Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
James McVittie ◽  
David Wolfson ◽  
David Stephens ◽  
Vittorio Addona ◽  
David Buckeridge
Keyword(s):  
Cohort Study ◽  
Time Distribution ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Failure Time Distribution ◽  
Prevalent Cohort ◽  
Combining Data ◽  
Incident Cohort

AbstractA classical problem in survival analysis is to estimate the failure time distribution from right-censored observations obtained from an incident cohort study. Frequently, however, failure time data comprise two independent samples, one from an incident cohort study and the other from a prevalent cohort study with follow-up, which is known to produce length-biased observed failure times. There are drawbacks to each of these two types of study when viewed separately. We address two main questions here: (i) Can our statistical inference be enhanced by combining data from an incident cohort study with data from a prevalent cohort study with follow-up? (ii) What statistical methods are appropriate for these combined data? The theory we develop to address these questions is based on a parametrically defined failure time distribution and is supported by simulations. We apply our methods to estimate the duration of hospital stays.

scholarly journals Analytical Strategies for Failure Time Data with a Cured Fraction

2019 ◽  
Author(s):  
Sheng-li An ◽  
Fuqiang Huang ◽  
Pei Kang ◽  
Yingxin Liu
Keyword(s):  
Proportional Hazards ◽  
Failure Time ◽  
Eastern Cooperative Oncology Group ◽  
Phase Iii ◽  
Failure Time Data ◽  
Time Data ◽  
Aft Model ◽  
Time To Event Data ◽  
Analytical Strategies

Abstract Some failure time data come from a population that consists of some subjects who are susceptible to and others who are non-susceptible to the event of interest. The data typically have heavy censoring at the end of the follow-up period, and a traditional survival analysis would not always be appropriate, yet it is commonly seen in literatures. For such kind of data, we carry out simulation studies to compare the performances of the Cox’s PH model with the proportional hazards mixture cure (PHMC) model and the accelerated failure model (AFT model) with the AFT mixture cure (AFTMC) model respectively. Then we apply the models to the datasets of Lung Cancer and Eastern Cooperative Oncology Group (ECOG) phase III clinical trial E1684. The conclusions are as follows. The PHMC model and the AFTMC model do not have obvious advantages for time-to-event data without a cured fraction. In this case, it is recommended to use the Cox’s PH model or AFT model for analysis. If some subjects are non-susceptible to the event of interest in the data, it is recommended to use the PHMC model or AFTMC model for analysis, however, which may need a sufficient sample size. Keywords: Cox’s PH model; PHMC model; AFT model; AFTMC model; cure model

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

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