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Objectives EQ-5D is the most commonly used generic preference-based health-related quality of life (HRQoL) measure. The current study aimed at estimating the HRQoL index scores using EQ-5D-5 L measure in the capital of Iran; moreover, identifying some determinants of the HRQoL. Methods A sample of 3060 subjects was selected by a stratified random sampling method from the general adult population of Tehran. Face-to-face interview was conducted to fill out the questionnaire, in this cross-sectional survey. EQ-5D-5 L utility score were estimated using an interim value set, based on a crosswalk methodology. Additionally, the relationships between HRQoL and sociodemographic characteristics were tested by generalized linear model, using STATA version 13. Results The mean ± standard deviation utility and EQ-VAS scores were 0.79 ± 0.17 and 71.72 ± 19.37. The utility scores ranged 0.61 ± 0.19 in > 69 year-old females to 0.88 ± 0.12 in < 30 year-old males. In mobility, self-care, and usual activity dimensions, most of the respondents reported “no problems” (70.47, 90.62, and 76.34%, respectively). However, in anxiety/depression and pain/discomfort dimensions, most of the respondents had problems (53.23 and 54.03%, respectively). Females had lower utility score than males; the utility score reduced with age increase; the educational level lead to higher utility scores; and the utility scores of individuals without spouse (divorced or widowed) were lower than those of the married individuals and never married ones. Conclusions The current study reported HRQoL norm data for the general adult population in the capital of Iran; these data could be very useful for policy making and economic evaluations. A significant percentage of people in Tehran reported anxiety/ depression, which highlights the risk of psychological problems. Effective interventions are needed to increase their HRQoL, especially for the vulnerable groups of the community.

The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

Background In some survival studies, longitudinal biomarkers, along with baseline covariates, play crucial roles in predicting patient survival. Dynamic prediction models that incorporate updated longitudinal marker information offer updated survival predictions for patients. In this study, we employ a combination of the nonparametric gradient boosting machine learning algorithm and the landmark approach, which not only facilitates dynamic prediction but also circumvents the limitations of classical methods. Methods We conducted two simulation studies under different scenarios to compare three dynamic prediction models: the joint model, the Cox landmarking model, and the Landmarking Gradient Boosting Model (LGBM). We compared the three dynamic survival prediction methods using AUC (Area Under the Curve) and Brier score metrics. Using the LGBM, we performed dynamic prediction at various landmark times on a real kidney transplant dataset in the presence of two longitudinal markers. Results Simulation studies demonstrated that when there was a simple linear relationship between longitudinal markers and the survival process, the joint model outperformed both Cox landmarking and LGBM in terms of higher AUC (better discrimination) and lower Brier score (better overall performance) indices. Conversely, in scenarios characterized by complex and nonlinear relationships between longitudinal markers and the survival process, the LGBM outperformed the two classical methods, under conditions involving larger sample sizes ( n  = 1000, 1500 vs. n  = 300, 650), higher censoring rates (90% vs. 30%, 50%), and later landmark times (3.5, 5, 6.5 vs. 0.5, 2). The application of LGBM to real kidney transplant data revealed that at early landmark time points, factors such as blood urea nitrogen (BUN) (variable importance [VIMP] = 0.34), age (VIMP = 0.26), creatinine (VIMP = 0.24), hypertension (VIMP = 0.10), and gender (VIMP = 0.06) were associated with the risk of kidney transplant failure. At subsequent landmark time points, creatinine, BUN, and age emerged as the most important factors associated with kidney allograft failure. Conclusions Our findings demonstrate that in situations where the relationships between variables are complex and the proportional hazards assumption does not hold, the LGBM method performs better than Cox landmarking and joint modeling for dynamic survival prediction in cases with large sample sizes, high censoring rates, and later landmark times. Clinical trial number Not applicable.

Hematopoietic stem cell transplantation (HSCT) emerged over sixty years ago as a groundbreaking and potentially curative treatment for patients with acute myeloid leukemia (AML) who were not responding to chemotherapy. In this study, we aimed to investigate prognostic factors for survival after allo-HSCT in AML patients. This retrospective cohort study was carried out using data from 742 adult AML patients underwent allo-HSCT. we analysis prognostic factors for survival after allo-HSCT with censored quantile regression model. The 5-year OS, DFS and GRFS rates were 58, 53, and 30%, respectively. OS for recipients older than 35 years was 0.95 and 1.12 years lower than that for recipients under 35 years in the 25th and 40th percentiles, respectively. Compared to patients in their CRІ, those with CRІІІ disease experienced a decrease in OS at the 25th and 40th percentiles by 1.72 and 3.72 years, respectively. Moreover, OS for ABO matched patients was 0.92 and 1.29 years longer than that of patients with an ABO major mismatch. This study could assist oncologists and hematologists in understanding the prognostic factors affecting patient survival across various survival ranges, thereby potentially extending patients’ lifespans.

