Statistical shape analysis of maxillary palatal morphology in patients with palatally displaced canines.

OBJECTIVE: Maxillary morphology has long been a subject of interest due to its possible impact on palatally and labially displaced canines. This study aims to conduct a comparison of the palate morphology between individuals with palatal and labially displaced canines and control subjects using statistical shape analysis on a coronal cross-sectional of CBCT images. MATERIALS AND METHODS: Patients aged between 12 and 43 years with palatally or labially displaced canines referred to Hamadan School of Dentistry between 2014 and 2019 were recruited for this retrospective study. The sample included 29 palatally displaced canines (PDC), 20 labially displaced canines (LDC), and 20 control groups (CG). Initially, the maxillary palate coronal section was acquired and landmarked in the region between the right and the left first molar. Procrustes and principal component analyses were used to identify the primary patterns of palatal shape variation. Statistical tests were then performed to examine both shape and size differences. RESULTS: According to the results of Hotelling's T2 test, there is a significant difference between the mean shape of palate in PDC and CG (P = 0.009), while the difference between the PDC-LDC and LDC-CG groups is not significant. The longest full Procrustes distance was observed between PDC and CG (distance = 0.043), and the shortest full Procrustes distance was observed between LDC and CG (distance = 0.029). The first two principal components accounted for 84.47% of the total variance. The predictive accuracy of the discriminant analysis model showed that 72.46% of cases were correctly classified into the three study groups. CONCLUSIONS: In terms of centroid size, there was no significant difference in the sectional area between the three groups, but the difference between the mean shape of palate in the PDC and CG groups was significant. The PDC group showed more prominent mid-palatal area in the molar region.

Impact of COVID-19 Disease Control Committee (CDCC) policies on prevention of the disease using Bayes network inference in west of Iran.

BACKGROUND: The start of the COVID-19 pandemic was an emergency situation that led each country to adopt specific regional strategies to control it. Given the spread of COVID-19 disease, it is crucial to evaluate which policy is more effective in reducing disease transmission. The purpose of this study was to determine the impact of policies made by COVID-19 Disease Control Committee (CDCC) to reduce the risk of the disease in Hamadan province. METHODS: In the observational study, the data were extracted from three sources in Hamadan, west of Iran; first, the session reports of CDCC; second, information on periodic evaluations conducted by the primary health care directory in Hamadan from April to August 2021 and third, expert panel opinion. Bayes network analysis was used to determine the effect of each policy on mortality rate by GeNIe software version 2.2. RESULTS: Among the policies adopted by CDCC in Hamadan, seven policies, i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene had the most impact to prevent the spread of COVID-19, respectively. In this study, the prevalence of the disease was 17.64% with the implementation of these policies. Now, if all these policies are observed 30% more, the prevalence will decrease to 14.18%. CONCLUSION: This study showed that if the seven policies (i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene) are followed simultaneously in the community, the risk of contracting the disease will be greatly reduced. Therefore, in the pandemic of infectious diseases, such policies can help prevent the spread of the disease.

Longitudinal machine learning model for predicting systolic blood pressure in patients with heart failure.

Objective: Systolic blood pressure (SBP) strongly indicates the prognosis of heart failure (HF) patients, as it is closely linked to the risk of death and readmission. Hence, maintaining control over blood pressure is a vital factor in the management of these patients. In order to determine significant variables associated with changes in SBP over time and assess the effectiveness of classical and machine learning models in predicting SBP, this study aimed to conduct a comparative analysis between the two. Methods: This retrospective cohort study involved the analysis of data from 483 patients with HF who were admitted to Farshchian Heart Center located in Hamadan in the west of Iran, and hospitalized at least two times between October 2015 and July 2019. To predict SBP, we utilized a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR). The effectiveness of both models was evaluated based on the mean absolute error and root mean squared error. Results: The LMM analysis revealed that changes in SBP over time were significantly associated with sex, body mass index (BMI), sodium, time, and history of hypertension (P-value < 0.05). Furthermore, according to the MLS-SVR analysis, the four most important variables in predicting SBP were identified as history of hypertension, sodium, BMI, and triglyceride. In both the training and testing datasets, MLS-SVR outperformed LMM in terms of performance. Conclusions: Based on our results, it appears that MLS-SVR has the potential to serve as a viable alternative to classical longitudinal models for predicting SBP in patients with HF.

