Objective:The relationship between cause-specific mortality and regional socio-economic and environmental indicators remains poorly studied in Russia.The study first aims to study regional differences in cause-specifi...Objective:The relationship between cause-specific mortality and regional socio-economic and environmental indicators remains poorly studied in Russia.The study first aims to study regional differences in cause-specific mortality among the population aged 20 years and older in Russia,and second to investigate the association between regional deprivation and cause-specific mortality.Material and methods:Russian deprivation index was used to measure level of deprivation.The index consists of three components:social,economic and environmental.The index measures general deprivation,and its compo-nents measure social,economic and environmental deprivation.The mortality data by age(five-year groups)and sex in the subjects of Russia from 2006 to 2022 were extracted from the Russian Fertility and Mortality Database of the Center of Demographic Research of the New Economic School.Results:In the most general deprived areas,mortality rate from infectious and parasitic diseases increased by more than twice in the total population,women and men as compared to the least deprived quantile(Q1).Fully adjusted negative binomial regression showed an increase in mortality rate from injuries,poisoning and external causes and infectious and parasitic diseases in more social deprived areas as compared to Q1 in the total population,women and men.In men,there was a significantly higher mortality rate from neoplasms and from infectious and parasitic diseases in more economic deprived areas as compared to Q1.Both in total population and in women,there was a trend towards an increase in mortality from neoplasms depending on the level of environmental deprivation.Conclusions:This is the first study examining the relationship of contextual factors with cause-specific mortality that takes into account sex,age and year of death at the population level in Russia.General,social,economic and environmental deprivation are associated with cause-specific mortality.展开更多
In the analysis of competing risk data, the observed effect of a covariate can be obtained via a Fine and Gray sub-distribution hazard ratio. Sometimes, it is also desirable to obtain the virtual effect of a covariate...In the analysis of competing risk data, the observed effect of a covariate can be obtained via a Fine and Gray sub-distribution hazard ratio. Sometimes, it is also desirable to obtain the virtual effect of a covariate as if the competing risks were non-existent. Under the latent failure time scenario, when the event of interest and the competing risk event are independent, the cause-specific hazard ratio obtained from the Cox model where the competing events are censored represents the ratio of the marginal hazards and can be interpreted as the virtual effect of the covariate. However, when the two events are not independent, the cause-specific hazard ratio is not the ratio of the marginal hazards as the ratio depends not only on the marginal hazards but also on the correlation between the competing risk and the event of interest. Using simulation, we investigated the degree to which the cause-specific hazard ratio changes relative to the marginal hazard with this correlation. It was found that the discrepancy between the cause-specific hazard ratio and the theoretical marginal hazard ratio increased as the proportion of competing risk events and the correlation between the events increased （〉0.2）. Depending on the direction of the correlation, the cause-specific hazard ratio can over- or under-estimate the marginal hazard ratio. Using real-life datasets, we show how these results can be used to make inferences on the virtual effects.展开更多
Objective: The aim of this study was to explore the relationship between fasting glucose levels and all-cause and cause-specific mortality in Chinese population. Methods: The role of fasting blood glucose levels as a ...Objective: The aim of this study was to explore the relationship between fasting glucose levels and all-cause and cause-specific mortality in Chinese population. Methods: The role of fasting blood glucose levels as a predictor of all-cause and cause-specific mortality was estimated in 9930 participants from four Chinese general populations with a 20-year follow-up. Multivariate Cox proportional hazard models were used to identify the relationship between fasting glucose and mortality. Results: There were 1471 deaths after a median follow-up of 20.2 years (a total of 187,374 person-years), including 310 cardiovascular deaths, 581 cancer deaths, and 580 other-cause deaths. After adjustment for age, sex, urban or rural, northern or southern of China, types of work, education level, physical exercise, smoking status, drinking status, body mass index, systolic blood pressure, and serum total cholesterol at baseline, the hazard ratios (HRs) and 95% confidence intervals (C/s) for all-cause mortality in the fasting blood glucose categories of <60, 60—69, 70—79, 90—99, 100—109, 110—125, and >126 mg/dl were 1.38 (1.04-1.84), 1.20 (1.01-1.43), 1.18 (1.03-1.36), 1.18 (0.99-1.41), 1.48 (1.16-1.88), 1.17 (0.84-1.62), and 2.23 (1.72—2.90), respectively, in contrast to the reference group (80—89 mg/dl). The HRs, and 95% C/s for cardiovascular disease mortality in these groups were 2.58 (1.44-4.61), 1.41 (0.95-2.10), 1.56 (1.15-2.11), 1.29 (0.88-1.89), 1.36 (0.78-2.37), 1.05 (0.52—2.11), and 2.73 (1.64—4.56), respectively. Conclusions: Both low and high fasting glucose were significantly associated with increased risk of all-cause and cardiovascular mortality in Chinese general population.展开更多
