scholarly journals On a general structure for hazard-based regression models: An application to population-based cancer research

2018 ◽  
Vol 28 (8) ◽  
pp. 2404-2417 ◽  
Author(s):  
Francisco J Rubio ◽  
Laurent Remontet ◽  
Nicholas P Jewell ◽  
Aurélien Belot
Keyword(s):  
Regression Models ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
General Structure ◽  
Information Criterion ◽  
Population Based ◽  
Cancer Data ◽  
Hazards Model ◽  
Proposed Model

The proportional hazards model represents the most commonly assumed hazard structure when analysing time to event data using regression models. We study a general hazard structure which contains, as particular cases, proportional hazards, accelerated hazards, and accelerated failure time structures, as well as combinations of these. We propose an approach to apply these different hazard structures, based on a flexible parametric distribution (exponentiated Weibull) for the baseline hazard. This distribution allows us to cover the basic hazard shapes of interest in practice: constant, bathtub, increasing, decreasing, and unimodal. In an extensive simulation study, we evaluate our approach in the context of excess hazard modelling, which is the main quantity of interest in descriptive cancer epidemiology. This study exhibits good inferential properties of the proposed model, as well as good performance when using the Akaike Information Criterion for selecting the hazard structure. An application on lung cancer data illustrates the usefulness of the proposed 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.

Inter-pregnancy interval and later pediatric cardiovascular health of the offspring – a population-based cohort study

2020 ◽  
pp. 1-5
Author(s):  
Majdi Imterat ◽  
Tamar Wainstock ◽  
Eyal Sheiner ◽  
Gali Pariente
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Cardiovascular Morbidity ◽  
Cox Proportional Hazards Model ◽  
Hazards Model ◽  
Kaplan Meier ◽  
Inter Pregnancy Interval

Abstract Recent evidence suggests that a long inter-pregnancy interval (IPI: time interval between live birth and estimated time of conception of subsequent pregnancy) poses a risk for adverse short-term perinatal outcome. We aimed to study the effect of short (<6 months) and long (>60 months) IPI on long-term cardiovascular morbidity of the offspring. A population-based cohort study was performed in which all singleton live births in parturients with at least one previous birth were included. Hospitalizations of the offspring up to the age of 18 years involving cardiovascular diseases and according to IPI length were evaluated. Intermediate interval, between 6 and 60 months, was considered the reference. Kaplan–Meier survival curves were used to compare the cumulative morbidity incidence between the groups. Cox proportional hazards model was used to control for confounders. During the study period, 161,793 deliveries met the inclusion criteria. Of them, 14.1% (n = 22,851) occurred in parturient following a short IPI, 78.6% (n = 127,146) following an intermediate IPI, and 7.3% (n = 11,796) following a long IPI. Total hospitalizations of the offspring, involving cardiovascular morbidity, were comparable between the groups. The Kaplan–Meier survival curves demonstrated similar cumulative incidences of cardiovascular morbidity in all groups. In a Cox proportional hazards model, short and long IPI did not appear as independent risk factors for later pediatric cardiovascular morbidity of the offspring (adjusted HR 0.97, 95% CI 0.80–1.18; adjusted HR 1.01, 95% CI 0.83–1.37, for short and long IPI, respectively). In our population, extreme IPIs do not appear to impact long-term cardiovascular hospitalizations of offspring.

scholarly journals Comorbidities and Outcome of Alcoholic and Non-Alcoholic Liver Cirrhosis in Taiwan: A Population-Based Study

2020 ◽  
Vol 17 (8) ◽  
pp. 2825
Author(s):  
Tzu-Wei Yang ◽  
Chi-Chih Wang ◽  
Ming-Chang Tsai ◽  
Yao-Tung Wang ◽  
Ming-Hseng Tseng ◽  
...  
Keyword(s):  
Liver Cirrhosis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Survival Rates ◽  
Study Groups ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Study Cohort ◽  
Hazards Model

