Proportional hazards models for survival data with long-term survivors

2006 ◽  
Vol 76 (15) ◽  
pp. 1685-1693 ◽  
Author(s):  
Xiaobing Zhao ◽  
Xian Zhou
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Models ◽  
Long Term Survivors

scholarly journals A BAYESIAN WEIBULL ANALYSIS OF BREAST CANCER DATA WITH LONG-TERM SURVIVORS IN PARANA STATE, BRAZIL

2021 ◽  
Vol 39 (2) ◽  
pp. 293-310
Author(s):  
Talita Evelin Nabarrete Tristão de MORAES ◽  
Isolde PREVIDELLI ◽  
Giovani Loiola da SILVA
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
White Women ◽  
Age Groups ◽  
Population Based ◽  
Breast Cancer Data ◽  
Weibull Regression ◽  
Cancer Data ◽  
Long Term Survivors

Breast cancer is one of the most common diseases among women worldwide with about 25% of new cases each year. In Brazil, 59,700 new cases of breast cancer were expected in 2019, according to the Brazilian National Cancer Institute (INCA). Survival analysis has been an useful tool for the identifying the risk and prognostic factors for cancer patients. This work aims to characterize the prognostic value of demographic, clinical and pathological variables in relation to the survival time of 2,092 patients diagnosed with breast cancer in Parana State, Brazil, from 2004 to 2016. In this sense, we propose a Bayesian analysis of survival data with long-term survivors by using Weibull regression models through integrated nested Laplace approximations (INLA). The results point to a proportion of long-term survivors around 57:6% in the population under study. In regard to potential risk factors, we namely concluded that 40-50 year age group has superior survival than younger and older age groups, white women have higher breast cancer risk than other races, and marital status decreases that risk. Caution on the general use of these results is nevertheless advised, since we have analyzed population-based breast cancer data without proper monitoring by a healthprofessional.

scholarly journals Nadarajah-Haghighi Model for Survival Data With Long Term Survivors in the Presence of Right Censored Data

2021 ◽  
pp. 695-709
Author(s):  
Umar Usman ◽  
Shamsuddeen Suleiman ◽  
Bello Magaji Arkilla ◽  
Yakubu Aliyu
Keyword(s):  
Survival Data ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Right Censoring ◽  
Right Censored Data ◽  
Term Survival ◽  
Proposed Model ◽  
Cure Fraction ◽  
Long Term Survivors

In this paper, a new long term survival model called Nadarajah-Haghighi model for survival data with long term survivors was proposed. The model is used in fitting data where the population of interest is a mixture of individuals that are susceptible to the event of interest and individuals that are not susceptible to the event of interest. The statistical properties of the proposed model including quantile function, moments, mean and variance were provided. Maximum likelihood estimation procedure was used to estimate the parameters of the model assuming right censoring. Furthermore, Bayesian method of estimation was also employed in estimating the parameters of the model assuming right censoring. Simulations study was performed in order to ascertain the performances of the MLE estimators. Random samples of different sample sizes were generated from the model with some arbitrary values for the parameters for 5%, 1:3% and 1:5% cure fraction values. Bias, standard error and mean square error were used as discrimination criteria. Additionally, we compared the performance of the proposed model with some competing models. The results of the applications indicates that the proposed model is more efficient than the models compared with. Finally, we fitted some models considering type of treatment as a covariate. It was observed that the covariate  have effect on the shape parameter of the proposed model.

Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study

2012 ◽  
Vol 11 (3) ◽  
pp. 222-229 ◽  
Author(s):  
Etienne Gayat ◽  
Matthieu Resche-Rigon ◽  
Jean-Yves Mary ◽  
Raphaël Porcher
Keyword(s):  
Monte Carlo ◽  
Data Analysis ◽  
Propensity Score ◽  
Survival Data ◽  
Proportional Hazards ◽  
Monte Carlo Study ◽  
Proportional Hazards Models

Analysis of censored survival data using random regression models

2001 ◽  
Vol 72 (1) ◽  
pp. 1-10 ◽  
Author(s):  
R. F. Veerkamp ◽  
S. Brotherstone ◽  
B. Engel ◽  
T. H. E. Meuwissen
Keyword(s):  
Milk Yield ◽  
Survival Data ◽  
Variance Components ◽  
Regression Models ◽  
Proportional Hazards ◽  
Random Regression ◽  
Breeding Values ◽  
Censored Survival Data ◽  
Proportional Hazards Models ◽  
Randomly Censored Data

AbstractCensoring of records is a problem in the prediction of breeding values for longevity, because breeding values are required before actual lifespan is known. In this study we investigated the use of random regression models to analyse survival data, because this method combines some of the advantages of a multitrait approach and the more sophisticated proportional hazards models. A model was derived for the binary representation of survival data and links with proportional hazards models and generalized linear models are shown. Variance components and breeding values were predicted using a linear approximation, including time-dependent fixed effects and random regression coefficients. Production records in lactations 1 to 5 were available on 24741 cows in the UK, all having had the opportunity to survive five lactations. The random regression model contained a linear regression on milk yield within herd (no. = 1417) by lactation number (no. = 4), Holstein percentage and year-month of calving effect (no. = 72). The additive animal genetic effects were modelled using orthogonal polynomials of order 1 to 4 with random coefficients and the error terms were fitted for each lactation separately, either correlated or not. Variance components from the full (i.e. uncensored) data set, were used to predict breeding values for survival in each lactation from both uncensored and randomly censored data. In the uncensored data, estimates of heritabilities for culling probability in each lactation ranged from 0·02 to 0·04. Breeding values for lifespan (calculated from the survival breeding values) had a range of 2·4 to 3·6 lactations and a standard deviation of 0·25. Correlations between predicted breeding values for 129 bulls, each with more than 30 daughters, from the various data sets ranged from 0·81 to 0·99 and were insensitive to the model used. It is concluded that random regression analysis models used for test-day records analysis of milk yield, might also be of use in the analysis of censored survival data.