Joint Modeling of Longitudinal Outcome and Competing Risks: Application to HIV/AIDS Data.

BACKGROUND: Tuberculosis (TB) and human immunodeficiency virus (HIV) are major public health challenges globally, and the number of TB infections and death caused by HIV are high because of HIV/ TB co-infection. On the other hand, CD4 count plays a significant role in TB/HIV co-infections. We used a joint model of longitudinal outcomes and competing risks to identify the potential risk factors and the effect of CD4 cells on TB infection and death caused by HIV in HIV-infected patients. STUDY DESIGN: This was a retrospective cohort study. METHODS: The current study was performed on 1436 HIV+patients referred to Behavioral Diseases Counseling Centers in Kermanshah Province during 1998-2019. In this study, joint modeling was used to identify the effect of potential risk factors and CD4 cells on TB and death caused by HIV. RESULTS: The results demonstrated that the decreasing CD4 cell count was significantly associated with an increased risk of death, while it had no significant relation with the risk of TB. In addition, patients with TB were at a higher risk of death. Based on the results, a significant relationship was found between CD4 count and sex, marital status, education level, antiretroviral therapy (ART), time, and the interaction between time and ART. Further, people infected with HIV through sexual relationships were at higher risk of TB, while those with a history of imprisonment who received ART or were infected with HIV through drug injection had a lower risk of TB. CONCLUSION: The findings revealed that the decreasing CD4 count had a significant association with an increased risk of death caused by HIV. However, it was not significantly related to the risk of TB. Finally, patients with TB were at higher risk of death caused by HIV.

Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study.

A randomized controlled trial is commonly designed to assess the treatment effect in survival studies, in which patients are randomly assigned to the standard or the experimental treatment group. Upon disease progression, patients who have been randomized to standard treatment are allowed to switch to the experimental treatment. Treatment switching in a randomized controlled trial refers to a situation in which patients switch from their randomized treatment to another treatment. Often, the switchis from the control group to the experimental treatment. In this case, the treatment effect estimate is adjusted using either convenient naive methods such as intention-to-treat, per-protocol or advanced methods such as rank preserving structural failure time (RPSFT) models. In previous simulation studies performed so far, there was only one possible outcome for patients. However, in oncology in particular, multiple outcomes are potentially possible. These outcomes are called competing risks. This aspect has not been considered in previous studies when determining the effect of a treatment in the presence of noncompliance. This study aimed to extend the RPSFT method using a two-dimensional G-estimation in the presence of competing risks. The RPSFT method was extended for two events, the event of interest and the competing event. For this purpose, the RPSFT method was applied based on the cause-specific hazard approach, the result of which is compared to the naive methods used in simulation studies. The results show that the proposed method has a good performance compared to other methods.

The estimation of long and short term survival time and associated factors of HIV patients using mixture cure rate models.

BACKGROUND: HIV is one of the deadliest epidemics and one of the most critical global public health issues. Some are susceptible to die among people living with HIV and some survive longer. The aim of the present study is to use mixture cure models to estimate factors affecting short- and long-term survival of HIV patients. METHODS: The total sample size was 2170 HIV-infected people referred to the disease counseling centers in Kermanshah Province, in the west of Iran, from 1998 to 2019. A Semiparametric PH mixture cure model and a mixture cure frailty model were fitted to the data. Also, a comparison between these two models was performed. RESULTS: Based on the results of the mixture cure frailty model, antiretroviral therapy, tuberculosis infection, history of imprisonment, and mode of HIV transmission influenced short-term survival time (p-value < 0.05). On the other hand, prison history, antiretroviral therapy, mode of HIV transmission, age, marital status, gender, and education were significantly associated with long-term survival (p-value < 0.05). The concordance criteria (K-index) value for the mixture cure frailty model was 0.65 whereas for the semiparametric PH mixture cure model was 0.62. CONCLUSION: This study showed that the frailty mixture cure models is more suitable in the situation where the studied population consisted of two groups, susceptible and non-susceptible to the event of death. The people with a prison history, who received ART treatment, and contracted HIV through injection drug users survive longer. Health professionals should pay more attention to these findings in HIV prevention and treatment.

