Introduction: Gastric cancer is the second leading cause of cancer death worldwide and is the most common type of cancer in Iran. The objectives of this study were to assess the effects of prognostic factors on survival of patients with gastric cancer using the Aalen additive hazards model, and to illustrate the advantage of Aalen’s plot.Materials and Methods: Information of total of 213 patients with gastric cancer who underwent surgery in the gastroenterology ward of Taleghani hospital in Tehran between 2003 and 2008 were included in this historical cohort study. Age at diagnosis, sex, presence of metastasis, tumor size, histology type, lymph node metastasis, and pathologic stages were entered into analysis using the Aalen additive hazard model. To visualize a covariate effect over time, the estimated cumulative regression function by the Aalen’s model is examined.Results: The univariate and multivariate analysis identified that age at diagnosis, tumor size and pathologic stage were independent prognostic factors for the survival of patients with gastric cancer (p<0.05). Moreover, pathologic stage has a late or delayed effect according to the Aalen’s plot. Other clinicopathological characteristics were not statistically significant (p>0.05).Conclusions: In spite of using Cox model in survival analysis by many researchers, Aalen’s model may yield new insights in prognostic studies of survival time of patients with gastric cancer over time. Our results suggest that early detection of patients in younger age and in primary stages is important to increase survival from gastric cancer.

Background: Survival rates for breast cancer (BC) are often based on the outcomes of this disease. The aim of this study was to compare the performance of three survival models, namely Cox regression, Aalen’ s, and Lin and Ying’ s additive hazards (AH) models for identifying the prognostic factors regarding the survival time of BC patients. Materials and Methods: This study was a historical cohort study which used 1025 females’ medical records that underwent modified radical mastectomy or breast saving. These patients were admitted to Besat and Chamran Hospitals, Tehran, Iran, during 2010– 2015 and followed until 2017. The Aalen’ s and Lin and Ying’ s AH models and also traditional Cox model were applied for analysis of time to death of BC patients using R 3. 5. 1 software. Results: In Aalen’ s and also Lin and Ying’ s AH models, age at diagnosis, history of disease, number of lymph nodes, metastasis, hormonal therapy, and evacuation lymph nodes were prognostic factors for the survival of BC patients (P < 0. 05). In addition, in the Lin and Ying’ s AH model tumor size (P = 0. 048) was also identified as a significant factor. According to Aalen’ s plot, metastasis, age at diagnosis, and number of lymph nodes had a time‑ varying effect on survival time. These variables had a different slope as the times go on. Conclusion: AH model may yield new insights in prognostic studies of survival time of patients with BC over time. Because of the positive slope of estimated cumulative regression function in Aalen’ s plot, metastasis, higher age at diagnosis, and high number of lymph nodes are important factors in reducing the survival BC, and then based on these factors, the therapists should consider a special therapeutic protocol for BC patients.

Background: Gastric cancer is a common and lethal disease throughout the world. This study was designed and carried out to determine the five-year survival rate of gastric cancer patients who had undergone surgical treatment at Taleghani Hospital of Tehran, and to assess its associated factors.Materials and Methods: In this historical-cohort study, information obtained from a total of 213 gastric cancer patients who underwent surgery at Taleghani Hospital of Tehran between 2003 and 2008 was included. In the analyses, Kaplan-Meier method, log-rank test, Cox proportional hazards model, and Lin-Ying additive hazards model were used.Results: The five-year survival rate and the median life expectancy in the studied patients were 14.6% and 29.6 months, respectively. Two covariates showed significant impacts on the gastric cancer patients’ data in both models: age at diagnosis and tumor size. We found that pathologic stage was significant under the Lin-Ying model (P=0.043); however, it was not significant under the Cox model (P=0.069). Other clinicopathological characteristics were not statistically significant (P>0.05).Conclusion: Since Cox and Lin-Ying models consider different aspects of the association between risk factors and the study outcome, it seems desirable to use both of them as complementary models but not as alternative models to obtain a more comprehensive understanding of data. This study showed that the early detection of patients in younger ages and in primary stages is important to decrease the risk of death in patients with gastric cancer and to increase the survival rate.

Nonparametric additive model is one of the common models for modeling the relationship between variables. In this paper, we consider the high-dimensional nonparametric additive model in which the number of explanatory variables can exceed the number of observations, but the number of important explanatory variables relative to the number of observations is small. When the number of explanatory variables in the model is large, model interpretation becomes more difficult and computational costs increase. Therefore, identifying the explanatory variables that have a significant impact on the response or non-zero additive components in this model is crucial. To this end, we first approximate the additive components using B-spline bases. By employing this approximation, the problem of variable selection is transformed into selecting groups of non-zero coefficients. Then, we use grouped penalty functions for selecting non-zero coefficients. This is usually done by minimizing the sum of squared errors subject to a constraint. Minimizing this target function requires the use of optimization methods. In this paper, we utilize a group descent algorithm to solve the aforementioned minimization problem. Finally, the performance of this algorithm is examined under three different penalty functions through simulation studies and analysis of a real dataset.

