scholarly journals Bayesian random threshold estimation in a Cox proportional hazards cure model

2013 ◽  
Vol 33 (4) ◽  
pp. 650-661 ◽  
Author(s):  
Lili Zhao ◽  
Dai Feng ◽  
Emily L. Bellile ◽  
Jeremy M. G. Taylor
Keyword(s):  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Threshold Estimation ◽  
Cure Model ◽  
Random Threshold

scholarly journals Estimation in a Cox Proportional Hazards Cure Model

Biometrics ◽  
2000 ◽  
Vol 56 (1) ◽  
pp. 227-236 ◽  
Author(s):  
Judy P. Sy ◽  
Jeremy M. G. Taylor
Keyword(s):  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Cure Model

Assessing the prediction accuracy of cure in the Cox proportional hazards cure model: an application to breast cancer data

2014 ◽  
Vol 13 (6) ◽  
pp. 357-363 ◽  
Author(s):  
Junichi Asano ◽  
Akihiro Hirakawa ◽  
Chikuma Hamada
Keyword(s):  
Breast Cancer ◽  
Prediction Accuracy ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Breast Cancer Data ◽  
Cure Model ◽  
Cancer Data

Change point detection in Cox proportional hazards mixture cure model

2020 ◽  
pp. 096228022095911
Author(s):  
Bing Wang ◽  
Jialiang Li ◽  
Xiaoguang Wang
Keyword(s):  
Survival Data ◽  
Change Point ◽  
Proportional Hazards ◽  
Test Procedure ◽  
Cox Proportional Hazards ◽  
Change Point Detection ◽  
Parameter Estimates ◽  
Finite Sample ◽  
Cure Model ◽  
Mixture Cure Model

The mixture cure model has been widely applied to survival data in which a fraction of the observations never experience the event of interest, despite long-term follow-up. In this paper, we study the Cox proportional hazards mixture cure model where the covariate effects on the distribution of uncured subjects’ failure time may jump when a covariate exceeds a change point. The nonparametric maximum likelihood estimation is used to obtain the semiparametric estimates. We employ a two-step computational procedure involving the Expectation-Maximization algorithm to implement the estimation. The consistency, convergence rate and asymptotic distributions of the estimators are carefully established under technical conditions and we show that the change point estimator is n consistency. The m out of n bootstrap and the Louis algorithm are used to obtain the standard errors of the estimated change point and other regression parameter estimates, respectively. We also contribute a test procedure to check the existence of the change point. The finite sample performance of the proposed method is demonstrated via simulation studies and real data examples.

scholarly journals Standard errors for the cox proportional hazards cure model

2001 ◽  
Vol 33 (12-13) ◽  
pp. 1237-1251 ◽  
Author(s):  
J.P. Sy ◽  
J.M.G. Taylor
Keyword(s):  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Standard Errors ◽  
Cure Model

scholarly journals Extension of a Cox proportional hazards cure model when cure information is partially known

Biostatistics ◽  
2014 ◽  
Vol 15 (3) ◽  
pp. 540-554 ◽  
Author(s):  
Y. Wu ◽  
Y. Lin ◽  
S.-E. Lu ◽  
C.-S. Li ◽  
W. J. Shih
Keyword(s):  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Cure Model

scholarly journals Multiple imputation of missing covariates for the Cox proportional hazards cure model

2016 ◽  
Vol 35 (26) ◽  
pp. 4701-4717 ◽  
Author(s):  
Lauren J. Beesley ◽  
Jonathan W. Bartlett ◽  
Gregory T. Wolf ◽  
Jeremy M. G. Taylor
Keyword(s):  
Multiple Imputation ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Missing Covariates ◽  
Cure Model

Age at Exposure to Parental Suicide and the Subsequent Risk of Suicide in Young People

Crisis ◽  
2018 ◽  
Vol 39 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Kuan-Ying Lee ◽  
Chung-Yi Li ◽  
Kun-Chia Chang ◽  
Tsung-Hsueh Lu ◽  
Ying-Yeh Chen
Keyword(s):  
Young People ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Birth Registry ◽  
Data Set ◽  
Parental Suicide ◽  
The Impact ◽  
Age At Exposure

Abstract. Background: We investigated the age at exposure to parental suicide and the risk of subsequent suicide completion in young people. The impact of parental and offspring sex was also examined. Method: Using a cohort study design, we linked Taiwan's Birth Registry (1978–1997) with Taiwan's Death Registry (1985–2009) and identified 40,249 children who had experienced maternal suicide (n = 14,431), paternal suicide (n = 26,887), or the suicide of both parents (n = 281). Each exposed child was matched to 10 children of the same sex and birth year whose parents were still alive. This yielded a total of 398,081 children for our non-exposed cohort. A Cox proportional hazards model was used to compare the suicide risk of the exposed and non-exposed groups. Results: Compared with the non-exposed group, offspring who were exposed to parental suicide were 3.91 times (95% confidence interval [CI] = 3.10–4.92 more likely to die by suicide after adjusting for baseline characteristics. The risk of suicide seemed to be lower in older male offspring (HR = 3.94, 95% CI = 2.57–6.06), but higher in older female offspring (HR = 5.30, 95% CI = 3.05–9.22). Stratified analyses based on parental sex revealed similar patterns as the combined analysis. Limitations: As only register-­based data were used, we were not able to explore the impact of variables not contained in the data set, such as the role of mental illness. Conclusion: Our findings suggest a prominent elevation in the risk of suicide among offspring who lost their parents to suicide. The risk elevation differed according to the sex of the afflicted offspring as well as to their age at exposure.