Identification of gene profiles related to the development of oral cancer using a deep learning technique.

BACKGROUND: Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning. METHODS: Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes. RESULTS: Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes. CONCLUSIONS: Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

Improving service efficiency and throughput of cardiac surgery patients using Monte Carlo simulation: a queueing setting.

Bed occupancy rate (BOR) is important for healthcare policymakers. Studies showed the necessity of using simulation approach when encountering complex real-world problems to plan the optimal use of resources and improve the quality of services. So, the aim of the present study is to estimate average length of stay (LOS), BOR, bed blocking probability (BBP), and throughput of patients in a cardiac surgery department (CSD) using simulation models. We studied the behavior of a CSD as a complex queueing system at the Farshchian Hospital. In the queueing model, customers were patients and servers were beds in intensive care unit (ICU) and post-operative ward (POW). A computer program based on the Monte Carlo simulation, using Python software, was developed to evaluate the behavior of the system under different number of beds in ICU and POW. The queueing simulation study showed that, for a fixed number of beds in ICU, BOR in POW decreases as the number of beds in POW increases and LOS in ICU increases as the number of beds in POW decreases. Also, based on the available data, the throughput of patients in the CSD during 800 days was 1999 patients. Whereas, the simulation results showed that, 2839 patients can be operated in the same period. The results of the simulation study clearly demonstrated the behavior of the CSD; so, it must be mentioned, hospital administrators should design an efficient plan to increase BOR and throughput of patients in the future.

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.

BACKGROUND: The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients. METHODS: This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models. RESULTS: Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features. CONCLUSIONS: Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.

Prediction of Hepatitis disease using ensemble learning methods.

Objective: Hepatitis is one of the chronic diseases that can lead to liver cirrhosis and hepatocellular carcinoma, which cause deaths around the world. Hence, early diagnosis is needed to control, treat, and reduce the effects of this disease. This study's main goal was to compare the performance of traditional and ensemble learning methods for predicting hepatitis B virus (HBV), and hepatitis C virus (HCV). Also, important variables related to HBV and HCV were identified. Methods: This case-control study was conducted in Hamadan Province, in the west of Iran, between 2014 to 2019. It included 534 subjects (267 cases and 267 controls). The bagging, random forest, AdaBoost, and logistic regression were used for predicting HBV and HCV. These methods' performance was evaluated using accuracy. Results: According to the results, the accuracy of bagging, random forest, Adaboost, and logistic regression were 0.65 ± 0.03, 0.66 ± 0.03, 0.62 ± 0.04, and 0.64 ± 0.03, respectively, with random forest showing the best performance for predicting HBV. This method showed that ALT was the most important variable for predicting HBV. The the accuracy of random forest was 0.77±0.03 for predicting HCV. Also, the random forest showed that the order of variable importance has belonged to AST, ALT, and age for predicting HCV. Conclusion: This study showed that random forest performed better than other methods for predicting HBV and HCV.

Zero-augmented beta-prime model for multilevel semi-continuous data: a Bayesian inference.

