This study investigates the application of the two-parameter Weibull distribution in modeling state holding times within HIV/AIDS progression dynamics. By comparing the performance of the Weibull-based Accelerated Fai...This study investigates the application of the two-parameter Weibull distribution in modeling state holding times within HIV/AIDS progression dynamics. By comparing the performance of the Weibull-based Accelerated Failure Time (AFT) model, Cox Proportional Hazards model, and Survival model, we assess the effectiveness of these models in capturing survival rates across varying gender, age groups, and treatment categories. Simulated data was used to fit the models, with model identification criteria (AIC, BIC, and R2) applied for evaluation. Results indicate that the AFT model is particularly sensitive to interaction terms, showing significant effects for older age groups (50 - 60 years) and treatment interaction, while the Cox model provides a more stable fit across all age groups. The Survival model displayed variability, with its performance diminishing when interaction terms were introduced, particularly in older age groups. Overall, while the AFT model captures the complexities of interactions in the data, the Cox model’s stability suggests it may be better suited for general analyses without strong interaction effects. The findings highlight the importance of model selection in survival analysis, especially in complex disease progression scenarios like HIV/AIDS.展开更多
Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent s...Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.展开更多
This study has provided a starting point for defining and working with Cox models in respect of multivariate modeling. In medical researches, there may be situations, where several risk factors potentially affect pati...This study has provided a starting point for defining and working with Cox models in respect of multivariate modeling. In medical researches, there may be situations, where several risk factors potentially affect patient prognosis, howbeit, only one or two might predict patient’s predicament. In seeking to find out which of the risk factors contribute the most to the survival times of patients, there was the need for researchers to adjust the covariates to realize their impact on survival times of patients. Aside the multivariate nature of the covariates, some covariates might be categorical while others might be quantitative. Again, there might be cases where researchers need a model that has <span style="font-family:Verdana;">the capability of extending survival analysis methods to assessing simulta</span><span style="font-family:Verdana;">neously the effect of several risk factors on survival times. This study unveiled the Cox model as a robust technique which could accomplish the aforementioned cases.</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">An investigation meant to evaluate the ITN-factor vis-à-vis its </span><span style="font-family:Verdana;">contribution towards death due to Malaria was exemplified with the Cox model. Data were taken from hospitals in Ghana. In doing so, we assessed hospital in-patients who reported cases of malaria (origin state) to time until death or censoring (destination stage) as a result of predictive factors (exposure to the malaria parasites) and some socioeconomic variables. We purposefully used Cox models to quantify the effect of the ITN-factor in the presence of other risk factors to obtain some measures of effect that could describe the rela</span><span style="font-family:Verdana;">tionship between the exposure variable and time until death adjusting for</span><span style="font-family:Verdana;"> other variables. PH assumption holds for all three covariates. Sex of patient was insignificant to deaths due to malaria. Age of patient and user status </span></span><span style="font-family:Verdana;">were</span><span style="font-family:Verdana;"> both significant. The magnitude of the coefficient (0.384) of ITN user status depicts its high contribution to the variation in the dependent variable.</span>展开更多
Starting with the Aalen （1989） version of Cox （1972） ＇regression model＇ we show the method for construction of ＂any＂ joint survival function given marginal survival functions. Basically, however, we restrict o...Starting with the Aalen （1989） version of Cox （1972） ＇regression model＇ we show the method for construction of ＂any＂ joint survival function given marginal survival functions. Basically, however, we restrict ourselves to model positive stochastic dependences only with the general assumption that the underlying two marginal random variables are centered on the set of nonnegative real values. With only these assumptions we obtain nice general characterization of bivariate probability distributions that may play similar role as the copula methodology. Examples of reliability and biomedical applications are given.展开更多
OBJECTIVE To retrospectively analyze clinical data of patientsfrom our hospital who underwent radical surgery for esophagealcarcinoma and for adenocarcinoma of the gastric cardia,as well asto investigate prognostic fa...OBJECTIVE To retrospectively analyze clinical data of patientsfrom our hospital who underwent radical surgery for esophagealcarcinoma and for adenocarcinoma of the gastric cardia,as well asto investigate prognostic factors affecting the long-term survival ofthe patients.METHODS Data from the patients eligible for our study,admitted to the 4th Hospital of Hebei Medical University fromJanuary 1996 to December 2004,were randomized,and 12distinctive clinicopathologic factors influencing the survival rateof those who underwent radical surgery for esophageal carcinomaor carcinoma of the gastric cardia were collected.Univariate andmultivariate analysis of these individual variables were performedusing the Cox proportional hazard model.RESULTS It was shown by univariate analysis that age,tumorsize,pathologic type,lymph node status,TNM staging,depthof infiltration and encroachment into local organs,etc.,were thefactors that markedly influenced the prognosis of patients(P<0.01).Multivariate analysis showed that pathologic type,numberof the lymph node metastases,involvement of local organs,andTNM staging were independent prognostic factors(P<0.05).CONCLUSION The independent factors influencing theprognosis of patients with esophageal cancer and carcinoma ofthe gastric cardia include pathologic type,number of lymph nodemetastases,involvement of local organs and TNM staging.Themain prognostic factors affecting the patient's survival are patientage,tumor size and depth of infiltration.In addition,patients withinvolvement of the local organs have a worse prognosis,and theyshould be closely followed up.展开更多
文摘This study investigates the application of the two-parameter Weibull distribution in modeling state holding times within HIV/AIDS progression dynamics. By comparing the performance of the Weibull-based Accelerated Failure Time (AFT) model, Cox Proportional Hazards model, and Survival model, we assess the effectiveness of these models in capturing survival rates across varying gender, age groups, and treatment categories. Simulated data was used to fit the models, with model identification criteria (AIC, BIC, and R2) applied for evaluation. Results indicate that the AFT model is particularly sensitive to interaction terms, showing significant effects for older age groups (50 - 60 years) and treatment interaction, while the Cox model provides a more stable fit across all age groups. The Survival model displayed variability, with its performance diminishing when interaction terms were introduced, particularly in older age groups. Overall, while the AFT model captures the complexities of interactions in the data, the Cox model’s stability suggests it may be better suited for general analyses without strong interaction effects. The findings highlight the importance of model selection in survival analysis, especially in complex disease progression scenarios like HIV/AIDS.
