bayesian approach

Vital parameters must be monitored during sedation. This study aimed to evaluate the effects of ketamine-midazolam (KM) and ketamine-propofol (KP) combinations on the heart rate (HR) and oxygen saturation (SPO2) of non-cooperative children. The model parameters were estimated using the Bayesian approach. The data were collected in a double-masked crossover study with repeated measurements (CSWRM). Twenty-two non-cooperative children 3–6 years old were included, and the linear mixed model was adopted for data analysis. The Bayesian estimation of the parameters and their 95% credible interval were calculated in SAS 9.4. The mean heart rate of KM recipients compared to KP recipients was significantly different by 4.47 beats per minute (bpm). The mean HR in KP was lower than KM's, but oxygen saturation (SPO2) was not significantly different. Although the two drug combinations did not differ in SPO2, they differed in HR. As such, the KP combination is recommended.

Recurrent event data are common in many longitudinal studies. Often, a terminating event such as death can be correlated with the recurrent event process. A shared frailty model applied to account for the association between recurrent and terminal events. In some situations, a fraction of subjects experience neither recurrent events nor death; these subjects are cured.

In this paper, we discussed the Bayesian approach of a joint frailty model for recurrent and terminal events in the presence of cure fraction. We compared estimates of parameters in the Frequentist and Bayesian approaches via simulation studies in various sample sizes; we applied the joint frailty model in the presence of cure fraction with Frequentist and Bayesian approaches for breast cancer.

In small sample size Bayesian approach compared to Frequentist approach had a smaller standard error and mean square error, and the coverage probabilities close to nominal level of 95%. Also, in Bayesian approach, the sampling means of the estimated standard errors were close to the empirical standard error.

The simulation results suggested that when sample size was small, the use of Bayesian joint frailty model in the presence of cure fraction led to more efficiency in parameter estimation and statistical inference.

Hematopoietic stem cell transplantation (HSCT) is the most effective of all hematologic malignancies treatments, resulting in a significant improvement in survival rate.

This study aimed at determining the survival rate and factors affecting the survival in patients undergoing hematopoietic stem cell transplantation, using the joint model.

This study was a retrospective cohort study, used for collecting data from patients with hematopoietic malignancies who underwent hematopoietic stem cell transplantation in Taleghani Hospital (Shahid Beheshti University of Medical Sciences), Tehran, Iran during the years 2007 and 2015 and were followed up till 2017. A Bayesian joint model of longitudinal and survival was chosen, using Win Bugs software.

A total of 395 patients were enrolled. The median overall survival was 6.3 years (95% CI (5.86, 6.76)). Eighty-one patients had died. The obtained results from this study manifested that age (HR: 1.02, 95% CI: (1.002, 1.04)) and pre-transplantation relapse (HR = 1.64, 95% CI: (1.09, 2.4)) have incremental impact on death after transplantation, while malignancy type (NHL (HR: 0.33, 95%CI: (0.152, 0.73)) and AML (HR: 0.62, 95% CI: (0.29, 0.7)) are also effective in reducing death after transplantation. Similarly, the correlation index between longitudinal and survival models proved to be significant (HR: 0.6, 95% CI: (0.0802, 0.37)).

This study showed that age, per-transplantation relapse, and malignancy type are the effective factors in the survival rate. Moreover, the link parameter between longitudinal response (WBC) and the survival indicated that an increase in WBC count leads to a decrease in the death risk.

Lung cancer is one of the most common cancers around the world. The aim of this study was to use Extended Cox Model (ECM) with Bayesian approach to survey the behavior of potential time-varying prognostic factors of Non-small cell lung cancer.

Survival status of all 190 patients diagnosed with Non-Small Cell lung cancer referring to hospitals in Yazd were recorded from 2009 to 2013 by phone call. We fitted conventional Cox proportional hazards (Cox PH) as well as Bayesian ECM. Inference for estimated risk ratios was based on 90% credible intervals. Log pseudo marginal likelihood criteria (LMPL) was used for model comparison. Statistical computations were based on R language.