Semi-continuous data characterized by an excessive proportion of zeros and right-skewed continuous positive values appear frequently in medical research. One example would be the pharmaceutical expenditure (PE) data for which a substantial proportion of subjects investigated may report zero. Two-part mixed-effects models have been developed to analyse clustered measures of semi-continuous data from multilevel studies. In this study, we propose a new flexible two-part mixed-effects model with skew distributions for nested semi-continuous cost data under the framework of a Bayesian approach. The proposed model specification consists of two mixed-effects models linked by the correlated random effects: Part I) a model on the occurrence of positive values using a generalized logistic mixed model; and Part II) a model on the magnitude of positive values using a linear mixed model where the model errors follow skew distributions including beta-prime (BP). The proposed method is illustrated with pharmaceutical expenditure data from a multilevel observational study and the analytic results are reported by comparing potential models under different skew distributions. Simulation studies are conducted to assess the performance of the proposed model. The DIC3, LPML, WAIC, and LOO as the Bayesian model selection criteria and measures of divergence used to compare the models.

The Dynamic Effective Reproductive Number of COVID-19 during the Epidemic in Iran.

Background: We aimed to determine the generation time, the best model for estimating reproduction number (R), and to estimate the basic reproduction number (R0) and effective reproduction number (Rt) for COVID-19 in Iran. Methods: We used the daily incidence cases of COVID-19, hospitalized due to a probable diagnosis of COVID-19 from 19 February 2020 to 17 November 2020 in Iran. Four models, including maximum likelihood (ML), exponential growth (EG), time-dependent (TD), sequential Bayesian (SB) were evaluated. The weekly reproduction number with a 95% confidence interval (CI) was calculated. Results: TD model shows the best fit compared to other models for estimating reproduction number in Iran. The R0 in Iran in the first week of the epidemic, leading up to 21 February 2020 was 7.19, 95% CI: 5.56, 9.00. The lowest value for the Rt was equal to 0.77 between 3 to 10 March 2020 and 4 to 11 December 2020. From 11 June 2020 up to13 August 2020, the Rt was more than one but after then to 24 September 2021 was less than one. Conclusion: TD model was the best fit for estimating the R in Iran. The worst situation of the epidemic in Iran was related to the weeks leading up to 26 February 2020 and 28 October 2020, and better status was related to the weeks leading up to 10 March 2020 and 11 December 2020.

Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model.

BACKGROUND: This study aimed to use the hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict the long term occurrence of major adverse cardiac and cerebrovascular events (MACCE) of patients underwent percutaneous coronary intervention (PCI) with stent implantation. METHOD: This retrospective cohort study included a total of 220 patients (69 women and 151 men) who underwent PCI in Ekbatan medical center in Hamadan city, Iran, from March 2009 to March 2012. The occurrence and non-occurrence of MACCE, (including death, CABG, stroke, repeat revascularization) were considered as a binary outcome. The predictive performance of ANFIS model for predicting MACCE was compared with ANFIS-PSO and logistic regression. RESULTS: During ten years of follow-up, ninety-six patients (43.6%) experienced the MACCE event. By applying multivariate logistic regression, the traditional predictors such as age (OR = 1.05, 95%CI: 1.02-1.09), smoking (OR = 3.53, 95%CI: 1.61-7.75), diabetes (OR = 2.17, 95%CI: 2.05-16.20) and stent length (OR = 3.12, 95%CI: 1.48-6.57) was significantly predicable to MACCE. The ANFIS-PSO model had higher accuracy (89%) compared to the ANFIS (81%) and logistic regression (72%) in the prediction of MACCE. CONCLUSION: The predictive performance of ANFIS-PSO is more efficient than the other models in the prediction of MACCE. It is recommended to use this model for intelligent monitoring, classification of high-risk patients and allocation of necessary medical and health resources based on the needs of these patients. However, the clinical value of these findings should be tested in a larger dataset.

Prevalence of toxoplasmosis in patients infected with tuberculosis; a sero-molecular case-control study in northwest Iran.

In this study, we investigated the possible association between TB and Toxoplasma gondii infection. One hundred confirmed TB individuals living in northwest Iran were classified into three subgroups; newly diagnosed patients (NTB), old diagnosed patients (OTB) and multidrug resistance patients (MDR-TB). One hundred healthy subjects in the same age and sex distribution were ethnically matched. Sera samples were screened for anti-Toxoplasma antibodies. Nested-PCR was performed by targeting the B1 and GRA6 genes. The frequency of Toxoplasma infection based on IgG titer was 71.1% in the OTB subgroup and 33% in the control group, indicating significant association between Toxoplasma seropositivity and OTB (P = 0.001). According to phylogenetic network, the type I strain of Toxoplasma was identified in the OTB subgroup (10.1%). We concluded that patients with OTB subgroup are at high risk for acquisition of Toxoplasma infection which could reactivate the latent toxoplasmosis.

