Valuing the SF-6Dv2 in the capital of Iran using a discrete choice experiment with duration.

BACKGROUND: The second version of the Short-Form 6-Dimension (SF-6Dv2) classification system has recently been developed. The objective of this study was to develop a value set for SF-6Dv2 based on the societal preferences of a general population in the capital of Iran. METHODS: A representative sample of the capital of Iran (n = 3061) was recruited using a stratified multistage quota sampling technique. Face-to-face interviews were conducted using binary choice sets from the international valuation protocol of the discrete choice experiment with duration. The conditional logit was used to estimate the final value set, and a latent class model was employed to assess heterogeneity of preferences. RESULTS: Coefficients generated from the models were logically consistent and significant. The best model was the one that included an additional interaction term for cases where one or more dimensions reached their most severe levels. It provides a value set with logical consistent coefficients and the lowest percentage of worse than death health states. Predicted values for the SF-6Dv2 were within the range of - 0.796-1. Pain dimension had the largest impact on utility decrement, whereas vitality had the least impact. The presence of preference heterogeneity was evident, and the Bayesian Information Criterion indicated the optimal fit for a latent class model with two classes. CONCLUSION: This study provided the SF-6Dv2 value set for application in the context of Iran. This value set will facilitate the use of the SF-6Dv2 instrument in health economic evaluations and clinical settings.

CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction.

Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.

Comparison of maternal complications between fresh and frozen embryo transfer during gestation.

Objective: Maternal complications in infertile women undergoing in vitro fertilization are an important discussion, and patients should be informed about these complications depending on the method of embryo transfer. In this study, maternal complications during gestation were compared between frozen and fresh embryo transfer in infertile women who underwent in vitro fertilization at Shariati Hospital from 2018 to 2021. Materials and Methods: This study was a retrospective cohort study, and patient data were collected using archive files. From 396 in vitro fertilization patients, 302 were in the frozen embryo transfer group and 94 were in the fresh embryo transfer group. Patients in both groups were similar in terms of the number of transferred embryos and age (p>0.05). Data regarding threatened miscarriage, early miscarriage, placenta previa occurrence, gestational hypertension, preterm birth, gestational diabetes, and pre-eclampsia were gathered and compared between the two groups. Results: The rates of threatened miscarriage, placenta previa, gestational hypertension, gestational diabetes, preterm birth, and pre-eclampsia were not significantly different between the fresh and frozen embryo transfer groups (p>0.05). However, the early miscarriage rate in the fresh embryo transfer group was significantly higher (34% vs. 16.2%, p<0.001). Conclusion: According to the results of this study, maternal complications, except early miscarriage, were not different between fresh and frozen embryo transfer. However, frozen embryo transfer is safer in terms of the early miscarriage rate.

DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS: Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.

The effects of prognostic factors on transplant and mortality of patients with end-stage liver disease using Markov multistate model.

Background: Decompensated cirrhosis patients have a high risk of death which can be considerably reduced with liver transplantation (LT). This study aimed to simultaneously investigate the effect of some patients' characteristics on mortality among those with/without LT and also LT incident. Materials and Methods: In this historical cohort study, the information from 780 eligible patients aged 18 years or older was analyzed by the Markov multistate model; they had been listed between 2008 and 2014, needed a single organ for initial orthotopic LT, and followed at least for up to 5 years. Results: With a median survival time of 6 (5-8) years, there were 275 (35%) deaths. From 255 (33%) patients who had LT, 55 (21%) subsequently died. Factors associated with a higher risk of mortality and LT occurrence were included: higher model for end-stage liver disease (MELD) score (hazard ratio [HR] = 1.16, confidence interval [CI]: 1.09-1.24 and HR = 1.22, CI: 1.41-1.30) and ascites complication (HR = 2.34, CI: 1.74-3.16 and HR = 11.43, CI: 8.64-15.12). Older age (HR = 1.03, CI: 1.01-1.06), higher creatinine (HR = 6.87, CI: 1.45-32.56), and autoimmune disease versus hepatitis (HR = 2.53, CI: 1.12-5.73) were associated with increased risk of mortality after LT. Conclusion: The MELD and ascites are influential factors on waiting list mortality and occurrence of LT. Total life expectancy is not influenced by higher MELD.