Background Censorship is the primary challenge in survival modeling, especially in human health studies. The classical methods have been limited by applications like Kaplan–Meier or restricted assumptions like the Cox regression model. On the other hand, Machine learning algorithms commonly rely on the high dimensionality of data and ignore the censorship attribute. In addition, these algorithms are more sophisticated to understand and utilize. We propose a novel approach based on the Bayesian network to address these issues. Methods We proposed a two-slice temporal Bayesian network model for the survival data, introducing the survival and censorship status in each observed time as the dynamic states. A score-based algorithm learned the structure of the directed acyclic graph. The likelihood approach conducted parameter learning. We conducted a simulation study to assess the performance of our model in comparison with the Kaplan–Meier and Cox proportional hazard regression. We defined various scenarios according to the sample size, censoring rate, and shapes of survival and censoring distributions across time. Finally, we fit the model on a real-world dataset that includes 760 post gastrectomy surgery due to gastric cancer. The validation of the model was explored using the hold-out technique based on the posterior classification error. Our survival model performance results were compared using the Kaplan–Meier and Cox proportional hazard models. Results The simulation study shows the superiority of DBN in bias reduction for many scenarios compared with Cox regression and Kaplan–Meier, especially in the late survival times. In the real-world data, the structure of the dynamic Bayesian network model satisfied the finding from Kaplan–Meier and Cox regression classical approaches. The posterior classification error found from the validation technique did not exceed 0.04, representing that our network predicted the state variables with more than 96% accuracy. Conclusions Our proposed dynamic Bayesian network model could be used as a data mining technique in the context of survival data analysis. The advantages of this approach are feature selection ability, straightforward interpretation, handling of high-dimensional data, and few assumptions.

Background: Using data from different health centers can provide more accurate knowledge of the survival prognostic factors and their effect on the patient’s survival. In this multicenter study, we aimed to investigate the role of prognostic factors on breast cancer survival with large data set. Methods: This historical cohort study was carried out using data from 1785 participants with breast cancer. Data were gathered from medical records of patients referring to 4 breast cancer research centers in Tehran, Iran, between 1997 and 2013. Age at diagnosis (year), size of the tumor, involve lymph nodes, tumor grade, type of surgery, auxiliary treatment of chemotherapy, radiotherapy, recurrence, and metastasis were the prognosis factors considered in this study. A shared frailty model with a gamma distribution for frailty term was used. Results: The median follow-up period was 29.71 months with the interquartile range of 19 to 61 months. During the follow-up period, 337 (18.9%) patients died from breast cancer and 1448 (81.1%) survived. The 1-, 3-, 5-, and 10-year survival rates were 96%, 84%, 76%, and 58%, respectively. In the Cox model by centers, in Center A, the type of surgery, number of nodes involved, and the grade 3 tumor; in center B, age, radiotherapy, metastasis, and between 1 and 3 involved nodes; in center C, age, radiotherapy, recurrence, metastasis, tumor size, and grade 3 tumor; and in center D, chemotherapy, metastasis, and lymph nodes involved were significant. Shared frailty model showed that type of surgery, number of lymph nodes involved, metastasis, radiotherapy, and the tumor grade are the prognostic factors survival in breast cancer. The frailty variance was significant, and it affirmed there was significant variability between centers. Conclusions: This study showed it is necessary to consider the frailty term in modeling multicenter survival studies and confirmed the importance of early diagnosis of cancer before the involvement of lymph nodes and the onset of metastasis and timely treatment could lead to longer life and increased quality of life for patients.

An amendment to this paper has been published and can be accessed via the original article.