In this study, 190 patients with non-small cell lung cancer were followed, of whom 160 died because of the disease (84.2%). Median of survival time was 8 ± 0.076 month. After fitting the Cox PH Model, it was determined that the PH assumption was not satisfied for the type of treatment, the disease stage, and pathology status variables (p <0.001). LPML for Cox PH and Bayesian ECM was -431.593 and -401.01, respectively. Estimated hazard ratio curves based on Bayesian ECM showed that the risk ratio for these variables exhibited significant time varying behavior on hazard of lung cancer through follow up time.

Based on LMPL, Bayesian ECM was found to have a better fit than Cox PH Model which declares, results from Cox PH should be interpreted with care. Especially, from beginning of the study to about 20 month after, very high risk ratio was estimated for variables whose PH was not satisfying for them.

Congenital Hypothyroidism (CH) is one of the reasons for mental retardation and defective growth in neonates. It can be treated if it is diagnosed early. The congenital hypothyroidism can be diagnosed using newborn screening in the first days after birth. Disease mapping helps to identify high-risk areas of the disease. This study aimed to evaluate the pattern of CH using the Poisson Spatio-temporal model in disease mapping under the Bayesian paradigm. The recorded data of all infants diagnosed with CH between 2011 and 2018 in Guilan, Iran were used in this study. The Poisson Spatio-temporal model under the Bayesian paradigm was run using the Markov Chain Monte Carlo method in Open BUGS software. Moreover, the maps of the towns in Guilan were prepared via Arc GIS software. Out of 219800 live births in Guilan, Iran, the incidence of CH was 2:1000 in this time period. The pattern of disease mapping for the posterior mean of relative risk for CH was identical in this 7-year period. Furthermore, the pattern of disease mapping with spatial model excluding time dependence was similar to the maps of the Spatio-temporal model. The incidence rate of CH was approximately constant during this time, and disease mapping revealed no rising trends in this period. This probably can be due to resolving iodine deficiency as one of the main causes of CH incidence by consuming kinds of seafood and iodized salt in Guilan province.

The prevalence of HIV is increasing in Iran, so obtaining an estimate of the survival of HIV-infected persons can be helpful to prevent and control this infection. This research aimed to use the Bayesian joint model by which identifies factors associated with the survival and determine the relationship between the trend of CD4+ T cell counts and survival time in HIV-infected persons. In this retrospective cohort study, we collected HIV/AIDS surveillance data from Mashhad’s Counseling Center of Behavioral Diseases in the province of Khorasan Razavi, Northeast of Iran, during 1994 - 2014. Data collection included variables CD4+ T cells count, survival time, and other related factors. We used the Bayesian joint model to estimate the survival time and identify the factors associated with survival time in HIV-infected persons. The study included 260 individuals, of whom 212 (81.54%) were male. The survival sub-model of the joint model identified gender (95% credible interval (CI): 0.486, 3.197) and antiretroviral treatment (95% CI: -1.935, -0.641) as the variables associated with the patients’ survival. The longitudinal sub-model, which determined the variables associated with the number of CD4+ T-cells included time (95% CI: -0.934, -0.554), age (95% CI: -0.152, -0.011), and antiretroviral treatment (95% CI: -6.193, -3.505). Using CD4+ T cells as a covariate in the Bayesian joint model, the survival time for HIV-infected persons was estimated more precisely than separate model and it can be inferred that at the beginning of antiretroviral treatment, especially in men and controls, the CD4+ T cell counts can increase the survival time of HIV-infected persons.