BACKGROUND Signet ring cell carcinoma(SRCC)is an uncommon subtype in colorectal cancer(CRC),with a short survival time.Therefore,it is imperative to establish a useful prognostic model.As a simple visual predictive to...BACKGROUND Signet ring cell carcinoma(SRCC)is an uncommon subtype in colorectal cancer(CRC),with a short survival time.Therefore,it is imperative to establish a useful prognostic model.As a simple visual predictive tool,nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years.Until now,there has been no nomogram to predict the outcome of CRC patients with SRCC.AIM To build effective nomograms for predicting overall survival(OS)and causespecific survival(CSS)of CRC patients with SRCC.METHODS Data were extracted from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms.Performance of the nomograms was assessed by concordance index,calibration curves,and receiver operating characteristic(ROC)curves.ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis(TNM)staging system.Patients were classified as high-risk,moderate-risk,and low-risk groups using the novel nomograms.Kaplan-Meier curves were plotted to compare survival differences.RESULTS In total,1230 patients were included.The concordance index of the nomograms for OS and CSS were 0.737(95%confidence interval:0.728-0.747)and 0.758(95%confidence interval:0.738-0.778),respectively.The calibration curves and ROC curves demonstrated good predictive accuracy.The 1-,3-,and 5-year area under the curve values of the nomogram for predicting OS were 0.796,0.825 and 0.819,in comparison to 0.743,0.798,and 0.803 for the TNM staging system.In addition,the 1-,3-,and 5-year area under the curve values of the nomogram for predicting CSS were 0.805,0.847 and 0.863,in comparison to 0.740,0.794,and 0.800 for the TNM staging system.Based on the novel nomograms,stratified analysis showed that the 5-year probability of survival in the high-risk,moderate-risk,and low-risk groups was 6.8%,37.7%,and 67.0%for OS(P<0.001),as well as 9.6%,38.5%,and 67.6%for CSS(P<0.001),respectively.CONCLUSION Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC,which are superior to the conventional TNM staging system.展开更多
Regression models play a vital role in the study of data regarding survival of subjects.The Cox proportional hazards model for regression analysis has been frequently used in sur-vival modelling.In survival studies,it...Regression models play a vital role in the study of data regarding survival of subjects.The Cox proportional hazards model for regression analysis has been frequently used in sur-vival modelling.In survival studies,it is also possible that survival time may occur with multiple occurrences of event or competing risks.The situation of competing risks arises when there are more than one mutually exclusive causes of death(or failure)for the person(or subject).In this paper,we developed a parametric regression model using Gompertz distribution via the Cox proportional hazards model with competing risks.We discussed point and interval estimation of unknown parameters and cumulative cause-specific hazard function with maximum-likelihood method and Bayesian method of estimation.The Bayes estimates are obtained based on non-informative priors and symmetric as well as asym-metric loss functions.To observe the finite sample behaviour of the proposed model under both estimation procedures,we carried out a Monte Carlo simulation analysis.To demon-strate our methodology,we also included real data analysis.展开更多
Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high- dimension and small sample size cases ...Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high- dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The per- formance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic （ROC） curve and bootstrap .632 ＋ prediction error curves. The elastic net penalization method was shown to outper- form Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant（P 〈 0.001）. Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDHwas associated with a decrease in survival time, whereas SMARCAD1 expression was asso- ciated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting ＇for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the mieroarray features.展开更多