Predicting the COVID-19 mortality among Iranian patients using tree-based models: A cross-sectional study.

Background and Aims: To explore the use of different machine learning models in prediction of COVID-19 mortality in hospitalized patients. Materials and Methods: A total of 44,112 patients from six academic hospitals who were admitted for COVID-19 between March 2020 and August 2021 were included in this study. Variables were obtained from their electronic medical records. Random forest-recursive feature elimination was used to select key features. Decision tree, random forest, LightGBM, and XGBoost model were developed. Sensitivity, specificity, accuracy, F-1 score, and receiver operating characteristic (ROC)-AUC were used to compare the prediction performance of different models. Results: Random forest-recursive feature elimination selected following features to include in the prediction model: Age, sex, hypertension, malignancy, pneumonia, cardiac problem, cough, dyspnea, and respiratory system disease. XGBoost and LightGBM showed the best performance with an ROC-AUC of 0.83 [0.822-0.842] and 0.83 [0.816-0.837] and sensitivity of 0.77. Conclusion: XGBoost, LightGBM, and random forest have a relatively high predictive performance in prediction of mortality in COVID-19 patients and can be applied in hospital settings, however, future research are needed to externally confirm the validation of these models.

Application of Frailty Quantile Regression Model to Investigate of Factors Survival Time in Breast Cancer: A Multi-Center Study.

Background: The prognostic factors of survival can be accurately identified using data from different health centers, but the structure of multi-center data is heterogeneous due to the treatment of patients in different centers or similar reasons. In survival analysis, the shared frailty model is a common way to analyze multi-center data that assumes all covariates have homogenous effects. We used a censored quantile regression model for clustered survival data to study the impact of prognostic factors on survival time. Methods: This multi-center historical cohort study included 1785 participants with breast cancer from four different medical centers. A censored quantile regression model with a gamma distribution for the frailty term was used, and p-value less than 0.05 considered significant. Results: The 10th and 50th percentiles (95% confidence interval) of survival time were 26.22 (23-28.77) and 235.07 (130-236.55) months, respectively. The effect of metastasis on the 10th and 50th percentiles of survival time was 20.67 and 69.73 months, respectively (all p-value < 0.05). In the examination of the tumor grade, the effect of grades 2 and 3 tumors compare with the grade 1 tumor on the 50th percentile of survival time were 22.84 and 35.89 months, respectively (all p-value < 0.05). The frailty variance was significant, which confirmed that, there was significant variability between the centers. Conclusions: This study confirmed the usefulness of a censored quantile regression model for cluster data in studying the impact of prognostic factors on survival time and the control effect of heterogeneity due to the treatment of patients in different centers.

The Therapeutic Efficacy of Zinc Oxide Nanoparticles on Acute Toxoplasmosis in BALB/c Mice.

Background: Toxoplasma gondii infects nearly one-third of the world's population. Due to the significant side effects of current treatment options, identifying safe and effective therapies seems crucial. Nanoparticles (NPs) are new promising compounds in treating pathogenic organisms. Currently, no research has investigated the effects of zinc oxide NPs (ZnO-NPs) on Toxoplasma parasite. We aimed to investigate the therapeutic efficacy of ZnO-NPs against tachyzoite forms of T. gondii, RH strain in BALB/c mice. Methods: In an experiment with 35 female BALB/c mice infected with T. gondii tachyzoites, colloidal ZnO-NPs at concentrations of 10, 20, and 50 ppm, as well as a 50 ppm ZnO solution and a control group, were orally administered four hours after inoculation and continued daily until the mices' death. Survival rates were calculated and tachyzoite counts were evaluated in the peritoneal fluids of infected mice. Results: The administration of ZnO-NPs resulted in the reduction of tachyzoite counts in infected mice compared to both the ZnO-treated and control group (P<0.001). Intervention with ZnO-NPs significantly increased the survival time compared to the control group (6.2±0.28 days, P-value <0.05), additionally, the highest dose of ZnO-NPs (50 ppm) showed the highest mice survival time (8.7±0.42 days). Conclusion: ZnO-NPs were effective in decreasing the number of tachyzoites and increasing mice survival time in vivo. Moreover, there were no significant differences in survival time between the untreated control group and the group treated with zinc oxide, suggesting that, bulk ZnO is not significantly effective in comparison with ZnONPs.

