Showing 4 results for Cox Proportional Hazards Model
B Moghimi Dehkordi, A Rajaeefard, Hr Tabatabaee, B Zeighami, A Safaee, Z Tabeie,
Volume 3, Issue 1 (9-2007)
Abstract
Background & Objectives: Cancer has been traditionally regarded as a fatal disease it is a major public health problem in many countries throughout the world. In recent years, cancer morbidity and mortality has increased in our country and notably stomach cancer now ranks second or third among all cancers types with regard to morbidity.
Methods: Our study included all gastric cancer patients registered in the cancer registry of Fars province. The patients' survival status was followed using phone calls and death records from hospitals, other medical centers, and the city's cemetery. Data analysis involved the use of the nonparametric Kaplan-Meier and Cox proportional hazards models and was performed with the software package SPSS V.13.
Results: Of the 442 patients with gastric cancer, 303 cases (68.6 percents) were male, and the mean age of patients was 58.41 years (SD=14.46). In univariate analysis with the KM method, a statistically significant association was found between survival rates and the following factors: age at diagnosis (P<0.001), tumor grade (P=0.009), presence of metastases (P<0.001), and type of the initial treatment (P=<0.001). Factors without a significant relationship with the survival rate included sex, ethnicity, weight, BMI, tobacco use, history of cancer in close or distant relatives, place of residence, number of children, marital status, occupation, and income. In Cox regression, only age at diagnosis, tumor grade, and the presence of metastases showed a significant association with survival rates.
Conclusions: Our results imply that early detection of cancer at a lower age and in lower tumor grades could be important for increasing the patients' life expectancy.
H Noorkojuri, E Hajizadeh, Ar Baghestani, Ma Pourhoseingholi,
Volume 9, Issue 2 (10-2013)
Abstract
Background & Objectives: Cox regression model is one of the statistical methods in survival analysis. The use of smoothing techniques in Cox model makes the more accurate estimates for the parameters. Fractional polynomial is one of these techniques in Cox model. The aim of this study was to assess the effects of prognostic factors on survival of patients with gastric cancer using the fractional polynomial in Cox model and Cox proportional hazards.
Methods: Information of total of 216 patients with gastric cancer who underwent surgery in the gastroenterology ward of Taleghani Hospital in Tehran between 2003 and 2008 were included in this retrospective study. In this research, fractional polynomial in Cox model and Cox proportional hazards model were utilized for determining the effects of prognostic factors on patients’ survival time with gastric cancer. The SPSS version 18.0 and R version 2.14.1 were used for data analysis. These models were compared with Akaike information criterion.
Results: The analysis of Cox proportional hazards and fractional polynomial models resulted in age at diagnosis and tumor size as prognostic factors on survival time of patients with gastric cancer independently (P<0.05). Also, Akaike information criterion was equal in both models.
Conclusion: In the present study, the Cox proportional hazards and fractional polynomial models led to similar results with equal Akaike information criterions. Using of smoothing methods helped us eliminate non-linear effects but it seemed more appropriate to use Cox proportional hazards model in medical data because of its’ ease of interpretation and capability of modeling in both continuous and discrete covariates.
S Kargarian Marvasti , J Abolghasemi, I Heydari , Sh Rimaz,
Volume 13, Issue 2 (9-2017)
Abstract
Background and Objectives: Neuropathy is a common complication of diabetes that can cause disability in diabetic patients. The aim of this study was to determine of effective factors in the Event Time of neuropathy in type 2 diabetic patients using the Cox proportional hazards model.
Methods: This study included 371 patients with type II diabetes without neuropathy who were registered at Fereydunshahr Diabetes Clinic. Subjects were followed up for the development of neuropathy between 2006 until March 2016. The data were analyzed using the R software (ver. 3.2.3). The test was conducted at an error level of 5%.
Results: At the end of 10 years of study, the cumulative incidence and prevalence of neuropathy was 30.7% and 41.6%, respectively. The Kaplan-Meier method showed the mean time to detection of neuropathy was 76.6 ± 5 months after the first diagnosis of diabetes (83.8 ± 8 in men and 72.7 ± 6 in women). The semi-parametric Cox regression model revealed the one-year, two-year, five-year, and eight-year disease-free survival was 0.867, 0.819, 0.647, and 0.527, respectively. Also, four variables of duration of diabetes, sex, family history of diabetes, and HbA1c can be considered as strong determinants of the time of development of neuropathy in the semi-parametric model (COX) (P<0.05).
Conclusion: Optimal glycemic control and regular evaluation of legs in elderly patients, especially women with a positive family history, decrease the occurrence and progression of neuropathy and improve the quality of life in diabetic patients.
Sadegh Kargarian-Marvasti , Malihe Hasannezhad , Jamile Abolghasemi,
Volume 17, Issue 2 (9-2021)
Abstract
Background and Objectives: This study aimed to investigate the effective factors in the survival/hazard time of Covid-19 patients in three waves of epidemic.
Methods: All 880 Covid-19 patients were included in this prospective cohort study using the census method. Polymerase chain reaction was used to diagnose Covid-19. The survival status of these patients was followed up for 4 months. The analysis of this study was based on the time of infection in three epidemic waves in IRAN. To analyze the data, the Kaplan-Meier nonparametric approach and Cox proportional hazards regression model were used. Survival distributions were compared in three epidemic waves using the R software (version 3.6.2) (P<0.05).
Results: We diagnosed 880 positive case of Covid-19 using PCR test on 2269 susspected people who had respiratory symptomps. At the end of 1-year follow-up, cumulative incidence (risk) of Covid-19 was estimated 7%. Effective factors in the survival time of patients with Covid-19 based on Cox multivariate regression model were: 1- Age 2- Intensity of infection (Hospitalization) 3- Heart disease 4- Epidemic Wave and 5- Transmission mode of the disease (P <0.05). The Kaplan-Meier approach and log rank test (Mantel-Cox) showed a significant difference in the survival rate in three epidemic waves (P = 0.018).
Conclusion: Elderly patients, especially those with a history of heart disease, are at higher risk of death than other groups. In addition to regular screening, these patients will need active monitoring, especially at the time of hospitalization.