scholarly journals The U-Shape Relationship Between Glycated Hemoglobin Level and Long-Term All-Cause Mortality Among Patients With Coronary Artery Disease

2021 ◽  
Vol 8 ◽  
Author(s):  
Liwei Liu ◽  
Jianfeng Ye ◽  
Ming Ying ◽  
Qiang Li ◽  
Shiqun Chen ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Proportional Hazards ◽  
Glycated Hemoglobin ◽  
Cox Proportional Hazards ◽  
Proportional Hazards Models ◽  
Cox Proportional Hazards Models ◽  
Risk Of Mortality ◽  
All Cause Mortality ◽  
Artery Disease

Background: Although glycated hemoglobin (HbA1c) was considered as a prognostic factor in some subgroup of coronary artery disease (CAD), the specific relationship between HbA1c and the long-term all-cause death remains controversial in patients with CAD.Methods: The study enrolled 37,596 CAD patients and measured HbAlc at admission in Guangdong Provincial People's Hospital. The patients were divided into 4 groups according to HbAlc level (Quartile 1: HbA1c ≤ 5.7%; Quartile 2: 5.7% < HbA1c ≤ 6.1%; Quartile 3: 6.1% < HbA1c ≤ 6.7%; Quartile 4: HbA1c > 6.7%). The study endpoint was all-cause death. The restricted cubic splines and cox proportional hazards models were used to investigate the association between baseline HbAlc levels and long-term all-cause mortality.Results: The median follow-up was 4 years. The cox proportional hazards models revealed that HbAlc is an independent risk factor in the long-term all-cause mortality. We also found an approximate U-shape association between HbA1c and the risk of mortality, including increased risk of mortality when HbA1c ≤ 5.7% and HbA1c > 6.7% [Compared with Quartile 2, Quartile 1 (HbA1c ≤ 5.7), aHR = 1.13, 95% CI:1.01–1.26, P < 0.05; Quartile 3 (6.1% < HbA1c ≤ 6.7%), aHR = 1.04, 95% CI:0.93–1.17, P =0.49; Quartile 4 (HbA1c > 6.7%), aHR = 1.32, 95% CI:1.19–1.47, P < 0.05].Conclusions: Our study indicated a U-shape relationship between HbA1c and long-term all-cause mortality in CAD patients.

Examining Mortality and Rejection in Combined Heart–Lung Transplantations

2017 ◽  
Vol 65 (05) ◽  
pp. 423-429
Author(s):  
Sharven Taghavi ◽  
Senthil Jayarajan ◽  
Vishnu Ambur ◽  
Grayson Wheatley ◽  
Larry Kaiser ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Multivariable Analysis ◽  
Male Gender ◽  
Clinical Infection ◽  
Short Term ◽  
Proportional Hazards Regression ◽  
Male Sex ◽  
Long Term Survivors ◽  
United Network

Background There is a paucity of data on outcomes related to combined heart–lung transplantations (HLTs). Our objective was to identify variables associated with mortality and rejection in HLT. Methods The United Network for Organ Sharing database was reviewed for HLT performed between 1993 and 2008. Long-term survivors (survival > 5 years) were compared with short-term survivors (survival < 5 years). Factors associated with rejection were examined. Risk-adjusted multivariable Cox's proportional hazards regression analysis was performed to examine variables associated with mortality and rejection. Results Multivariable analysis revealed that recipient male gender was associated with mortality at 1 year (hazard ratio [HR]: 1.68, 95% confidence interval [CI]: 1.11–2.54, p = 0.01) and 5 years (HR: 1.41, 95% CI: 1.05–1.89, p = 0.02). Preoperative extracorporeal membrane oxygenation (ECMO) was associated with mortality at 1 year (HR: 7.55, 95% CI: 2.55–22.30, p < 0.01) and 5 years (HR: 3.14, 95% CI: 1.19–8.32, p = 0.02). Preoperative mechanical ventilation (MV) was associated with mortality at 1 year (HR: 3.51, 95% CI: 1.77–6.98, p < 0.01) and at 5 years (HR: 2.70, 95% CI: 1.51–4.85, p < 0.01). Multivariable analysis showed that male gender (HR: 1.78, 95% CI: 1.03–3.09, p = 0.04) and cytomegalovirus (CMV) positivity in the recipient and donor (HR: 3.09, 95% CI: 1.59–6.01, p < 0.01) were associated with rejection. Clinical infection in the donor (HR: 2.05, 95% CI: 1.16–3.61, p = 0.01) was also associated with rejection. Conclusion Survival was affected by recipient male sex and need for preoperative ECMO or MV. Risk factors for rejection included male sex, CMV positivity in the donor and recipient, and donor with clinical infection.

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