Background:The association of milk intake with cardiovascular disease(CVD)and cause-specific mortality remained controversial and evidence among the Chinese population was limited.We aimed to study the relationship be...Background:The association of milk intake with cardiovascular disease(CVD)and cause-specific mortality remained controversial and evidence among the Chinese population was limited.We aimed to study the relationship between milk intake and CVDs among general Chinese adults.Methods::A total of 104,957 participants received questionnaire survey.Results of physical examination such as anthropometric measurements and biochemical tests during 2007 to 2008,demographic data and their information on milk intake were collected through standardized questionnaires.Cox proportional hazard regression models were used to calculate hazard ratios(HRs)and their corresponding 95%confidence intervals(CIs)of CVD incidence,cause-specific mortality and all-cause mortality related to milk intake.Restricted cubic splines(RCSs)were applied to examine dose-response associations.Results::Among the 91,757 participants with a median follow-up period of 5.8 years,we documented 3877 CVD cases and 4091 all-cause deaths.Compared with participants who never consumed milk,the multivariate-adjusted HRs(95%CIs)of CVD incidence for 1 to 150 g/day,151 to 299 g/day,and≥300 g/day were 0.94(0.86-1.03)(P>0.05),0.77(0.66-0.89)(P<0.05),and 0.59(0.40-0.89)(P<0.05),respectively;each 100 g increase of daily milk intake was associated with 11%lower risk of CVD incidence(HR,0.89;95%CI:0.85-0.94;P<0.001),and 11%lower risk of CVD mortality(HR,0.89;95%CI:0.82-0.97;P=0.008)after adjustment for age,sex,residential area,geographic region,education level,family history of CVD,smoking,alcohol drinking,physical activity level,body mass index,and healthy diet status(ideal or not).RCS analyses also showed a linear dose-response relationship with CVD(P for overall significance of the curve<0.001;P for non-linearity=0.979;P for linearity<0.001)and stroke(P for overall significance of the curve=0.010;P for non-linearity=0.998;P for linearity=0.002)incidence,and CVD mortality(P for overall significance of the curve=0.045;P for non-linearity=0.768;P for linearity=0.014)within the current range of daily milk intake.Conclusions::Daily milk intake was associated with lower risk of CVD incidence and mortality in a linear inverse relationship.The findings provide new evidence for dietary recommendations in CVD prevention among Chinese adults and people with similar dietary pattern in other countries.展开更多
Poaching as well as loss of habitat and prey are identified as causes of tiger population declines.Although some studies have examined habitat requirements and prey availability,few studies have quantified cause-speci...Poaching as well as loss of habitat and prey are identified as causes of tiger population declines.Although some studies have examined habitat requirements and prey availability,few studies have quantified cause-specific mortality of tigers.We used cumulative incidence functions(CIFs)to quantify cause-specific mortality rates of tigers,expanding and refining earlier studies to assess the potential impact of a newly emerging disease.To quantify changes in tiger mortality over time,we re-examined data first collected by Goodrich et al.(2008;study period 1:1992–2004)as well as new telemetry data collected since January 2005(study period 2:2005–2012)using a total of 57 tigers(27 males and 30 females)monitored for an average of 747 days(range 26–4718 days).Across the entire study period(1992 to 2012)we found an estimated average annual survival rate of 0.75 for all tigers combined.Poaching was the primary cause of mortality during both study periods,followed by suspected poaching,distemper and natural/unknown causes.Since 2005,poaching mortality has remained relatively constant and,if combined with suspected poaching,may account for a loss of 17–19%of the population each year.Canine distemper virus(CDV)may be an additive form of mortality to the population,currently accounting for an additional 5%.Despite this relatively new source of mortality,poaching remains the main threat to Amur tiger survival and,therefore,population growth.展开更多
Few multicity studies have examined the acute effects of nitrogen dioxide(NO_(2))on respiratory disease(RD),especially its specific causes.This study aimed to investigate the associations between short-term exposure t...Few multicity studies have examined the acute effects of nitrogen dioxide(NO_(2))on respiratory disease(RD),especially its specific causes.This study aimed to investigate the associations between short-term exposure to NO_(2)and hospitalization of full-spectrum RDs in China.Hospitalization of 10 major categories and 40 cause-specific RDs were obtained from 20 provinces over the study period of 2013−2020.A time-stratified case-crossover study was conducted at the individual level to explore the associations between NO_(2)and RDs.NO_(2)was significantly associated with increased hospitalization of eight major RDs(acute upper respiratory infections,influenza and pneumonia,acute lower respiratory infections,upper respiratory tract diseases,chronic lower respiratory diseases,respiratory interstitium diseases,pleura diseases,and other respiratory diseases)and 18 specific causes of RDs,with the largest associations observed on lag 0−1 day.The effect estimates ranged from 0.75 to 4.09%per 10μg/m^(3)of NO_(2)exposure.The associations remained robust after controlling for copollutants.The concentration−response curves were mostly positive and linear.This nationwide study provides comprehensive information on the acute effects of NO_(2)on respiratory morbidity across the full spectrum,highlighting the need for caution with regard to this important traffic-related air pollutant in current pollution control programs.展开更多
