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

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

2020 ◽  
Author(s):  
Sheng-li An ◽  
Fuqiang Huang ◽  
Pei Kang ◽  
Yingxin Liu ◽  
Fu-qiang Huang ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Survival Data ◽  
Failure Time ◽  
Capture Rate ◽  
Eastern Cooperative Oncology Group ◽  
Phase Iii ◽  
Failure Time Data ◽  
Time Data ◽  
Aft Model ◽  
K Index

Abstract Background: Some failure time data comes from a population that consists of some subjects that are susceptible to and others that 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. Methods: We carry out simulation studies to compare the performances of Cox’s PH model with proportional hazards mixture cure (PHMC) model and accelerated failure model (AFT model) with 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. Results: When the cured rate is 0, the estimated bias, confidence interval capture rate, and K index of PHMC and AFTMC model are close to Cox’s PH and AFT model respectively. The MSE of PHMC model is slightly larger than Cox’s PH model and of AFTMC model are similar to AFT model. When survival data has a substantial proportion of subjects being cured, the absolute value of Bias and MSE in PHMC and AFTMC model are always smaller than Cox’s PH and AFT model respectively. The confidence interval capture rate of PHMC and AFTMC model are always closer to the acceptable range than Cox’s PH and AFT model. The K index of PHMC and AFTMC model are always greater than Cox’s PH and AFT model. Conclusions: The PHMC and AFTMC model do not have obvious advantages for time-to-event data without a cured fraction. In this case, it is recommended to utilize Cox’s PH or AFT model for analysis. If some subjects are non-susceptible to the event of interest in the data, it is recommended to utilize PHMC 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.

Performance Evaluation of Distributional Models to Analyze Random Right-Censored Breast Cancer Failure Time Data

2020 ◽  
Author(s):  
Madiha Liaqat ◽  
Shahid Kamal ◽  
Florian Fischer ◽  
Waqas Fazil
Keyword(s):  
Breast Cancer ◽  
Failure Time ◽  
Strong Association ◽  
Breast Cancer Incidence ◽  
Disease Free Survival ◽  
Failure Time Data ◽  
Time Data ◽  
Cancer Data ◽  
Aft Model ◽  
The Impact

Abstract Background Censoring frequently occurs in disease data analysis. Typically, non-parametric and semi-parametric methods are used to deal with different types of censored data. Distributional random right-censored failure time models on breast cancer data are employed to empirically find out a best-fitted model. A large number of studies are available on complete and disease-free survival time, but very few have focused on time to death from breast cancer recurrence.Methods In this retrospective study, we investigated the impact of factors related to breast cancer on cause-specific failure time. We included data from women who suffered from breast cancer as a primary disease and observed recurrence. Several factors related to breast cancer incidence and prognosis are studied. A multivariate accelerated failure time (AFT) model is used to evaluate the combined effect of study factors on death due to breast cancer.Results Univariate Weibull model showed that all factors included in the model have a strong association with breast cancer failure time. These factors are age at diagnosis, age at recurrence, molecular markers (estrogen, progesterone receptors, and Her2.neu), tumor grade, chemotherapy, and radiotherapy. The best model for right-censored breast cancer failure time data was a Weibull AFT, which was chosen by a stepwise backward selection.Conclusions The AFT model is the best choice for the analysis of time to failure data when hazards are non-proportional, as it provides efficient estimates and an estimate of the median failure time ratios.

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.

5. Modeling Multiple Failure Time Data: A Survey of Variance-Corrected Proportional Hazards Models with Empirical Applications to Arrest Data

2003 ◽  
Vol 33 (1) ◽  
pp. 111-167 ◽  
Author(s):  
Michael E. Ezell ◽  
Kenneth C. Land ◽  
Lawrence E. Cohen
Keyword(s):  
Proportional Hazards ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Proportional Hazards Models ◽  
Failure Times ◽  
Software Packages ◽  
California Youth Authority ◽  
Data Requirements ◽  
Illustrative Application

Proportional hazards models are powerful methods for the analysis of dynamic social processes and are widely used in sociology to estimate the effects of covariates on event timing (e.g., time to arrest, birth, marriage). The proper statistical modeling of failure time data is an important analytical issue in sociology, but to date the field has largely neglected the application of these models to multiple failure time data in which the conventional assumption of the independence of failure times is not tenable. This paper critically describes a class of models known as variance-corrected proportional hazards models that have been developed by statisticians to take into account a lack of independence among failure times. The purpose is to provide an exposition and comparison for sociologists of several such models and associated methods for handling multiple failure time data that can be readily estimated in commonly available statistical software packages. We pay special attention to the data requirements necessary for estimation of the models. The paper concludes with an illustrative application of the models to analyze the arrest patterns of a sample of California Youth Authority parolees.

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