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
In clinical trials, the primary efficacy endpoint often corresponds to a so-called "composite endpoint". Composite endpoints combine several events of interest within a single outcome variable. Thereby it is...In clinical trials, the primary efficacy endpoint often corresponds to a so-called "composite endpoint". Composite endpoints combine several events of interest within a single outcome variable. Thereby it is intended to enlarge the expected effect size and thereby increase the power of the study. However, composite endpoints also come along with serious challenges and problems. On the one hand, composite endpoints may lead to difficulties during the planning phase of a trial with respect to the sample size calculation, asthe expected clinical effect of an intervention on the composite endpoint depends on the effects on its single components and their correlations. This may lead to wrong assumptions on the sample size needed. Too optimistic assumptions on the expected effect may lead to an underpowered of the trial, whereas a too conservatively estimated effect results in an unnecessarily high sample size. On the other hand, the interpretation of composite endpoints may be difficult, as the observed effect of the composite does not necessarily reflect the effects of the single components. Therefore the demonstration of the clinical efficacy of a new intervention by exclusively evaluating the composite endpoint may be misleading. The present paper summarizes results and recommendations of the latest research addressing the above mentioned problems in the planning, analysis and interpretation of clinical trials with composite endpoints, thereby providing a practical guidance for users.展开更多
In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale par...In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,Bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that Bayesian estimates have a good performance.展开更多
Setting: Four decentralised sites are located in rural areas and one centralised hospital in KwaZulu-Natal province, South Africa. Objective: To analyse risk factors associated with multidrug-resistant tuberculosis (M...Setting: Four decentralised sites are located in rural areas and one centralised hospital in KwaZulu-Natal province, South Africa. Objective: To analyse risk factors associated with multidrug-resistant tuberculosis (MDR-TB) using competing risks analysis. Understanding factors associated with MDR-TB and obtaining valid parameter estimates could help in designing control and intervention strategies to lower TB mortality. Method: A prospective study was performed using a competing risk analysis in patients receiving treatment for MDR-TB. The study focused on 1542 patients (aged 18 years and older) who were diagnosed of MDR-TB between July 2008 and June 2010. Time to cure MDR-TB was used as the dependent variable and time to death was the competing risk event. Results: The Fine-Gray regression model indicated that baseline weight was highly significant with sub-distribution hazard ration (SHR) = 1.02, 95% CI: 1.01 - 1.02. This means that weight gain in a month increased chances of curing MDR-TB by 2%. Results show that lower chances to cure MDR-TB were among patients between 41 to 50 years compared to those patients who were between 18 to 30 years old (SHR = 0.80, 95% CI: 0.61 - 1.06). The chances of curing MDR-TB in female patients were low compared to male patients (SHR = 0.84, 95% CI = 0.68 - 1.03), however this was not significant. Furthermore, HIV negative patients had higher chances to cure MDR-TB (SHR = 1.07, 95% CI: 0.85 - 1.35) compared to HIV positive patients. Patients who were treated in the decentralised sites had lower chances to be cured of MDR-TB (SHR = 0.19, 95% CI: 0.07 - 0.54) as compared to patients who were treated in the centralised hospital. Conclusion: Identifying key factors associated with TB and specifying strategies to prevent them can reduce mortality of patients due to TB disease, hence positive treatment outcomes leading to the goal of reducing or end TB deaths. Urgent action is required to improve the coverage and quality of diagnosis, treatment and care for people with drug-resistant展开更多