Background The prognostic factors of survival can be accurately identified using data from different health centers, but the structure of multi-center data is heterogeneous due to the treatment of patients in different centers or similar reasons. In survival analysis, the shared frailty model is a common way to analyze multi-center data that assumes all covariates have homogenous effects. We used a censored quantile regression model for clustered survival data to study the impact of prognostic factors on survival time. Methods This multi-center historical cohort study included 1785 participants with breast cancer from four different medical centers. A censored quantile regression model with a gamma distribution for the frailty term was used, and p-value less than 0.05 considered significant. Results The 10th and 50th percentiles (95% confidence interval) of survival time were 26.22 (23–28.77) and 235.07 (130–236.55) months, respectively. The effect of metastasis on the 10th and 50th percentiles of survival time was 20.67 and 69.73 months, respectively (all p-value < 0.05). In the examination of the tumor grade, the effect of grades 2 and 3 tumors compare with the grade 1 tumor on the 50th percentile of survival time were 22.84 and 35.89 months, respectively (all p-value < 0.05). The frailty variance was significant, which confirmed that, there was significant variability between the centers. Conclusions This study confirmed the usefulness of a censored quantile regression model for cluster data in studying the impact of prognostic factors on survival time and the control effect of heterogeneity due to the treatment of patients in different centers.

Background Diseases have undeniable effects on Health-Related Quality of Life (HRQoL). Chronic diseases, in particular, limit the productive potentials and HRQoL of individuals. EQ-5D is a very popular generic instrument, which can be used to estimate HRQoL scores in any diseases. The current study investigates mean HRQoL scores in certain chronic diseases and examines the relationship between utility scores and chronic diseases in Iran. Method This cross-sectional study was carried out among the general adult population of Tehran. 3060 individuals were chosen by a stratified probability sampling method. The EQ-5D-5L questionnaire was applied. The utility scores were estimated using the Iranian crosswalk-based value set. The effect of chronic diseases on the HRQoL scores was derived by the Ordinary Least Squares (OLS) method. Data was analyzed using Stata version 13 software. Results The mean ± standard deviation utility and EQ-VAS scores were 0.85 ± 0.14 and 76.73 ± 16.55 in the participants without any chronic conditions. The scores were 0.69 ± 0.17 and 61.14 ± 20.61 in the participants with chronic conditions. The highest and lowest mean utility scores were related to thyroid disease (0.70) and Stroke (0.54), respectively. Common chronic conditions had significant negative effects on the HRQoL scores. Stroke (0.204) and cancer (0.177) caused the most reduction in the EQ-5D-5L utility scores. Lumbar disc hernia, digestive diseases, osteoarthritis, breathing problems, and anxiety/nerves cause 0.133, 0.109, 0.108, 0.087, and 0.078 reductions, respectively, in the EQ-5D-5L utility scores. Conclusion This study provides insight into some common chronic conditions and their effects on the HRQoL. Policymakers and planners should pay attention to the effects of chronic conditions especially high prevalence one. They should adopt effective interventions to control this issue and increase health. The results of this study can also be beneficial in economic evaluation studies.

Background: Toxoplasma gondii infects nearly one-third of the world's population. Due to the significant side effects of current treatment options, identifying safe and effective therapies seems crucial. Nanoparticles (NPs) are new promising compounds in treating pathogenic organisms. Currently, no research has investigated the effects of zinc oxide NPs (ZnO-NPs) on Toxoplasma parasite. We aimed to investigate the therapeutic efficacy of ZnO-NPs against tachyzoite forms of T. gondii, RH strain in BALB/c mice. Methods: In an experiment with 35 female BALB/c mice infected with T. gondii tachyzoites, colloidal ZnO-NPs at concentrations of 10, 20, and 50 ppm, as well as a 50 ppm ZnO solution and a control group, were orally administered four hours after inoculation and continued daily until the mices’ death. Survival rates were calculated and tachyzoite counts were evaluated in the peritoneal fluids of infected mice. Results: The administration of ZnO-NPs resulted in the reduction of tachyzoite counts in infected mice compared to both the ZnO-treated and control group (P<0.001). Intervention with ZnO-NPs significantly increased the survival time compared to the control group (6.2±0.28 days, P-value <0.05), additionally, the highest dose of ZnO-NPs (50 ppm) showed the highest mice survival time (8.7±0.42 days). Conclusion: ZnO-NPs were effective in decreasing the number of tachyzoites and increasing mice survival time in vivo. Moreover, there were no significant differences in survival time between the untreated control group and the group treated with zinc oxide, suggesting that, bulk ZnO is not significantly effective in comparison with ZnO-NPs.