Few national studies have systemically examined the effects of criteria air pollutants on cardiovascular morbidity.This study aimed to investigate the associations between all criteria air pollutants and hospitalizati...Few national studies have systemically examined the effects of criteria air pollutants on cardiovascular morbidity.This study aimed to investigate the associations between all criteria air pollutants and hospitalization of causespecific cardiovascular diseases(CVD)in China.We obtained data on CVD hospitalization events of four major categories and 12 specific diseases from 153 hospitals distributed in 20 provincial-level regions from 2013 to 2020.We adopted a time-stratified case-crossover study design using individual cases to capture the effect of short-term exposure to six criteria air pollutants on CVD hospitalizations,using conditional logistic regression models.More than 1.1 million CVD hospitalization events were included.The lag pattern exploration demonstrated the largest effect for six air pollutants on lag 0–1 day.PM_(2.5),PM_(10),NO_(2),and CO were significantly associated with increased hospitalization from ischemic heart diseases,cerebrovascular diseases,other heart diseases,and five specific causes of CVD.The effect estimates of NO_(2)were the most robust when adjusting for copollutants.The concentration-response curves were positive and linear for most pollutant–endpoint pairs(except for O_(3)),and these positive associations remained even below the 24-h levels recommended by WHO Air Quality Guidelines and China Air Quality Standards.This nationwide case-crossover study in China demonstrated that short-term exposure to multiple ambient air pollutants may significantly increase the risk of cause-specific CVD hospitalizations even under the most stringent air quality regulations,striking an alert for potential CVD patients against these environmental risk factors.展开更多
Rare event data is encountered when the events of interest occur with low frequency, and the estimators based on the cohort data only may be inefficient. However, when external information is available for the estimat...Rare event data is encountered when the events of interest occur with low frequency, and the estimators based on the cohort data only may be inefficient. However, when external information is available for the estimation, the estimators utilizing external information can be more efficient. In this paper, we propose a method to incorporate external information into the estimation of the baseline hazard function and improve efficiency for estimating the absolute risk under the additive hazards model. The resulting estimators are shown to be uniformly consistent and converge weakly to Gaussian processes. Simulation studies demonstrate that the proposed method is much more efficient. An application to a bone marrow transplant data set is provided.展开更多
文摘Objective:The relationship between cause-specific mortality and regional socio-economic and environmental indicators remains poorly studied in Russia.The study first aims to study regional differences in cause-specific mortality among the population aged 20 years and older in Russia,and second to investigate the association between regional deprivation and cause-specific mortality.Material and methods:Russian deprivation index was used to measure level of deprivation.The index consists of three components:social,economic and environmental.The index measures general deprivation,and its compo-nents measure social,economic and environmental deprivation.The mortality data by age(five-year groups)and sex in the subjects of Russia from 2006 to 2022 were extracted from the Russian Fertility and Mortality Database of the Center of Demographic Research of the New Economic School.Results:In the most general deprived areas,mortality rate from infectious and parasitic diseases increased by more than twice in the total population,women and men as compared to the least deprived quantile(Q1).Fully adjusted negative binomial regression showed an increase in mortality rate from injuries,poisoning and external causes and infectious and parasitic diseases in more social deprived areas as compared to Q1 in the total population,women and men.In men,there was a significantly higher mortality rate from neoplasms and from infectious and parasitic diseases in more economic deprived areas as compared to Q1.Both in total population and in women,there was a trend towards an increase in mortality from neoplasms depending on the level of environmental deprivation.Conclusions:This is the first study examining the relationship of contextual factors with cause-specific mortality that takes into account sex,age and year of death at the population level in Russia.General,social,economic and environmental deprivation are associated with cause-specific mortality.