The prognosis of different etiologies of liver cirrhosis (LC) is not well understood. Previous studies performed on alcoholic LC-dominated cohorts have demonstrated a few conflicting results. We aimed to compare the outcome and the effect of comorbidities on survival between alcoholic and non-alcoholic LC in a viral hepatitis-dominated LC cohort. We identified newly diagnosed alcoholic and non-alcoholic LC patients, aged ≥40 years old, between 2006 and 2011, by using the Longitudinal Health Insurance Database. The hazard ratios (HRs) were calculated using the Cox proportional hazards model and the Kaplan–Meier method. A total of 472 alcoholic LC and 4313 non-alcoholic LC patients were identified in our study cohort. We found that alcoholic LC patients were predominantly male (94.7% of alcoholic LC and 62.6% of non-alcoholic LC patients were male) and younger (78.8% of alcoholic LC and 37.4% of non-alcoholic LC patients were less than 60 years old) compared with non-alcoholic LC patients. Non-alcoholic LC patients had a higher rate of concomitant comorbidities than alcoholic LC patients (79.6% vs. 68.6%, p < 0.001). LC patients with chronic kidney disease demonstrated the highest adjusted HRs of 2.762 in alcoholic LC and 1.751 in non-alcoholic LC (all p < 0.001). In contrast, LC patients with hypertension and hyperlipidemia had a decreased risk of mortality. The six-year survival rates showed no difference between both study groups (p = 0.312). In conclusion, alcoholic LC patients were younger and had lower rates of concomitant comorbidities compared with non-alcoholic LC patients. However, all-cause mortality was not different between alcoholic and non-alcoholic LC patients.

scholarly journals Hyperlipidemia and Statins Use for the Risk of New Diagnosed Sarcopenia in Patients with Chronic Kidney: A Population-Based Study

2020 ◽  
Vol 17 (5) ◽  
pp. 1494 ◽  
Author(s):  
Min-Hua Lin ◽  
She-Yu Chiu ◽  
Pei-Hsuan Chang ◽  
Yu-Liang Lai ◽  
Pau-Chung Chen ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Research Database ◽  
Newly Diagnosed ◽  
Hazards Model

Background: Previous research found that statins, in addition to its efficiency in treating hyperlipidemia, may also incur adverse drug reactions, which mainly include myopathies and abnormalities in liver function. Aim: This study aims to assess the risk for newly onset sarcopenia among patients with chronic kidney disease using statins. Material and Method: In a nationwide retrospective population-based cohort study, 75,637 clinically confirmed cases of chronic kidney disease between 1997 and 2011were selected from the National Health Insurance Research Database of Taiwan. The selection of the chronic kidney disease cohort included a discharge diagnosis with chronic kidney disease or more than 3 outpatient visits with the diagnosis of chronic kidney disease found within 1 year. After consideration of patient exclusions, we finally got a total number of 67,001 cases of chronic kidney disease in the study. The Cox proportional hazards model was used to perform preliminary analysis on the effect of statins usage on the occurrence of newly diagnosed sarcopenia; the Cox proportional hazards model with time-dependent covariates was conducted to take into consideration the individual temporal differences in medication usage, and calculated the hazard ratio (HR) and 95% confidence interval after controlling for gender, age, income, and urbanization. Results: Our main findings indicated that patients with chronic kidney disease who use statins seem to effectively prevent patients from occurrences of sarcopenia, high dosage of statins seem to show more significant protective effects, and the results are similar over long-term follow-up. In addition, the risk for newly diagnosed sarcopenia among patients with lipophilic statins treatment was lower than that among patients with hydrophilic statins treatment. Conclusion: It seems that patients with chronic kidney disease could receive statin treatment to reduce the occurrence of newly diagnosed sarcopenia. Additionally, a higher dosage of statins could reduce the incidence of newly diagnosed sarcopenia in patients with chronic kidney disease.

Disparities in receipt of radiotherapy and survival by age, sex, and race among patients with nonmetastatic squamous cell carcinoma of the anus.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 721-721
Author(s):  
Doug Baughman ◽  
Krishna Bilas Ghimire ◽  
Binay Kumar Shah
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Survival Rates ◽  
Relative Survival ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
P Value ◽  
Hazards Model ◽  
To Receive

