters used for assessing cardiotoxicity.MethodsIn this paper our focus is on imputing the missing data and then using the current status regression methods, previously described in Rai et al. for estimating and comparing cumulative incidence rates in an illness-death/failure model.ResultsWe undertook a comprehensive simulation study to evaluate the performance of our imputation approach and applied it to a Phase IV clinical trial to evaluate the effect of anthracycline exposure on long-term cardiotoxicity in childhood cancer survivors, which had missing cardiotoxicity information. ConclusionsOur simulations suggest that the results obtained by imputing the missing values using regression methods are significantly more efficient than those obtained without imputation. The proposed approach is easy to implement, and we demonstrate its usefulness by applying it to the data reported in Rai et al. and compare the results reported there to our approach that utilizes imputation.

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.

The Cox-Aalen model for left-truncated and mixed interval-censored data

Statistics ◽  
2019 ◽  
Vol 53 (5) ◽  
pp. 1152-1167 ◽  
Author(s):  
Pao-sheng Shen ◽  
Li Ning Weng
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Aalen Model ◽  
Interval Censored
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