文摘In the analysis of competing risk data, the observed effect of a covariate can be obtained via a Fine and Gray sub-distribution hazard ratio. Sometimes, it is also desirable to obtain the virtual effect of a covariate as if the competing risks were non-existent. Under the latent failure time scenario, when the event of interest and the competing risk event are independent, the cause-specific hazard ratio obtained from the Cox model where the competing events are censored represents the ratio of the marginal hazards and can be interpreted as the virtual effect of the covariate. However, when the two events are not independent, the cause-specific hazard ratio is not the ratio of the marginal hazards as the ratio depends not only on the marginal hazards but also on the correlation between the competing risk and the event of interest. Using simulation, we investigated the degree to which the cause-specific hazard ratio changes relative to the marginal hazard with this correlation. It was found that the discrepancy between the cause-specific hazard ratio and the theoretical marginal hazard ratio increased as the proportion of competing risk events and the correlation between the events increased （〉0.2）. Depending on the direction of the correlation, the cause-specific hazard ratio can over- or under-estimate the marginal hazard ratio. Using real-life datasets, we show how these results can be used to make inferences on the virtual effects.
文摘Objective: The aim of this study was to explore the relationship between fasting glucose levels and all-cause and cause-specific mortality in Chinese population. Methods: The role of fasting blood glucose levels as a predictor of all-cause and cause-specific mortality was estimated in 9930 participants from four Chinese general populations with a 20-year follow-up. Multivariate Cox proportional hazard models were used to identify the relationship between fasting glucose and mortality. Results: There were 1471 deaths after a median follow-up of 20.2 years (a total of 187,374 person-years), including 310 cardiovascular deaths, 581 cancer deaths, and 580 other-cause deaths. After adjustment for age, sex, urban or rural, northern or southern of China, types of work, education level, physical exercise, smoking status, drinking status, body mass index, systolic blood pressure, and serum total cholesterol at baseline, the hazard ratios (HRs) and 95% confidence intervals (C/s) for all-cause mortality in the fasting blood glucose categories of <60, 60—69, 70—79, 90—99, 100—109, 110—125, and >126 mg/dl were 1.38 (1.04-1.84), 1.20 (1.01-1.43), 1.18 (1.03-1.36), 1.18 (0.99-1.41), 1.48 (1.16-1.88), 1.17 (0.84-1.62), and 2.23 (1.72—2.90), respectively, in contrast to the reference group (80—89 mg/dl). The HRs, and 95% C/s for cardiovascular disease mortality in these groups were 2.58 (1.44-4.61), 1.41 (0.95-2.10), 1.56 (1.15-2.11), 1.29 (0.88-1.89), 1.36 (0.78-2.37), 1.05 (0.52—2.11), and 2.73 (1.64—4.56), respectively. Conclusions: Both low and high fasting glucose were significantly associated with increased risk of all-cause and cardiovascular mortality in Chinese general population.
文摘BACKGROUND Signet ring cell carcinoma(SRCC)is an uncommon subtype in colorectal cancer(CRC),with a short survival time.Therefore,it is imperative to establish a useful prognostic model.As a simple visual predictive tool,nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years.Until now,there has been no nomogram to predict the outcome of CRC patients with SRCC.AIM To build effective nomograms for predicting overall survival(OS)and causespecific survival(CSS)of CRC patients with SRCC.METHODS Data were extracted from the Surveillance,Epidemiology,and End Results database between 2004 and 2015.Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms.Performance of the nomograms was assessed by concordance index,calibration curves,and receiver operating characteristic(ROC)curves.ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis(TNM)staging system.Patients were classified as high-risk,moderate-risk,and low-risk groups using the novel nomograms.Kaplan-Meier curves were plotted to compare survival differences.RESULTS In total,1230 patients were included.The concordance index of the nomograms for OS and CSS were 0.737(95%confidence interval:0.728-0.747)and 0.758(95%confidence interval:0.738-0.778),respectively.The calibration curves and ROC curves demonstrated good predictive accuracy.The 1-,3-,and 5-year area under the curve values of the nomogram for predicting OS were 0.796,0.825 and 0.819,in comparison to 0.743,0.798,and 0.803 for the TNM staging system.In addition,the 1-,3-,and 5-year area under the curve values of the nomogram for predicting CSS were 0.805,0.847 and 0.863,in comparison to 0.740,0.794,and 0.800 for the TNM staging system.Based on the novel nomograms,stratified analysis showed that the 5-year probability of survival in the high-risk,moderate-risk,and low-risk groups was 6.8%,37.7%,and 67.0%for OS(P<0.001),as well as 9.6%,38.5%,and 67.6%for CSS(P<0.001),respectively.CONCLUSION Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC,which are superior to the conventional TNM staging system.
