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.

scholarly journals Semiparametric Odds Rate Model for Modeling Short-Term and Long-Term Effects with Application to a Breast Cancer Genetic Study

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Mengdie Yuan ◽  
Guoqing Diao
Keyword(s):  
Breast Cancer ◽  
Genetic Study ◽  
Failure Time ◽  
Proportional Odds Model ◽  
Failure Time Data ◽  
Rate Model ◽  
Proportional Odds ◽  
Time Data ◽  
Short Term

AbstractThe proportional odds model is commonly used in the analysis of failure time data. The assumption of constant odds ratios over time in the proportional odds model, however, can be violated in some applications. Motivated by a genetic study with breast cancer patients, we propose a novel semiparametric odds rate model for the analysis of right-censored survival data. The proposed model incorporates the short-term and long-term covariate effects on the failure time data and includes the proportional odds model as a nested model. We develop efficient likelihood-based inference procedures and establish the large sample properties of the proposed nonparametric maximum likelihood estimators. Simulation studies demonstrate that the proposed methods perform well in practical settings. An application to the motivating example is provided.

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

scholarly journals The impact of CBP expression in estrogen receptor-positive breast cancer

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wafaa S. Ramadan ◽  
Iman M. Talaat ◽  
Mahmood Y. Hachim ◽  
Annette Lischka ◽  
Timo Gemoll ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Estrogen Receptor ◽  
Cell Lines ◽  
Strong Association ◽  
Disease Free Survival ◽  
Tumor Formation ◽  
Luminal A ◽  
Number Of Patients ◽  
The Impact

Abstract Background The development of new biomarkers with diagnostic, prognostic and therapeutic prominence will greatly enhance the management of breast cancer (BC). Several reports suggest the involvement of the histone acetyltransferases CREB-binding protein (CBP) and general control non-depressible 5 (GCN5) in tumor formation; however, their clinical significance in BC remains poorly understood. This study aims to investigate the value of CBP and GCN5 as markers and/or targets for BC prognosis and therapy. Expression of CBP, GCN5, estrogen receptor α (ERα), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) in BC was analyzed in cell lines by western blot and in patients’ tissues by immunohistochemistry. The gene amplification data were also analyzed for CBP and GCN5 using the publicly available data from BC patients. Results Elevated expression of CBP and GCN5 was detected in BC tissues from patients and cell lines more than normal ones. In particular, CBP was more expressed in luminal A and B subtypes. Using chemical and biological inhibitors for CBP, ERα and HER2 showed a strong association between CBP and the expression of ERα and HER2. Moreover, analysis of the CREBBP (for CBP) and KAT2A (for GCN5) genes in a larger number of patients in publicly available databases showed amplification of both genes in BC patients. Amplification of CREBBP gene was observed in luminal A, luminal B and triple-negative but not in HER2 overexpressing subtypes. Furthermore, patients with high CREBBP or KAT2A gene expression had better 5-year disease-free survival than the low gene expression group (p = 0.0018 and p < 0.00001, respectively). Conclusions We conclude that the persistent amplification and overexpression of CBP in ERα- and PR-positive BC highlights the significance of CBP as a new diagnostic marker and therapeutic target in hormone-positive BC.

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

Correlation analysis of software failure time data

2010 ◽  
Vol 30 (3) ◽  
pp. 600-602
Author(s):  
Jun-gang LOU ◽  
Jian-hui JIANG
Keyword(s):  
Correlation Analysis ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Software Failure

scholarly journals Cisplatin +/− rucaparib after preoperative chemotherapy in patients with triple-negative or BRCA mutated breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Maitri Kalra ◽  
Yan Tong ◽  
David R. Jones ◽  
Tom Walsh ◽  
Michael A. Danso ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Neoadjuvant Therapy ◽  
Triple Negative ◽  
Residual Disease ◽  
Disease Free Survival ◽  
Parp Inhibition ◽  
High Risk Population ◽  
Risk Population ◽  
The Impact

AbstractPatients with triple-negative breast cancer (TNBC) who have residual disease after neoadjuvant therapy have a high risk of recurrence. We tested the impact of DNA-damaging chemotherapy alone or with PARP inhibition in this high-risk population. Patients with TNBC or deleterious BRCA mutation (TNBC/BRCAmut) who had >2 cm of invasive disease in the breast or persistent lymph node (LN) involvement after neoadjuvant therapy were assigned 1:1 to cisplatin alone or with rucaparib. Germline mutations were identified with BROCA analysis. The primary endpoint was 2-year disease-free survival (DFS) with 80% power to detect an HR 0.5. From Feb 2010 to May 2013, 128 patients were enrolled. Median tumor size at surgery was 1.9 cm (0–11.5 cm) with 1 (0–38) involved LN; median Residual Cancer Burden (RCB) score was 2.6. Six patients had known deleterious BRCA1 or BRCA2 mutations at study entry, but BROCA identified deleterious mutations in 22% of patients with available samples. Toxicity was similar in both arms. Despite frequent dose reductions (21% of patients) and delays (43.8% of patients), 73% of patients completed planned cisplatin. Rucaparib exposure was limited with median concentration 275 (82–4694) ng/mL post-infusion on day 3. The addition of rucaparib to cisplatin did not increase 2-year DFS (54.2% cisplatin vs. 64.1% cisplatin + rucaparib; P = 0.29). In the high-risk post preoperative TNBC/BRCAmut setting, the addition of low-dose rucaparib did not improve 2-year DFS or increase the toxicity of cisplatin. Genetic testing was underutilized in this high-risk population.

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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