Compound Poisson frailty model with a gamma process prior for the baseline hazard: accounting for a cured fraction.

Cox model and traditional frailty models assume that all individuals will eventually experience the event of interest. This assumption is often overlooked, and situations will arise where it is not realistic. We introduce Compound Poisson frailty model for survival analysis to deal with populations in which some of the individuals will not experience the event of interest. This model assumes that the target population is a mixture of individuals with zero frailty and those with positive frailty. In this paper, we consider a compound Poisson frailty model for right-censored event times from a Bayesian perspective and compute the Bayesian estimator using the Markov Chain Monte Carlo method, where a Gamma process prior is adopted for the baseline hazard function. Furthermore, we evaluate the approach using simulation studies and demonstrate the methodology by analyzing the data from achalasia patient cohort.

A novel dynamic Bayesian network approach for data mining and survival data analysis.

BACKGROUND: Censorship is the primary challenge in survival modeling, especially in human health studies. The classical methods have been limited by applications like Kaplan-Meier or restricted assumptions like the Cox regression model. On the other hand, Machine learning algorithms commonly rely on the high dimensionality of data and ignore the censorship attribute. In addition, these algorithms are more sophisticated to understand and utilize. We propose a novel approach based on the Bayesian network to address these issues. METHODS: We proposed a two-slice temporal Bayesian network model for the survival data, introducing the survival and censorship status in each observed time as the dynamic states. A score-based algorithm learned the structure of the directed acyclic graph. The likelihood approach conducted parameter learning. We conducted a simulation study to assess the performance of our model in comparison with the Kaplan-Meier and Cox proportional hazard regression. We defined various scenarios according to the sample size, censoring rate, and shapes of survival and censoring distributions across time. Finally, we fit the model on a real-world dataset that includes 760 post gastrectomy surgery due to gastric cancer. The validation of the model was explored using the hold-out technique based on the posterior classification error. Our survival model performance results were compared using the Kaplan-Meier and Cox proportional hazard models. RESULTS: The simulation study shows the superiority of DBN in bias reduction for many scenarios compared with Cox regression and Kaplan-Meier, especially in the late survival times. In the real-world data, the structure of the dynamic Bayesian network model satisfied the finding from Kaplan-Meier and Cox regression classical approaches. The posterior classification error found from the validation technique did not exceed 0.04, representing that our network predicted the state variables with more than 96% accuracy. CONCLUSIONS: Our proposed dynamic Bayesian network model could be used as a data mining technique in the context of survival data analysis. The advantages of this approach are feature selection ability, straightforward interpretation, handling of high-dimensional data, and few assumptions.

Post-surgery Survival in Patients with Adenocarcinoma of Stomach Using Multistate Model.

PURPOSE: Stomach neoplasms are the fifth common cancer worldwide. The related factors for survival following stomach neoplasms are well-studied; however, information on recurrent events is limited. This study aimed to identify the related factors on recurrent and deaths following stomach neoplasms. METHODS: In this cohort study, information on 672 patients with adenocarcinoma who were hospitalized during 1995-2012 was used. Multistate models were applied to determine the effective factors on recurrent and death events. RESULTS: Median of survival time (months) and 5-year survival was estimated as 24.5 and 25%, respectively. The probability of death was 57% for non-recurrent patients, which increased to 88% among recurrent patients. Hazard of death was 49% lower for females (Hazard Ratio (HR):0.51, P = 0.009) while females had higher hazard of death following recurrent (HR:3.55, P < 0.001). Male patients and those with cardia involvement had higher risk of recurrence. A significant effect of age on the risk of death among patients with and without recurrence was estimated (HR:1.02, 1.03; P = 0.001 for both). Age, cardia involvement, and disease stage are amongst the effective factors on non-recurrent death while complement treatments increased the non-recurrent and recurrent survival. CONCLUSION: In patients, effects of some factors for survival may vary throughout the course of disease and depend on recurrence status. We found that while female patients experienced lower recurrence, they had higher risk of death following recurrence. Age, tumor location, and type of therapy were risk factors for non-recurrent death. Finally, tumor location and type of surgery had significant effects on recurrence.