721 Background: Combination chemoradiotherapy is the standard of care for treatment of non-metastatic squamous cell carcinoma of the anus (SCCA). This population-based study evaluated disparities in receipt of radiotherapy (RT) and its effect on survival in patients with localized and regional SCCA in the United States. Methods: The Surveillance, Epidemiology, and End Results (SEER) 18 database was used to identify patients with localized and regional SCCA diagnosed between 1998 and 2008. We used univariate and multivariate logistic regression to model the relationships between receipt of RT and age, sex, marital status, stage, and race. Relative survival rates were calculated and compared using two sample z-tests. A Cox proportional hazards model was used to find adjusted hazard ratios (HR). Results: A total of 3,971 patients with localized or regional SCCA as the only primary malignancy were included in the study, of which 3,278 (82.6%) received RT. After adjusting for covariates, those 65 years and older (adjusted OR 0.82, p=0.029) were less likely to receive RT. Females were more likely to receive RT compared to males (adjusted OR 1.54, p<0.001). We found no difference in receipt of RT by race. Comparisons of 1- and 5-year relative survival rates showed lower survival for blacks (p-value <0.01 at 1-year and <0.0001 at 5-years), those 65 years and older, and males. A 1-year survival disparity was found for those not receiving RT (p-value <0.0001 at 1-year), but no difference was observed at 5-years. A Cox proportional hazards model adjusting for all covariates showed greater hazard for blacks (adjusted HR 1.36, p=0.001), those not receiving RT (adjusted HR 1.23, p=0.03), patients 65 years or older, and males. Conclusions: This population based study identified older patients as less likely to receive RT and females as more likely to receive RT. Survival analysis identified blacks, males, older patients, and those not receiving RT as having lower rates of survival.

Frailty Approach for the Analysis of Clustered Failure Time Observations in Dental Research

2005 ◽  
Vol 84 (1) ◽  
pp. 54-58 ◽  
Author(s):  
S.K. Chuang ◽  
T. Cai ◽  
C.W. Douglass ◽  
L.J. Wei ◽  
T.B. Dodson
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Implant Failure ◽  
Failure Patterns ◽  
Hazards Model ◽  
Within Subjects ◽  
Immediate Implant

Because dental implant failure patterns tend to cluster within subjects, we hypothesized that the risk of implant failure varies among subjects. To address this hypothesis in the setting of clustered, correlated observations, we considered a retrospective cohort study where we identified a cohort having at least one implant placed. The cohort was composed of 677 patients who had 2349 implants placed. To test the hypothesis, we applied an innovative analytic method, i.e., the Cox proportional hazards model with frailty, to account for correlation within subjects and the heterogeneity of risk, i.e., frailty, among subjects for implant failure. Consistent with our hypothesis, risk for implant failure among subjects varied to a statistically significantly degree (p = 0.041). In addition, the risk for implant failure is significantly associated with several factors, including tobacco use, implant length, immediate implant placement, staging, well size, and proximity of adjacent implants or teeth.

scholarly journals An Inference-based Prognostic Framework for Health Management of Automotive Systems

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Chaitanya Sankavaram ◽  
Anuradha Kodali ◽  
Krishna Pattipati ◽  
Satnam Singh ◽  
Yilu Zhang ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Remaining Useful Life ◽  
Survival Functions ◽  
Time Data ◽  
Automotive Systems ◽  
Multiple Fault Diagnosis ◽  
Hazards Model ◽  
Useful Life

This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.

scholarly journals Correcting inaccurate background mortality in excess hazard models through breakpoints

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Robert Darlin Mba ◽  
◽  
Juste Aristide Goungounga ◽  
Nathalie Grafféo ◽  
Roch Giorgi
Keyword(s):  
Excess Mortality ◽  
Cancer Survival ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Relative Survival ◽  
Population Based ◽  
Deprivation Level ◽  
Parameter Estimates ◽  
Background Mortality ◽  
Proposed Model

Abstract Background Methods for estimating relative survival are widely used in population-based cancer survival studies. These methods are based on splitting the observed (the overall) mortality into excess mortality (due to cancer) and background mortality (due to other causes, as expected in the general population). The latter is derived from life tables usually stratified by age, sex, and calendar year but not by other covariates (such as the deprivation level or the socioeconomic status) which may lack though they would influence background mortality. The absence of these covariates leads to inaccurate background mortality, thus to biases in estimating the excess mortality. These biases may be avoided by adjusting the background mortality for these covariates whenever available. Methods In this work, we propose a regression model of excess mortality that corrects for potentially inaccurate background mortality by introducing age-dependent multiplicative parameters through breakpoints, which gives some flexibility. The performance of this model was first assessed with a single and two breakpoints in an intensive simulation study, then the method was applied to French population-based data on colorectal cancer. Results The proposed model proved to be interesting in the simulations and the applications to real data; it limited the bias in parameter estimates of the excess mortality in several scenarios and improved the results and the generalizability of Touraine’s proportional hazards model. Conclusion Finally, the proposed model is a good approach to correct reliably inaccurate background mortality by introducing multiplicative parameters that depend on age and on an additional variable through breakpoints.

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