Purpose: Recent studies showed that African Americans (AA) breast cancer patients experience lower survival than any other race. The knowledge of cause-specific survival of such patients is necessary to investigate th...Purpose: Recent studies showed that African Americans (AA) breast cancer patients experience lower survival than any other race. The knowledge of cause-specific survival of such patients is necessary to investigate the different factors associated with the disease and support the clinical practice. Methods: The parametric competing risk method is applied to build up the survival models and the parametric mixture model is used to study the overall survival of these patients. The Kaplan-Meier survival estimation is also computed to compare the results. Results: The overall death rate decreases sharply immediately after the diagnosis and increases thereafter. The risk of death from breast cancer itself is the highest at the first five years;other causes, however, pose more threats to patients after this period. The patients who received only surgery have higher survival rate in long run. The use of radiation only does not have the significant effect on patients’ survival. Conclusion: Our study shows that the parametric competing risk models are promising in estimating the cause-specific survival of AA breast cancer patients and can be used for clinical practice. We also observed that heart and other diseases pose more threat to breast cancer patients in the long run.展开更多
AIM: To show a new paradigm of simultaneously testing whether breast cancer therapies impact other causes of death. METHODS: MA.14 allocated 667 postmenopausal women to 5 years of tamoxifen 20 mg/daily ± 2 years ...AIM: To show a new paradigm of simultaneously testing whether breast cancer therapies impact other causes of death. METHODS: MA.14 allocated 667 postmenopausal women to 5 years of tamoxifen 20 mg/daily ± 2 years of octreotide 90 mg, given by depot intramuscular injections monthly. Event-free survival was the primary endpoint of MA.14; at median 7.9 years, the tamoxifen+octreotide and tamoxifen arms had similar event-free survival(P = 0.62). Overall survival was a secondary endpoint, and the two trial arms also had similar overall survival(P = 0.86). We used the median 9.8 years follow-up to examine by intention-to-treat, the multivariate time-to-breast cancer-specific(Br Ca) and other cause(OC) mortality with log-normal survival analysis adjusted by treatment and stratification factors. We tested whether baseline factors including Insulin-like growth factor 1(IGF1), IGF binding protein-3, C-peptide, body mass index, and 25-OH vitamin D were associated with(1) all cause mortality, and if so; and(2) cause-specific mortality. We also fit step-wise forward cause-specific adjusted models.RESULTS: The analyses were performed on 329 patients allocated tamoxifen and 329 allocated tamoxifen+octreotide. The median age of MA.14 patients was 60.1 years: 447(82%) < 70 years and 120(18%) ≥ 70 years. There were 170 deaths: 106(62.3%) BrC a; 55(32.4%) OC, of which 24 were other malignancies, 31 other causes of death; 9(5.3%) patients with unknown cause of death were excluded from competing risk assessments. BrC a and OC deaths were not significantly different by treatment arm(P = 0.40): tamoxifen patients experienced 50 BrC a and 32 OC deaths, while tamoxifen + octreotide patients experienced 56 Br Ca and 23 OC deaths. Proportionately more deaths(P = 0.004) were from BrC a for patients< 70 years, where 70% of deaths were due to Br Ca, compared to 54% for those ≥ 70 years of age. The proportion of deaths from OC increased with increasing body mass index(BMI)(P = 0.02). Higher pathologic T and N were associated with more BrC 展开更多
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.
文摘In clinical trials, the primary efficacy endpoint often corresponds to a so-called "composite endpoint". Composite endpoints combine several events of interest within a single outcome variable. Thereby it is intended to enlarge the expected effect size and thereby increase the power of the study. However, composite endpoints also come along with serious challenges and problems. On the one hand, composite endpoints may lead to difficulties during the planning phase of a trial with respect to the sample size calculation, asthe expected clinical effect of an intervention on the composite endpoint depends on the effects on its single components and their correlations. This may lead to wrong assumptions on the sample size needed. Too optimistic assumptions on the expected effect may lead to an underpowered of the trial, whereas a too conservatively estimated effect results in an unnecessarily high sample size. On the other hand, the interpretation of composite endpoints may be difficult, as the observed effect of the composite does not necessarily reflect the effects of the single components. Therefore the demonstration of the clinical efficacy of a new intervention by exclusively evaluating the composite endpoint may be misleading. The present paper summarizes results and recommendations of the latest research addressing the above mentioned problems in the planning, analysis and interpretation of clinical trials with composite endpoints, thereby providing a practical guidance for users.
基金Supported by the National Natural Science Foundation of China（71571144,71401134,71171164,11701406） Supported by the International Cooperation and Exchanges in Science and Technology Program of Shaanxi Province（2016KW-033）
文摘In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,Bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that Bayesian estimates have a good performance.