Multivariate Longitudinal Assessment of Kidney Function Outcomes on Graft Survival after Kidney Transplantation Using Multivariate Joint Modeling Approach: A Retrospective Cohort Study.

Background: The performance of a transplanted kidney is evaluated by monitoring variations in the value of the most important markers. These markers are measured longitudinally, and their variation is influenced by other factors. The simultaneous use of these markers increases the predictive power of the analytical model. This study aimed to determine the simultaneous longitudinal effect of serum creatinine and blood urea nitrogen (BUN) markers, and other risk factors on allograft survival after kidney transplantation. Methods: In a retrospective cohort study, the medical records of 731 renal transplant patients, dated July 2000 to December 2013, from various transplant centers in Mashhad (Iran) were examined. Univariate and multivariate joint models of longitudinal and survival data were used, and the results from both models were compared. The R package joineRML was used to implement joint models. P values <0.05 were considered statistically significant. Results: Results of the multivariate model showed that allograft rejection occurred more frequently in patients with elevated BUN levels (HR=1.68, 95% CI: 1.24-2.27). In contrast, despite a positive correlation between serum creatinine and allograft rejection (HR=1.49, 95% CI: 0.99-2.22), this relationship was not statistically significant. Conclusion: Results of the multivariate model showed that longitudinal measurements of BUN marker play a more important role in the investigation of the allograft rejection.

The comparison of censored quantile regression methods in prognosis factors of breast cancer survival.

The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

A paradoxical change in economic inequality in presenting visual acuity between 2009 and 2014: a nonuseful decline.

BACKGROUND: Despite the widespread literate on health inequalities and their determinants, changes in health inequalities over time have not received enough attention. AIMS: To measure and decompose the over-time changes in economic inequality in presenting visual acuity measured using Logarithm of the Minimum Angle of Resolution. METHODS: We analysed 4706 participants who had complete data on presenting visual acuity and economic status in 2009 and 2014 in the Shahroud Eye Cohort Study. We measured changes in presenting visual acuity concentration indices and decomposed them the using a longitudinal approach. RESULTS: Both the presenting visual acuity and economic status deteriorated between 2009 and 2014. The mean (standard deviation) for presenting visual acuity and economic status scores in 2009 versus 2014 were 0.090 (0.2) versus 0.103 (0.2) and 0.01 (1.0) versus 0.0005 (1.07), respectively. Presenting visual acuity concentration index (95% confidence interval) in the first versus second phases of the study were -0.245 (-0.212 to -0.278) versus -0.195 (-0.165 to -0.225), respectively. Longitudinal decomposition of this change in concentration indices during the 5-year period indicated that the most important contributor to reduction in economic inequality of presenting visual acuity was deterioration of presenting visual acuity among people with higher economic status due to their ageing. CONCLUSION: Unexpectedly, reduction in economic inequality in presenting visual acuity was due to presenting visual acuity deterioration among the higher economic status group rather than its amelioration among the lower economic status group. Therefore, the needs of all socioeconomic groups should be considered separately to modify presenting visual acuity in each group and, consequently, reduce the economic inequality in presenting visual acuity.

The impact of common chronic conditions on health-related quality of life: a general population survey in Iran using EQ-5D-5L.