Determining the Risk Factors Affecting on Death Due to Colorectal Cancer Progression: Survival Analysis in the Presence of Competing Risks.

PURPOSE: In survival analysis, some patients may be at risk of more than one event, for example cancer-related death and cancer-unrelated death. In this case, if the aim of study becomes to assess the impact of risk factors on different causes of death, the competing risk model should be used rather than classical survival model. The aim of the present study is to determine the risk factors for related and unrelated mortality in patients with colorectal cancer using competing risk regression models. METHODS: The present retrospective cohort study was carried out on 310 CRC patients. Death due to cancer progression was considered as the interest event, and death due to unrelated cancer was considered as a competing event. Two most popular methods, cause-specific and subdistribution hazard regression model, were used to determine the effect of covariates on incidence and cause-specific hazard. Data analysis was performed using R3.6.2 software and cmprsk and survival packages. RESULTS: The mean (SD) of patients' age was 55.84 ± 13.2 years and 53.9% of them were male. BMI, T and N stage had a significant effect on both incidence and cause specific hazard of cancer-related death. CONCLUSION: The results of this study showed that cancer-related death is strongly correlated with under-weight (BMI < 18.5) and advanced clinical stage of the disease in patients with colorectal cancer. So, in the presence of competing events, both types of regression hazard models should be applied to permit a full understanding of the impact of covariates on the incidence and the rate of occurrence of each outcome.

Factors affecting systolic blood pressure trajectory in low and high activity conditions.

Background: Typically, blood pressure dips during sleep and increases during daytime. The blood pressure trend is affected by the autonomic nervous system. The activity of this system is observable in the low and high activity conditions. The aim of this study was to assess the effect of individual characteristics on systolic blood pressure (SBP) across day-night under low and high activity conditions. Methods: The samples were 34 outpatients who were candidates for evaluation of 24 hours of blood pressure with an ambulatory. They were admitted to the heart clinic of Farshchian hospital, located in Hamadan province in the west of Iran. The hourly SBP during 24 hours was considered as a response variable. To determine the factors effecting SBP in each condition, the hidden semi-Markov model (HSMM), with 2 hidden states of low and high activity, was fitted to the data. Results: Males had lower SBP than females in both states. The effect of age was positive in the low activity state (ß=0.30; p<0.001) and negative in high activity state (ß= -0.21; p=0.001). The positive effect of cigarette smoking on SBP was seen in low activity state (ß=5.02; p=0.029). The overweight and obese patients had higher SBP compared to others in high activity state (ß=11.60; p<0.001 and ß=5.87; p=0.032, respectively). Conclusion: The SBP variability can be displayed by hidden states of low and high activity. Moreover, the effects of studied variables on SBP were different in low and high activity states.

Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods.

OBJECTIVES: Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalance and missing data, which are two common issues in medical data. The current study's main goal was to compare the performance of six machine learning (ML) methods for predicting hospital readmission in HF patients. METHODS: In this retrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in Farshchian Heart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM), least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predict hospital readmission. These methods' performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Two imputation methods were also used to deal with missing data. RESULTS: Of the 1,856 HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracy in the range of 0.57-0.60, while RF performed the best, with the highest accuracy (range, 0.90-0.91). Other ML methods showed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance of the SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the median imputation method. CONCLUSIONS: This study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.

Estimating the treatment effect in patients with gastric cancer in the presence of noncompliance.