文摘Setting: Four decentralised sites are located in rural areas and one centralised hospital in KwaZulu-Natal province, South Africa. Objective: To analyse risk factors associated with multidrug-resistant tuberculosis (MDR-TB) using competing risks analysis. Understanding factors associated with MDR-TB and obtaining valid parameter estimates could help in designing control and intervention strategies to lower TB mortality. Method: A prospective study was performed using a competing risk analysis in patients receiving treatment for MDR-TB. The study focused on 1542 patients (aged 18 years and older) who were diagnosed of MDR-TB between July 2008 and June 2010. Time to cure MDR-TB was used as the dependent variable and time to death was the competing risk event. Results: The Fine-Gray regression model indicated that baseline weight was highly significant with sub-distribution hazard ration (SHR) = 1.02, 95% CI: 1.01 - 1.02. This means that weight gain in a month increased chances of curing MDR-TB by 2%. Results show that lower chances to cure MDR-TB were among patients between 41 to 50 years compared to those patients who were between 18 to 30 years old (SHR = 0.80, 95% CI: 0.61 - 1.06). The chances of curing MDR-TB in female patients were low compared to male patients (SHR = 0.84, 95% CI = 0.68 - 1.03), however this was not significant. Furthermore, HIV negative patients had higher chances to cure MDR-TB (SHR = 1.07, 95% CI: 0.85 - 1.35) compared to HIV positive patients. Patients who were treated in the decentralised sites had lower chances to be cured of MDR-TB (SHR = 0.19, 95% CI: 0.07 - 0.54) as compared to patients who were treated in the centralised hospital. Conclusion: Identifying key factors associated with TB and specifying strategies to prevent them can reduce mortality of patients due to TB disease, hence positive treatment outcomes leading to the goal of reducing or end TB deaths. Urgent action is required to improve the coverage and quality of diagnosis, treatment and care for people with drug-resistant
基金The project was supported by the National Natural Science Foundation(Grant Nos.7117116471401134+1 种基金7157114411701406),the Natural Science Basic Research Program of Shaanxi Province(GrantNo.2015JM1003).
文摘Purpose: Recent studies showed that African Americans (AA) breast cancer patients experience lower survival than any other race. The knowledge of cause-specific survival of such patients is necessary to investigate the different factors associated with the disease and support the clinical practice. Methods: The parametric competing risk method is applied to build up the survival models and the parametric mixture model is used to study the overall survival of these patients. The Kaplan-Meier survival estimation is also computed to compare the results. Results: The overall death rate decreases sharply immediately after the diagnosis and increases thereafter. The risk of death from breast cancer itself is the highest at the first five years;other causes, however, pose more threats to patients after this period. The patients who received only surgery have higher survival rate in long run. The use of radiation only does not have the significant effect on patients’ survival. Conclusion: Our study shows that the parametric competing risk models are promising in estimating the cause-specific survival of AA breast cancer patients and can be used for clinical practice. We also observed that heart and other diseases pose more threat to breast cancer patients in the long run.
基金Supported by the Canadian Cancer Society through a grant to the NCIC Clinical Trials Group from the Canadian Cancer Society Research InstituteNovartis provided the NCIC CTG MA.14 drug octreotide LAR
文摘AIM: To show a new paradigm of simultaneously testing whether breast cancer therapies impact other causes of death. METHODS: MA.14 allocated 667 postmenopausal women to 5 years of tamoxifen 20 mg/daily ± 2 years of octreotide 90 mg, given by depot intramuscular injections monthly. Event-free survival was the primary endpoint of MA.14; at median 7.9 years, the tamoxifen+octreotide and tamoxifen arms had similar event-free survival(P = 0.62). Overall survival was a secondary endpoint, and the two trial arms also had similar overall survival(P = 0.86). We used the median 9.8 years follow-up to examine by intention-to-treat, the multivariate time-to-breast cancer-specific(Br Ca) and other cause(OC) mortality with log-normal survival analysis adjusted by treatment and stratification factors. We tested whether baseline factors including Insulin-like growth factor 1(IGF1), IGF binding protein-3, C-peptide, body mass index, and 25-OH vitamin D were associated with(1) all cause mortality, and if so; and(2) cause-specific mortality. We also fit step-wise forward cause-specific adjusted models.RESULTS: The analyses were performed on 329 patients allocated tamoxifen and 329 allocated tamoxifen+octreotide. The median age of MA.14 patients was 60.1 years: 447(82%) < 70 years and 120(18%) ≥ 70 years. There were 170 deaths: 106(62.3%) BrC a; 55(32.4%) OC, of which 24 were other malignancies, 31 other causes of death; 9(5.3%) patients with unknown cause of death were excluded from competing risk assessments. BrC a and OC deaths were not significantly different by treatment arm(P = 0.40): tamoxifen patients experienced 50 BrC a and 32 OC deaths, while tamoxifen + octreotide patients experienced 56 Br Ca and 23 OC deaths. Proportionately more deaths(P = 0.004) were from BrC a for patients< 70 years, where 70% of deaths were due to Br Ca, compared to 54% for those ≥ 70 years of age. The proportion of deaths from OC increased with increasing body mass index(BMI)(P = 0.02). Higher pathologic T and N were associated with more BrC