文摘Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.
文摘This study has provided a starting point for defining and working with Cox models in respect of multivariate modeling. In medical researches, there may be situations, where several risk factors potentially affect patient prognosis, howbeit, only one or two might predict patient’s predicament. In seeking to find out which of the risk factors contribute the most to the survival times of patients, there was the need for researchers to adjust the covariates to realize their impact on survival times of patients. Aside the multivariate nature of the covariates, some covariates might be categorical while others might be quantitative. Again, there might be cases where researchers need a model that has <span style="font-family:Verdana;">the capability of extending survival analysis methods to assessing simulta</span><span style="font-family:Verdana;">neously the effect of several risk factors on survival times. This study unveiled the Cox model as a robust technique which could accomplish the aforementioned cases.</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">An investigation meant to evaluate the ITN-factor vis-à-vis its </span><span style="font-family:Verdana;">contribution towards death due to Malaria was exemplified with the Cox model. Data were taken from hospitals in Ghana. In doing so, we assessed hospital in-patients who reported cases of malaria (origin state) to time until death or censoring (destination stage) as a result of predictive factors (exposure to the malaria parasites) and some socioeconomic variables. We purposefully used Cox models to quantify the effect of the ITN-factor in the presence of other risk factors to obtain some measures of effect that could describe the rela</span><span style="font-family:Verdana;">tionship between the exposure variable and time until death adjusting for</span><span style="font-family:Verdana;"> other variables. PH assumption holds for all three covariates. Sex of patient was insignificant to deaths due to malaria. Age of patient and user status </span></span><span style="font-family:Verdana;">were</span><span style="font-family:Verdana;"> both significant. The magnitude of the coefficient (0.384) of ITN user status depicts its high contribution to the variation in the dependent variable.</span>
文摘Starting with the Aalen （1989） version of Cox （1972） ＇regression model＇ we show the method for construction of ＂any＂ joint survival function given marginal survival functions. Basically, however, we restrict ourselves to model positive stochastic dependences only with the general assumption that the underlying two marginal random variables are centered on the set of nonnegative real values. With only these assumptions we obtain nice general characterization of bivariate probability distributions that may play similar role as the copula methodology. Examples of reliability and biomedical applications are given.
基金supported by the Hebei Provincial Program for the Subjects with High Scholarship and Creative Research Potential,China.
文摘OBJECTIVE To retrospectively analyze clinical data of patientsfrom our hospital who underwent radical surgery for esophagealcarcinoma and for adenocarcinoma of the gastric cardia,as well asto investigate prognostic factors affecting the long-term survival ofthe patients.METHODS Data from the patients eligible for our study,admitted to the 4th Hospital of Hebei Medical University fromJanuary 1996 to December 2004,were randomized,and 12distinctive clinicopathologic factors influencing the survival rateof those who underwent radical surgery for esophageal carcinomaor carcinoma of the gastric cardia were collected.Univariate andmultivariate analysis of these individual variables were performedusing the Cox proportional hazard model.RESULTS It was shown by univariate analysis that age,tumorsize,pathologic type,lymph node status,TNM staging,depthof infiltration and encroachment into local organs,etc.,were thefactors that markedly influenced the prognosis of patients(P<0.01).Multivariate analysis showed that pathologic type,numberof the lymph node metastases,involvement of local organs,andTNM staging were independent prognostic factors(P<0.05).CONCLUSION The independent factors influencing theprognosis of patients with esophageal cancer and carcinoma ofthe gastric cardia include pathologic type,number of lymph nodemetastases,involvement of local organs and TNM staging.Themain prognostic factors affecting the patient's survival are patientage,tumor size and depth of infiltration.In addition,patients withinvolvement of the local organs have a worse prognosis,and theyshould be closely followed up.
