Showing 3 results for Roshanaei
M Safari, M Sadeghifar, Gh Roshanaei , A Zahiri,
Volume 14, Issue 2 (Vol.14, No.2, 2018)
Abstract
Background and Objectives: Tuberculosis is a chronic bacterial disease and a major cause of morbidity and mortality. It is caused by a Mycobacterium tuberculosis. Awareness of the incidence and number of new cases of the disease is valuable information for revising the implemented programs and development indicators. time series and regression are commonly used models for prediction but these methods require some assumptions. The purpose of this study was to predict new TB cases using the hidden Markov model which does not require many assumption.
Methods: The data used in this study was the monthly number of new TB cases during 2006-2016 identified and recorded in Hamedan Province. Rorecasting the number of new TB cases was done using hidden Markov models using the hidden Markov package in the R software.
Results: According to the AIC and BIC criterion, two states had the best fit to the data, i.e. the data of this study were a mixture of two Poisson distributions with average number of event 5.96 and 10.2 respectively. The results also predicted the number of new cases over the next 24 months based on the hidden Markov model would be between 8 and 9 new cases in each month.
Conclusion: The hidden Markov model is the best model for prediction using the Markov chain. This model, in addition to detection of an appropriate model for the available data, can determine the transition probability matrix, which can help physicians predict the future state of the disease and take preventive measures befor reaching advanced stages.
M Safari, M Abbasi, F Gohari Ensaf , Z Berangi, Gh Roshanaei,
Volume 15, Issue 4 (Vol.15, No.4 2020)
Abstract
Background and Objectives: In survival analysis, using the Cox model to determine the effective factors requires the assumptions whose failure of leads to biased results. The aim of this paper was to determine the factors affecting the survival of metastatic gastric cancer patients using the non-parametric method of Randomized Survival Forest (RSF) model and to compare its result with the Cox model.
Methods: In this retrospective cohort study, 201 patients with metastatic gastric cancer were evaluated in Hamadan Province. Patient survival was calculated from diagnosis to death or end of study. Demographic characteristics (such as gender and age) and clinical variables (including stage, tumor size, etc.) were extracted from the patient records. Factors affecting survival were determined using the Cox model and RSF. Data analysis was performed using the R3.4.3 software and RandomForestSRC and survival packages.
Results: The mean (SD) age of patients was 61.5 (12.9) years old. The Cox model showed that chemotherapy (p=0.033) was effective in survival, and the results of fitting the RSF model showed that the most important variables affecting survival were type of surgery, location of metastasis, chemotherapy, age, tumor grade, surgery, number of involved lymph nodes, sex and radiotherapy. Based on the model appropriateness, the RSF model with log-rank split rule had a better performance compared to the Cox model.
Conclusion: If the number of variables is high and there is a relationship between the variables, the RSF method identifies the important and effective variables on survival with high accuracy without requiring restrictive assumptions compared to the Cox model.
Malihe Safari, Salman Khazaei, , Mohammad Abbasi, Ghodratollah Roshanaei,
Volume 17, Issue 2 (Vol 17,No.2, Summer 2021 2021)
Abstract
Background and Objectives: The incidence of rectal cancer is increasing in developing societies, especially in younger age groups. The aim of this study was to evaluate the factors affecting the survival of patients with rectal cancer in the presence of competing risks.
Methods: In this retrospective cohort study, the data of 121 patients with rectal cancer during 2001-2017 were studied. Death related to cancer progression was considered as the interest outcome and other causes of death were considered as competing risks. Cause-specific and sub-distribution hazard models were used to investigate the factors affecting patient survival in the presence of competing risk.
Results: The mean (SD) age of the patients was 53.4 (13.9) years and 68 patients (56.2%) were male. The results of log-rank test showed that sex, age, metastasis, type of first treatment, rate of penetration into intestinal wall, tumor location, number of lymphomas involved and tumor size had significant effects on the patient survival (P<0.05). Based on cause-specific and sub-distribution hazard models, tumor stage, lymph node metastasis, and tumor grade had significant effects on death hazard due to the cancer progression (P<0.05).
Conclusion: Due to the need to consider competing risks, the results of both competing risk methods showed that tumor grade, lymph node metastasis and stage increased the instantaneous hazard and hazard of cancer death. Therefore, to determine the specific risk factors for each cause of death in the survival analysis, competing risk methods should be used if there is more than one cause of death.