文摘Regression models play a vital role in the study of data regarding survival of subjects.The Cox proportional hazards model for regression analysis has been frequently used in sur-vival modelling.In survival studies,it is also possible that survival time may occur with multiple occurrences of event or competing risks.The situation of competing risks arises when there are more than one mutually exclusive causes of death(or failure)for the person(or subject).In this paper,we developed a parametric regression model using Gompertz distribution via the Cox proportional hazards model with competing risks.We discussed point and interval estimation of unknown parameters and cumulative cause-specific hazard function with maximum-likelihood method and Bayesian method of estimation.The Bayes estimates are obtained based on non-informative priors and symmetric as well as asym-metric loss functions.To observe the finite sample behaviour of the proposed model under both estimation procedures,we carried out a Monte Carlo simulation analysis.To demon-strate our methodology,we also included real data analysis.
基金funded by the Vice Chancellor for Research and Technology of Hamadan University of Medical Sciences (grant No.9210173382)
文摘Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high- dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The per- formance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic （ROC） curve and bootstrap .632 ＋ prediction error curves. The elastic net penalization method was shown to outper- form Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant（P 〈 0.001）. Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDHwas associated with a decrease in survival time, whereas SMARCAD1 expression was asso- ciated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting ＇for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the mieroarray features.
基金This study was supported by grants from the Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(Nos.2017-I2M-1-004,2019-I2M-2-003)National Key R&D Program of China(Nos.2017YFC0211700 and 2018YFE0115300)the National Natural Science Foundation of China(No.91643208).
文摘Background:The association of milk intake with cardiovascular disease(CVD)and cause-specific mortality remained controversial and evidence among the Chinese population was limited.We aimed to study the relationship between milk intake and CVDs among general Chinese adults.Methods::A total of 104,957 participants received questionnaire survey.Results of physical examination such as anthropometric measurements and biochemical tests during 2007 to 2008,demographic data and their information on milk intake were collected through standardized questionnaires.Cox proportional hazard regression models were used to calculate hazard ratios(HRs)and their corresponding 95%confidence intervals(CIs)of CVD incidence,cause-specific mortality and all-cause mortality related to milk intake.Restricted cubic splines(RCSs)were applied to examine dose-response associations.Results::Among the 91,757 participants with a median follow-up period of 5.8 years,we documented 3877 CVD cases and 4091 all-cause deaths.Compared with participants who never consumed milk,the multivariate-adjusted HRs(95%CIs)of CVD incidence for 1 to 150 g/day,151 to 299 g/day,and≥300 g/day were 0.94(0.86-1.03)(P>0.05),0.77(0.66-0.89)(P<0.05),and 0.59(0.40-0.89)(P<0.05),respectively;each 100 g increase of daily milk intake was associated with 11%lower risk of CVD incidence(HR,0.89;95%CI:0.85-0.94;P<0.001),and 11%lower risk of CVD mortality(HR,0.89;95%CI:0.82-0.97;P=0.008)after adjustment for age,sex,residential area,geographic region,education level,family history of CVD,smoking,alcohol drinking,physical activity level,body mass index,and healthy diet status(ideal or not).RCS analyses also showed a linear dose-response relationship with CVD(P for overall significance of the curve<0.001;P for non-linearity=0.979;P for linearity<0.001)and stroke(P for overall significance of the curve=0.010;P for non-linearity=0.998;P for linearity=0.002)incidence,and CVD mortality(P for overall significance of the curve=0.045;P for non-linearity=0.768;P for linearity=0.014)within the current range of daily milk intake.Conclusions::Daily milk intake was associated with lower risk of CVD incidence and mortality in a linear inverse relationship.The findings provide new evidence for dietary recommendations in CVD prevention among Chinese adults and people with similar dietary pattern in other countries.
文摘Poaching as well as loss of habitat and prey are identified as causes of tiger population declines.Although some studies have examined habitat requirements and prey availability,few studies have quantified cause-specific mortality of tigers.We used cumulative incidence functions(CIFs)to quantify cause-specific mortality rates of tigers,expanding and refining earlier studies to assess the potential impact of a newly emerging disease.To quantify changes in tiger mortality over time,we re-examined data first collected by Goodrich et al.(2008;study period 1:1992–2004)as well as new telemetry data collected since January 2005(study period 2:2005–2012)using a total of 57 tigers(27 males and 30 females)monitored for an average of 747 days(range 26–4718 days).Across the entire study period(1992 to 2012)we found an estimated average annual survival rate of 0.75 for all tigers combined.Poaching was the primary cause of mortality during both study periods,followed by suspected poaching,distemper and natural/unknown causes.Since 2005,poaching mortality has remained relatively constant and,if combined with suspected poaching,may account for a loss of 17–19%of the population each year.Canine distemper virus(CDV)may be an additive form of mortality to the population,currently accounting for an additional 5%.Despite this relatively new source of mortality,poaching remains the main threat to Amur tiger survival and,therefore,population growth.