Tumor stage is one of the most reliable prognostic factors in the clinical characterization of colorectal cancer. The identification of genes associated with tumor staging may facilitate the personalized molecular diagnosis and treatment along with better risk stratification in colorectal cancer. The study aimed to identify genetic signatures associated with tumor staging and patients’ survival in colorectal cancer and recognize the patients' risk category for clinical outcomes based on transcriptomic data. In this retrospective cohort study, two available transcriptomic datasets, including 232 patients with colorectal cancer under accession number GSE17537 and GSE17536 were used as discovery and validation sets, respectively. A Bayesian sparse group selection method in the discovery set was applied to identify the associated genes with the tumor staging. Then further screening was performed using survival analysis, and significant genes were used to develop a gene signature model. Finally, the robust performance of the signature model was assessed in the validation set. A total of 56 genes were significantly associated with the tumor staging in colorectal cancer. Survival analysis resulted in a shortlist of 19 genes, including ADH1B (P = 0.012), AHI (P = 0.006), AKAP12 (P = 0.018), BNIP3 (P = 0.015), CLDN11 (P = 0.015), CST9L (P = 0.028), DPP10 (P = 0.029), FBXO33 (P = 0.036), HEBP (P = 0.025), INTS4 (P = 0.003), LIPJ (P = 0.001), MMP21 (P = 0.006), NGRN (P = 0.014), PAFAH1B2 (P = 0.035), PCOLCE2 (P = 0.009), PIM1 (P = 0.007), TBKBP1 (P = 0.003), TCEB3B (P = 0.001), and TIPARP (P = 0.018), developing the signature model and validation. In both discovery and validation sets, the discrimination ability of the signature model to categorize patients with colorectal cancer into low- and high-risk subgroups for mortality and recurrence at 3- and 5-years showed good discrimination performances, with the area under the receiver operating characteristic curve (ROC) ranging from 0.64 to 0.88. It also had good sensitivity (discovery set 63.1%, validation set 61.7%) and specificity (discovery set 75.0%, validation set 59.3%) to discriminate between early- and late-stage groups. We identified a 19-gene signature associated with tumor staging and survival of colorectal cancer, which may represent potential diagnosis and prognosis markers, and help to classify patients with colorectal cancer into low- or high-risk subgroups.

Mammography is a valuable tool for early diagnosis of breast cancer in asymptomatic women. Considering the high prevalence of breast cancer in Iranian women and the low participation in mammography screening program, the purpose of this study was to investigate the factors affecting frequency of mammography screening in women over 40 years of age using zero-. In this study, the required information about number of performing mammography in women’s’ lifetime, demographic characteristics and behavioral risk factors were obtained through interview based on a researcher-made questionnaire. To investigate the factors affecting mammography, zero inflation Poisson regression models were performed using Win Bugs software. The mean (SD) age of women participating in this study was 49.87 (6.76). 77% of the participants have never undergone mammography, 8.9% once, 6.9% twice, 6.7% three times, and 0.5% more than three times. Age had a positive effect on the number of mammograms in the women who have perform mammograms at least once. Having a family history of cancer and breast cancer, middle compared to low economic status, higher compared to low education and menopause were significantly associated with lower probability of never performing mammography. Given the relatively low participation of women in mammography, more facilities are needed for high risk women (aged 40-70) to facilitate the diagnosis process.

In clinical dental studies, each participant has usually several visits, and since the review and ongoing monitoring of the subjects are often expensive or even impossible, so people are examined periodically during regularly pre-scheduled visits. Therefore, discrete or grouped clustered failure time data are collected. We aimed to show the use of Monte Carlo Markov Chain (MCMC) and the non-informative prior in a Bayesian framework in multilevel modeling of clustered grouped survival data.
Methods & Materials: A two-level model with additive variance components model for the random effects was considered. Both the grouped proportional hazards model and logistic regression with logit link function model were used. Using grouped proportional hazards method, we could approximate intracluster correlation of the log failure times. The statistical package OpenBUGS was adopted to estimate the parameter of interest based on the MCMC method. A cohort study was used in which 1011 persons visited at clinic dentistry of Tehran University of Medical Sciences, Iran, between the years 2002 and 2013 for dental implant and 2368 implants were placed for them in total. Clinical status of dental implants was evaluated in three periods after placement, thus clustered grouped failure times of the dental implants were recorded.
The grouped proportional hazards model showed that clustering effect among the log failure times of the different implants from the same person was fairly strong (correlation = 0.99). Complication and biomaterial variables had no effect on the implant failure, and there was no difference in the failure times related to the molar, premolar, canine, primary, and incisor since 95% credible interval (CI) included 0. The CI related to the gender and place of teeth not including 0, so these variables were significant in the model. The estimates of the baseline parameters (γ1, γ2, and γ3) were increasing indicating increasing hazard rates from interval 1-3. Results of logistic regression were similar to grouped proportional hazards model with wider confidence intervals.
The use of MCMC approach and non-informing prior in Bayesian framework to mimic maximum likelihood estimations in a frequentist approach in multilevel modeling of clustered grouped survival data can be easily applied with the use of the software OpenBUGS