AIM: In this study, these methods were used to estimate the treatment effect in patients with gastric cancer in the presence of noncompliance. BACKGROUND: In medical sciences, simple and advanced methods are used to estimate treatment effects in the presence of noncompliance. METHODS: This historical cohort study surveyed 178 patients with gastric cancer underwent chemotherapy alone (chemotherapy alone group) and 193 patients underwent surgery and chemotherapy (surgery plus chemotherapy group) from 2003 to 2007 at the Cancer Institute of Imam Khomeini Hospital (Tehran). Demographic and clinical characteristics were extracted from patients' hospital records. The survival of patients was calculated as being from diagnosis to death or to the end of the study. The treatment effect was estimated using three methods: treatment as a time-dependent covariate, IPCW, and Structural Nested Models using STATA and R software. RESULTS: Fifty-six patients (31.5%) who underwent chemotherapy and 69 patients (35.8%) who underwent surgery and chemotherapy died by the end of the study. The hazard ratio in group I compared to group II was estimated between 1.5 to 2.07 times based on the simple analysis method. The modified hazard ratio was estimated to be 1.21 (95% CI: 1.11-1.32) based on the SNM method. Surgery plus chemotherapy is superior to chemotherapy alone, and it improves the overall survival (OS) rate of gastric cancer patients. CONCLUSION: Survival was improved in patients undergoing chemotherapy and surgery together compared to those undergoing chemotherapy alone. The results of the current study suggest that treatment effect can be estimated unbiasedly using the appropriate method.

Association between rs11614913 Polymorphism of The MiR-196-a2 Gene and Colorectal Cancer in The Presence of Departure from Hardy-Weinberg Equilibrium.

OBJECTIVE: Colorectal cancer (CRC) is the fourth most common and the second most lethal cancer worldwide. CRC mortality is increasing in Iran. In the current study, we aimed to investigate association between rs11614913 polymorphism of the miR-196-a2 gene and CRC. MATERIALS AND METHODS: In this case-control study, we assessed association of the rs11614913 polymorphism in 194 patients with CRC (case) and 286 healthy individuals (control). The expectation-maximization (EM) algorithm method was used to adjust deviation from Hardy-Weinberg equilibrium (HWE). RESULTS: There was no significant difference between genotypic frequencies of rs11614913 polymorphism in the control and case groups. Genotypic frequencies differed in the adjusted and unadjusted deviations from the HWE. Analysis of unadjusted and adjusted independent variables showed that age, sex, alcohol consumption, and drug use were statistically significant. CONCLUSION: Our findings showed that rs11614913 polymorphism was not associated with CRC risk. Deviation from HWE affected the results. It is recommended to perform further studies to establish HWE. Ignoring the equilibrium can cause in consistencies in the results of studies.

Investigating the effect of behavioral and demographic characteristics of Iranian women on the number of children ever born.

Background: The number of children ever born (CEB) to a woman, as an index of her fertility behavior, are interesting for the governments and demographer policymakers. In recent years, a notable reduction of fertility and population aging in Iran has caused concern among politicians, and it has led to starting new changes in demographic policies. Therefore, to adopting new demographic and health policies programs, identification of factors that affecting CEB is essential. Methods: To evaluate determinant factors on CEB, information of 20093 married Iranian women aged between 15 and 54 years has been analyzed from the Iranian National Institute of Health Research survey. Based on the structure of data and the possible influential unobserved population heterogeneity on CEB in each city and province, a multilevel count regression model was applied. The analysis was performed using the 'R' software (version 3.5) with a significant level of 0.05. Results: Findings show that the mean and median number of CEB was 2.82 and 2.00 for all women, respectively. Meanwhile, these values were 4.56 and 4.00 for the women who reached menopause. There was a significant unobserved heterogeneity affecting CEB in each province (σp=0.018). Also, the results of the multilevel model show that living in an urban area (RR=0.90), higher age at first marriage (RR=0.96), higher education (RR=0.84, RR=0.81), and exposure to mass media (RR=0.87) decrease the risk ratio of the number of CEB (p <0.001). Conclusion: It seems that the tendency of women to academic education and their access to mass media has a significant effect on reducing childbearing. Therefore, in future planning, attention to these two factors can be useful and helpful to move to increase fertility.