基金supported by the National Natural Science Foundation of China(Grant Nos.92043301,82103790)the Shanghai International Science and Technology Partnership Project(Grant No.21230780200).
文摘Few multicity studies have examined the acute effects of nitrogen dioxide(NO_(2))on respiratory disease(RD),especially its specific causes.This study aimed to investigate the associations between short-term exposure to NO_(2)and hospitalization of full-spectrum RDs in China.Hospitalization of 10 major categories and 40 cause-specific RDs were obtained from 20 provinces over the study period of 2013−2020.A time-stratified case-crossover study was conducted at the individual level to explore the associations between NO_(2)and RDs.NO_(2)was significantly associated with increased hospitalization of eight major RDs(acute upper respiratory infections,influenza and pneumonia,acute lower respiratory infections,upper respiratory tract diseases,chronic lower respiratory diseases,respiratory interstitium diseases,pleura diseases,and other respiratory diseases)and 18 specific causes of RDs,with the largest associations observed on lag 0−1 day.The effect estimates ranged from 0.75 to 4.09%per 10μg/m^(3)of NO_(2)exposure.The associations remained robust after controlling for copollutants.The concentration−response curves were mostly positive and linear.This nationwide study provides comprehensive information on the acute effects of NO_(2)on respiratory morbidity across the full spectrum,highlighting the need for caution with regard to this important traffic-related air pollutant in current pollution control programs.
基金supported by the National Natural Science Foundation of China(grant numbers 92043301 and 91843302)the Shanghai International Science and Technology Partnership Project(grant number 21230780200).
文摘Few national studies have systemically examined the effects of criteria air pollutants on cardiovascular morbidity.This study aimed to investigate the associations between all criteria air pollutants and hospitalization of causespecific cardiovascular diseases(CVD)in China.We obtained data on CVD hospitalization events of four major categories and 12 specific diseases from 153 hospitals distributed in 20 provincial-level regions from 2013 to 2020.We adopted a time-stratified case-crossover study design using individual cases to capture the effect of short-term exposure to six criteria air pollutants on CVD hospitalizations,using conditional logistic regression models.More than 1.1 million CVD hospitalization events were included.The lag pattern exploration demonstrated the largest effect for six air pollutants on lag 0–1 day.PM_(2.5),PM_(10),NO_(2),and CO were significantly associated with increased hospitalization from ischemic heart diseases,cerebrovascular diseases,other heart diseases,and five specific causes of CVD.The effect estimates of NO_(2)were the most robust when adjusting for copollutants.The concentration-response curves were positive and linear for most pollutant–endpoint pairs(except for O_(3)),and these positive associations remained even below the 24-h levels recommended by WHO Air Quality Guidelines and China Air Quality Standards.This nationwide case-crossover study in China demonstrated that short-term exposure to multiple ambient air pollutants may significantly increase the risk of cause-specific CVD hospitalizations even under the most stringent air quality regulations,striking an alert for potential CVD patients against these environmental risk factors.
基金partly supported by the National Natural Science Foundation of China(No.11690015,11301355,11671275,11771431 and 71501016)Key Laboratory of RCSDS,CAS(No.2008DP173182)+1 种基金Qin Xin Talents Cultivation Program(QXTCP B201705)Beijing Information Science&Technology University
文摘Rare event data is encountered when the events of interest occur with low frequency, and the estimators based on the cohort data only may be inefficient. However, when external information is available for the estimation, the estimators utilizing external information can be more efficient. In this paper, we propose a method to incorporate external information into the estimation of the baseline hazard function and improve efficiency for estimating the absolute risk under the additive hazards model. The resulting estimators are shown to be uniformly consistent and converge weakly to Gaussian processes. Simulation studies demonstrate that the proposed method is much more efficient. An application to a bone marrow transplant data set is provided.