Proliferation-dominant high-grade astrocytoma: survival benefit associated with extensive resection of FLAIR abnormality region

2020 ◽  
Vol 132 (4) ◽  
pp. 998-1005 ◽  
Author(s):  
Haihui Jiang ◽  
Yong Cui ◽  
Xiang Liu ◽  
Xiaohui Ren ◽  
Mingxiao Li ◽  
...  
Keyword(s):  
Survival Benefit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Characteristic Curve ◽  
Extent Of Resection ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
High Grade ◽  
Extensive Resection ◽  
High Grade Astrocytoma

OBJECTIVEThe aim of this study was to investigate the relationship between extent of resection (EOR) and survival in terms of clinical, molecular, and radiological factors in high-grade astrocytoma (HGA).METHODSClinical and radiological data from 585 cases of molecularly defined HGA were reviewed. In each case, the EOR was evaluated twice: once according to contrast-enhanced T1-weighted images (CE-T1WI) and once according to fluid attenuated inversion recovery (FLAIR) images. The ratio of the volume of the region of abnormality in CE-T1WI to that in FLAIR images (VFLAIR/VCE-T1WI) was calculated and a receiver operating characteristic curve was used to determine the optimal cutoff value for that ratio. Univariate and multivariate analyses were performed to identify the prognostic value of each factor.RESULTSBoth the EOR evaluated from CE-T1WI and the EOR evaluated from FLAIR could divide the whole cohort into 4 subgroups with different survival outcomes (p < 0.001). Cases were stratified into 2 subtypes based on VFLAIR/VCE-T1WIwith a cutoff of 10: a proliferation-dominant subtype and a diffusion-dominant subtype. Kaplan-Meier analysis showed a significant survival advantage for the proliferation-dominant subtype (p < 0.0001). The prognostic implication has been further confirmed in the Cox proportional hazards model (HR 1.105, 95% CI 1.078–1.134, p < 0.0001). The survival of patients with proliferation-dominant HGA was significantly prolonged in association with extensive resection of the FLAIR abnormality region beyond contrast-enhancing tumor (p = 0.03), while no survival benefit was observed in association with the extensive resection in the diffusion-dominant subtype (p=0.86).CONCLUSIONSVFLAIR/VCE-T1WIis an important classifier that could divide the HGA into 2 subtypes with distinct invasive features. Patients with proliferation-dominant HGA can benefit from extensive resection of the FLAIR abnormality region, which provides the theoretical basis for a personalized resection strategy.

scholarly journals Adaptive metabolic and inflammatory responses identified using accelerated aging metrics are linked to adverse outcomes in severe SARS-CoV-2 infection

2021 ◽  
Author(s):  
Alejandro Márquez-Salinas ◽  
Carlos A Fermín-Martínez ◽  
Neftalí Eduardo Antonio-Villa ◽  
Arsenio Vargas-Vázquez ◽  
Enrique C. Guerra ◽  
...  
Keyword(s):  
Critical Illness ◽  
Proportional Hazards ◽  
Inflammatory Responses ◽  
Age Groups ◽  
Accelerated Aging ◽  
Cox Proportional Hazards ◽  
Adverse Outcomes ◽  
Adaptive Responses ◽  
Predictive Capacity ◽  
Risk Of Death

Abstract Background Chronological age (CA) is a predictor of adverse COVID-19 outcomes; however, CA alone does not capture individual responses to SARS-CoV-2 infection. Here, we evaluated the influence of aging metrics PhenoAge and PhenoAgeAccel to predict adverse COVID-19 outcomes. Furthermore, we sought to model adaptive metabolic and inflammatory responses to severe SARS-CoV-2 infection using individual PhenoAge components. Methods In this retrospective cohort study, we assessed cases admitted to a COVID-19 reference center in Mexico City. PhenoAge and PhenoAgeAccel were estimated using laboratory values at admission. Cox proportional hazards models were fitted to estimate risk for COVID-19 lethality and adverse outcomes (ICU admission, intubation, or death). To explore reproducible patterns which model adaptive responses to SARS-CoV-2 infection, we used k-means clustering using PhenoAge components. Results We included 1068 subjects of whom 222 presented critical illness and 218 died. PhenoAge was a better predictor of adverse outcomes and lethality compared to CA and SpO2 and its predictive capacity was sustained for all age groups. Patients with responses associated to PhenoAgeAccel&gt;0 had higher risk of death and critical illness compared to those with lower values (log-rank p&lt;0.001). Using unsupervised clustering we identified four adaptive responses to SARS-CoV-2 infection: 1) Inflammaging associated with CA, 2) metabolic dysfunction associated with cardio-metabolic comorbidities, 3) unfavorable hematological response, and 4) response associated with favorable outcomes. Conclusions Adaptive responses related to accelerated aging metrics are linked to adverse COVID-19 outcomes and have unique and distinguishable features. PhenoAge is a better predictor of adverse outcomes compared to CA.

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