Introduction: Variable selection has become an increasingly important topic in biomedical research, as evidenced by its modern applications in high-throughput genomic data analysis. Specifically, interest in analyzing high-throughput data to link gene expression profiles to the timing of an event such as death has grown, with the goal of evaluating the influence of biomedical variables on survival outcomes. One common special case in survival data is competing risks data where identifying a small subset of gene expression profiles related to cumulative incidence function (CIF) is crucial. Methods: Several methods for directly modeling CIF are proposed, involving modeling the subdistribution hazard function of the interested cause or event using the proportional hazards approach. We proposed a regularized method for variable selection in the additive subdistribution hazards model by combining the nonconcave penalized likelihood approach and the pseudoscore method. We also conducted Monte Carlo simulations to evaluate the performance of our proposed method. In addition, a publicly available dataset was used to illustrate the proposed model. Results: Results from simulation studies were presented together with an application to genomic data when the endpoint is progression-free survival and the objective is to identify genes related to CIF of bladder cancer in the presence of competing events. Five genes in common (CDC20, PLEK, FCN2, IGF1R and DCTD) were identified by the proposed penalized additive subdistribution hazards model with different penalties. Conclusions: Monte Carlo simulation studies results suggested that the results of all penalties were comparable in terms of sensitivity and specificity, whereas those based on Adaptive Elastic Net (AENET) and Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) penalties tended to perform better in terms of estimation accuracy.

Introduction: Diabetes is a chronic disease, non-epidemic disease that costs a lot of money in each year. One of the diagnostic criteria for diabetes is Glycosylated Hemoglobin (HBA1C), which in this study the effective factors on it examined by additive regression model. Materials and Methods: In this cross-sectional study, 130 patients with diabetes type-2 were selected based on simple random sampling in Ilam city (Iran). Several variables were examined such as gender, age, weight, height, systolic and diastolic blood pressure, hypertension, smoking, family history of diabetes, daily walking for at least 30 minutes, waist and hip circumferences, HbA1c, fasting blood sugar (FBS), RBC mean corpuscular volume (MCV) and BMI. The data were collected based on Canadian diabetes checklist questionnaire. Results: In simple linear regression, waist and hip circumferences and in multiple regression, hip circumference and BMI had a significant effect on HBA1C (P<0. 05). Importantly, in simple additive regression waist, hip circumferences and fasting blood Sugar as well as in multiple additive regression waist, hip circumferences, fasting blood sugar and BMI had significant effects on HbA1C (P<0. 05). Conclusion: Additive regression model with 0. 878 adjusted R-squared and AIC equal to 603. 464 was better model for examining the influential factors on HbA1C compared with the multiple regression model with adjusted R-squared and AIC equal to 0. 386 and 844. 730, respectively.

Background: Titanium dioxide (TiO2) is employed in various forms, ranging from nano to macro, in food products and packaging to prolong shelf life. However, recent research has shown potential health risks associated with its use. This review investigates the health implications of TiO2 nanoparticles (NPs) in food packaging or additives, while also examining TiO2's antimicrobial properties and related mechanisms. Methods: The research extensively explored TiO2 NPs' generation methods and antimicrobial potential, especially in the context of food packaging and cosmetics. A systematic search was conducted using Google Scholar, Pub Med, and Web of Science databases to identify relevant sources. A total of 97 sources were selected from 150, without date restrictions. These references, spanning 1972 to 2023, encompass diverse full-text English materials, including reviews, original research, conferences, handbooks, and book chapters. Results: Nanotechnology, specifically TiO2 NPs, enhances food packaging for safety and sustainability. Innovations such as reinforced, active, and biodegradable packaging have emerged to address industry challenges, improving mechanical performance and extending shelf life. However, despite the benefits, concerns about the health and environmental implications of TiO2 NPs have prompted regulatory reassessment. Conclusion: Addressing concerns about TiO2 NPs in food packaging is crucial due to potential health and environmental risks. The recent ban imposed by the European Union on TiO2 (E171) underscores the need for ongoing research and scrutiny to ensure the safe integration of nanotechnology in food packaging.

The activities of people and societies have increased significantly over the past century and have had a great impact on the natural environment and the living environment of societies and on people collectively or individually. On the other hand, the effect of natural events on the activities of people and societies is increasing. Based on this, the nature of hazards science is to measure the risk effect of these activities and events. The method of producing this article is empirical and historical analysis.It contains the well-known collection of testimonies and complaints of the natural and social environment. Of course, the amount that the authors did not remain unaware of. The question of this article is, what kind of hazards have these activities and events caused, and based on those hazards what types of hazards science has? The answer to this question will cause hazards scientists to be fundamentally and organized and increasingly and more actively engaged in solving and managing social, individual and natural environmental hazards. On the other hand, the contribution of hazards science to the understanding of hazards will be developed. Also, other interested people from other fields of science, who have the opportunity to enter hazards science, should be guided to the field of risk science typology.

Background: One substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable selection methods in estimating the survival time.Methods: The data included 1381 gene expression measurements and clinical information from 301 patients with bladder cancer operated in the years 1987 to 2000 in hospitals in Denmark, Sweden, Spain, France, and England. Four methods of the least absolute shrinkage and selection operator, smoothly clipped absolute deviation, the smooth inte-gration of counting and absolute deviation and elastic net were utilized for simultaneous variable selection and estima-tion under an additive hazards model. The criteria of area under ROC curve, Brier score and c-index were used to compare the methods.Results: The median follow-up time for all patients was 47 months. The elastic net approach was indicated to outper-form other methods. The elastic net had the lowest integrated Brier score (0.137±0.07) and the greatest median of the over-time AUC and C-index (0.803±0.06 and 0.779±0.13, respectively). Five out of 19 selected genes by the elastic net were significant (P<0.05) under an additive hazards model. It was indicated that the expression of RTN4, SON, IGF1R and CDC20 decrease the survival time, while the expression of SMARCAD1 increase it.Conclusion: The elastic net had higher capability than the other methods for the prediction of survival time in pa-tients with bladder cancer in the presence of competing risks base on additive hazards model.