BACKGROUND: Diseases have undeniable effects on Health-Related Quality of Life (HRQoL). Chronic diseases, in particular, limit the productive potentials and HRQoL of individuals. EQ-5D is a very popular generic instrument, which can be used to estimate HRQoL scores in any diseases. The current study investigates mean HRQoL scores in certain chronic diseases and examines the relationship between utility scores and chronic diseases in Iran. METHOD: This cross-sectional study was carried out among the general adult population of Tehran. 3060 individuals were chosen by a stratified probability sampling method. The EQ-5D-5L questionnaire was applied. The utility scores were estimated using the Iranian crosswalk-based value set. The effect of chronic diseases on the HRQoL scores was derived by the Ordinary Least Squares (OLS) method. Data was analyzed using Stata version 13 software. RESULTS: The mean ± standard deviation utility and EQ-VAS scores were 0.85 ± 0.14 and 76.73 ± 16.55 in the participants without any chronic conditions. The scores were 0.69 ± 0.17 and 61.14 ± 20.61 in the participants with chronic conditions. The highest and lowest mean utility scores were related to thyroid disease (0.70) and Stroke (0.54), respectively. Common chronic conditions had significant negative effects on the HRQoL scores. Stroke (0.204) and cancer (0.177) caused the most reduction in the EQ-5D-5L utility scores. Lumbar disc hernia, digestive diseases, osteoarthritis, breathing problems, and anxiety/nerves cause 0.133, 0.109, 0.108, 0.087, and 0.078 reductions, respectively, in the EQ-5D-5L utility scores. CONCLUSION: This study provides insight into some common chronic conditions and their effects on the HRQoL. Policymakers and planners should pay attention to the effects of chronic conditions especially high prevalence one. They should adopt effective interventions to control this issue and increase health. The results of this study can also be beneficial in economic evaluation studies.

Factors Affecting the Long-Term Survival of Kidney Transplantation in Northeastern of Iran between 2000 and 2015.

BACKGROUND: Graft and patient survival are of great importance after transplantation. This study aimed to determine the long-term survival rate of kidney transplantation and its effective factors among transplanted patients in Mashhad transplantation centers in northeastern Iran. METHODS: Overall, 618 kidney transplant recipients were examined in different transplantation centers during the years from 2000 to 2015 in a historical cohort study. The Kaplan-Meier method and the Log-rank test were used to calculate the survival rate of the kidney transplant, and to check the difference between survival curves respectively. Modeling of effective factors in survival rate was performed using Cox regression model. RESULTS: Overall, 1, 3, 5, 7, 10, and 15-year survival rate of kidney transplantation were 99%, 98%, 97%, 93%, 88 and 70% respectively. The adjusted hazard ratio indicated that variables such as recipient age >40 yr [HR=0.22, 95% CI=(0.071,0.691)], serum creatinine after transplantation >1.6 Mg/dl [HR=3.03, 95% CI=(1.284,7.125)], history of hypertension [HR=6.70, 95% CI=(2.746,16.348)], and BMI [HR (normal weight versus underweight)=0.26, 95% CI=(0.088,0.761), HR (over weight versus underweight)=0.13,95% CI=(0.038,0.442)] were significant factors on kidney transplant survival rate. CONCLUSION: The short-term transplant survival rate was good in transplant patients. What's more, through a consideration of variables such as age, creatinine serum after transplantation, history hypertension and body mass index, as well as proper planning to control their effect, it is possible to improve the long-term graft survival rate.

An intuitive framework for Bayesian posterior simulation methods.

Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account for uncertainty in inference decision-making. Various estimation methods have been introduced to implement Bayesian methods. Although these algorithms are powerful, they are not always easy to grasp for non-statisticians. This paper aims to provide an intuitive framework of four essential Bayesian computational methods for epidemiologists and other health researchers. We do not cover an extensive mathematical discussion of these approaches, but instead offer a non-quantitative description of these algorithms and provide some illuminating examples. Materials and methods: Bayesian computational methods, namely importance sampling, rejection sampling, Markov chain Monte Carlo, and data augmentation are presented. Results and conclusions: The substantial amount of research published on Bayesian inference has highlighted its popularity among researchers, while the basic concepts are not always straightforward for interested learners. We show that alternative approaches such as a weighted prior approach, which are intuitively appealing and easy-to-understand, work well in the case of low-dimensional problems and appropriate prior information. Otherwise, MCMC is a trouble-free tool in those cases.

Spatio-temporal analysis of misaligned burden of disease data using a geo-statistical approach.

Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo-statistical model to combine aggregated sparse, noisy BOD data from different sources with misaligned areal units. Our model incorporates the correlation of space, time, and age to estimate health indicators for areas with no data or a small number of observations. The model also considers the heterogeneity of data sources and the measurement errors of input data in the final estimates and uncertainty intervals. We applied the model to combine data from nine different sources of body mass index in a national and sub-national BOD study. The cross-validation results confirmed a high out-of-sample predictive ability in sparse and noisy data. The proposed model can be used by other BOD studies especially at the sub-national level when the areal units are subject to misalignment.

Bayesian methods for clinicians.

Background: The Bayesian methods have received more attention in medical research. It is considered as a natural paradigm for dealing with applied problems in the sciences and also an alternative to the traditional frequentist approach. However, its concept is somewhat difficult to grasp by nonexperts. This study aimed to explain the foundational ideas of the Bayesian methods through an intuitive example in medical science and to illustrate some simple examples of Bayesian data analysis and the interpretation of results delivered by Bayesian analyses. In this study, data sparsity, as a problem which could be solved by this approach, was presented through an applied example. Moreover, a common sense description of Bayesian inference was offered and some illuminating examples were provided for medical investigators and nonexperts. Methods: Data augmentation prior, MCMC, and Bayes factor were introduced. Data from the Khuzestan study, a 2-phase cohort study, were applied for illustration. Also, the effect of vitamin D intervention on pregnancy outcomes was studied. Results: Unbiased estimate was obtained by the introduced methods. Conclusion: Bayesian and data augmentation as the advanced methods provide sufficient results and deal with most data problems such as sparsity.

Accelerated and Standard Corneal Cross-Linking Protocols in Patients with Down Syndrome: A Non-inferiority Contralateral Randomized Trial.

INTRODUCTION: To compare the results of an accelerated corneal cross-linking (CXL) protocol (9 mW/cm2, 10 min) with the standard CXL protocol (3 mW/cm2, 30 min) in patients with Down syndrome (DS) who have keratoconus (KC). METHODS: Twenty-seven 10- to 20-year-old patients with DS who had bilateral progressive KC were enrolled in a contralateral randomized trial and completed 2 years of follow-up examinations. Fellow eyes were randomly allocated to the accelerated CXL group or the standard CXL group. The main outcome measure was change in maximum keratometry (Kmax) centered on the steepest point (zonal Kmax - 3 mm) with a non-inferiority margin of 1.0 diopter (D). Vision and refraction tests, ophthalmic examinations, and corneal tomography were performed at baseline and at 6, 12, and 24 months after CXL. Failure was defined as an increase of ≥ 1.0 D in zonal Kmax - 3 mm within a 12-month period. RESULTS: The mean age (± standard deviation) of the patients was 15.71 ± 2.40 years. The within-group change in zonal Kmax - 3 mm was not significant after 2 years in either group, and within-group zonal Kmax - 3 mm remained stable. At 2 years after CXL, the mean change in the zonal Kmax - 3 mm was - 0.02 ± 0.81 D and - 0.31 ± 0.86 D in the accelerated CXL and standard CXL groups, respectively (P = 0.088). At 1 year of follow-up, three patients in the accelerated CXL group showed treatment failure (mean change in zonal Kmax - 3 mm + 2.12 ± 0.11 D); no patients in the standard CXL group showed treatment failure. At 2 years of follow-up, these three patients showed a decrease of - 0.43 ± 0.18 D in zonal Kmax - 3 mm from a baseline value of 55.11 ± 0.32 D. The 2-year trends of the inferior-superior asymmetry and vertical coma were statistically significantly different between the two groups, with the accelerated CXL protocol showing superiority in patients with higher baseline values. CONCLUSION: In young patients with Down syndrome, the accelerated CXL protocol was able to halt disease progression and may be an alternative for the standard CXL protocol. In advanced KC, the efficacy of the accelerated approach was delayed and appeared later in the follow-up. In asymmetric cornea, the accelerated CXL resulted in centralization of the corneal cone. TRIAL REGISTRATION: Iranian Registry of Clinical Trials, IRCT20100706004333N3.