Chronic kidney disease (CKD) is a major public health problem that may lead to end-stage renal disease (ESRD). Renal transplantation has become the treatment modality of choice for the majority of patients with ESRD. It is therefore necessary to monitor the disease progression of patients who have undergone renal transplantation. In order to monitor the disease progression, the continuous assessment of kidney function over time is considered..
This study aimed to investigate the etiological role of recipient characteristics in serum creatinine changes within the follow-up period and in relation to the graft failure risk, as well as to evaluate whether or not the serum creatinine level represents an indicator of graft failure following renal transplantation..
This retrospective cohort study was conducted at the department of nephrology, Baqiyatallah Hospital, Baqiyatallah University of Medical Sciences, between April 2005 and December 2008. The study involved 413 renal transplantation patients. The primary outcomes were the determination of the serum creatinine levels at each attendance and the time to graft failure. Robust joint modeling of the longitudinal measurements (serum creatinine level) and time-to-event data (time to graft failure) were used for the analysis in the presence of outliers in the serum creatinine levels. The data analysis was implemented in WinBUGS 1.4.3..
There was a positive association between the serum creatinine level and graft failure (HR = 5.13, P Graft failure is more likely to occur in patients with higher serum creatinine levels..

During mixed dentition period, one can make accurate estimation of future dental development and can assess whether there will be enough space in the dental arch. In orthodontics treatment planning, it is vital to predict space required for unerupted canine and premolars in the arch.
The main goal of this study is to compare different teeth combinations in predicting needed space for unerupted canine and premolars on Bayesian approach and introduce the most reliable one.
Patients and The sample for this study consists of 47 dental casts (19 males, 28 females) with complete erupted dental arches. The meisodistal width of all teeth was measured using a dental caliper. We consider different combinations of teeth size and compare them to find the best predictor. In order to do that, quantile regression and Bayesian approach are applied using “R software”.
Combination of first maxillary molars with sum of central and lateral mandibular incisors has the smallest standard deviation. This is true for male and female samples. The regression formula based on this teeth combination has been introduced.
In our sample, combination of Mandibular incisors and maxillary first molar is found to be better than the other predictors for female and female model in both arches.

In the survival data with Long-term survivors the event has not occurred for all the patients despite long-term follow-up, so the survival time for a certain percent is censored at the end of the study. Mixture cure model was introduced by Boag, 1949 for reaching a more efficient analysis of this set of data. Because of some disadvantages of this model non-mixture cure model was introduced by Chen, 1999, which became well-known promotion time cure model. This model was based on the latent variable distribution of N. Non mixture cure models has obtained much attention after the introduction of the latent activating Scheme of Cooner, 2007, in recent decades, and diverse distributions have been introduced for latent variable.
Methods & Materials: In this article, generalized Poisson- inverse Gaussian distribution (GPIG) will be presented for the latent variable of N, and the novel model which is obtained will be utilized in analyzing long-term survival data caused by skin cancer. To estimate the model parameters with Bayesian approach, numerical methods of Monte Carlo Markov chain will be applied. The comparison drawn between the models is on the basis of deviance information criteria (DIC). The model with the least DIC will be selected as the best model.
The introduced model with GPIG, with deviation criterion of 411.775, had best fitness than Poisson and Poisson-inverse Gaussian distribution with deviation criterion of 426.243 and 414.673, respectively.
In the analyzing long-term survivors, to overcome high skewness and over dispersion using distributions that consist of parameters to estimate these statistics may improve the fitness of model. Using distributions which are converted to simpler distributions in special occasions, can be applied as a criterion for comparing other models.