A New Method for Imputing Censored Values in Crossover Designs with Time-to-Event Outcomes Using Median Residual Life.

Crossover designs are commonly applied in research due to efficiency and subject parsimony compared to parallel studies. Baseline measurements would improve the power of comparison. For time to event outcomes, the sample size is reduced due to censorship, if they are ignored; thus, applying traditional regression models will be limited. A logical solution is to impute the censored observation and apply common analytical models for analyzing the data. Nevertheless, techniques to impute censored data in time-to-event outcomes in crossover designs are not practiced as much. Accordingly, we propose a method to impute the censored observation using median residual life regression and then analyze the data using analyses of covariance (ANCOVA), considering the difference of period-specific baselines as covariate. We used simulation to show the favorable performance of our method relative to a recently proposed method, multiple imputation with model averaging and ANCOVA (MIMI). Specifically, the censored observations were multiply-imputed using prespecified parametric event time models, and then, the methods were applied to a real data example.

An Efficient and Effective Model to Handle Missing Data in Classification.

Missing data is one of the most important causes in reduction of classification accuracy. Many real datasets suffer from missing values, especially in medical sciences. Imputation is a common way to deal with incomplete datasets. There are various imputation methods that can be applied, and the choice of the best method depends on the dataset conditions such as sample size, missing percent, and missing mechanism. Therefore, the better solution is to classify incomplete datasets without imputation and without any loss of information. The structure of the "Bayesian additive regression trees" (BART) model is improved with the "Missingness Incorporated in Attributes" approach to solve its inefficiency in handling the missingness problem. Implementation of MIA-within-BART is named "BART.m". As the abilities of BART.m are not investigated in classification of incomplete datasets, this simulation-based study aimed to provide such resource. The results indicate that BART.m can be used even for datasets with 90 missing present and more importantly, it diagnoses the irrelevant variables and removes them by its own. BART.m outperforms common models for classification with incomplete data, according to accuracy and computational time. Based on the revealed properties, it can be said that BART.m is a high accuracy model in classification of incomplete datasets which avoids any assumptions and preprocess steps.

Assessing the effect of child's gender on their father-mother perception of the PedsQL™ 4.0 questionnaire: an iterative hybrid ordinal logistic regression/item response theory approach with Monte Carlo simulation.

BACKGROUND: This study aimed at investigating the possible confounding effect of children's gender on the parents' dyads perception of their child HRQoL at both item and scale levels of PedsQLTM4.0 questionnaire. METHODS: The PedsQL™ 4.0 Generic Core Scales were completed by 573 children and their father-and-mother dyads. An iterative hybrid ordinal logistic regression/item response theory model with Monte Carlo simulation was used to detect differential item functioning (DIF) invariance across mothers/fathers and daughter/sons. RESULTS: Assessing DIF across mother-daughter, father-daughter, mother-son, and father-son dyads revealed that although parents and their children perceived the meaning of some items of PedsQLTM4.0 instrument differently, the pattern of fathers' and mothers' report does not vary much across daughters and sons. CONCLUSION: In the Persian version of PedsQLTM4.0, the child's gender is not a confounding factor in the mothers' and fathers' report with respect to their daughters' and sons' HRQoL. Hence, paternal proxy-reports can be included in studies, along with maternal proxy-reports, and the reports can be combined short of concerning children gender, when looking at parent-child agreement.

Improved Kaplan-Meier Estimator in Survival Analysis Based on Partially Rank-Ordered Set Samples.

This study presents a novel methodology to investigate the nonparametric estimation of a survival probability under random censoring time using the ranked observations from a Partially Rank-Ordered Set (PROS) sampling design and employs it in a hematological disorder study. The PROS sampling design has numerous applications in medicine, social sciences and ecology where the exact measurement of the sampling units is costly; however, sampling units can be ordered by using judgment ranking or available concomitant information. The general estimation methods are not directly applicable to the case where samples are from rank-based sampling designs, because the sampling units do not meet the identically distributed assumption. We derive asymptotic distribution of a Kaplan-Meier (KM) estimator under PROS sampling design. Finally, we compare the performance of the suggested estimators via several simulation studies and apply the proposed methods to a real data set. The results show that the proposed estimator under rank-based sampling designs outperforms its counterpart in a simple random sample (SRS).

Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On-Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data.

Landmark model (LM) is a dynamic prediction model that uses a longitudinal biomarker in time-to-event data to make prognosis prediction. This study was designed to improve this model and to apply it to assess the cardiovascular risk in on-treatment blood pressure patients. A frailty parameter was used in LM, landmark frailty model (LFM), to account the frailty of the patients and measure the correlation between different landmarks. The proposed model was compared with LM in different scenarios respecting data missing status, sample size (100, 200, and 400), landmarks (6, 12, 24, and 48), and failure percentage (30, 50, and 100%). Bias of parameter estimation and mean square error as well as deviance statistic between models were compared. Additionally, discrimination and calibration capability as the goodness of fit of the model were evaluated using dynamic concordance index (DCI), dynamic prediction error (DPE), and dynamic relative prediction error (DRPE). The proposed model was performed on blood pressure data, obtained from systolic blood pressure intervention trial (SPRINT), in order to calculate the cardiovascular risk. Dynpred, coxme, and coxphw packages in the R.3.4.3 software were used. It was proved that our proposed model, LFM, had a better performance than LM. Parameter estimation in LFM was closer to true values in comparison to that in LM. Deviance statistic showed that there was a statistically significant difference between the two models. In the landmark numbers 6, 12, and 24, the LFM had a higher DCI over time and the three landmarks showed better performance in discrimination. Both DPE and DRPE in LFM were lower in comparison to those in LM over time. It was indicated that LFM had better calibration in comparison to its peer. Moreover, real data showed that the structure of prognostic process was predicted better in LFM than in LM. Accordingly, it is recommended to use the LFM model for assessing cardiovascular risk due to its better performance.

A multivariate multilevel analysis of the risk factors associated with anthropometric indices in Iranian mid-adolescents.

BACKGROUND: The present study was conducted to jointly assess some specific factors related to body fat measures using a multivariate multilevel analysis in a representative sample of Iranian mid-adolescents. METHODS: This study was conducted among 2538 students (1286 boys) aged 14-20 years old, who were randomly selected among 16 public high schools by multi-stage random sampling procedure from all education districts of Shiraz, Iran. Data on demographic characteristics, family history of obesity, physical activity, socio-economic (SES) variables and screen time were collected. Height, weight, triceps (TST), abdominal (AST), and subscapular (SST) skinfold thickness were measured and their body mass index (BMI) was calculated. A multivariate multilevel approach was used to analyze the factors associated with obesity measures of the TST, AST, SST at the child and district levels. RESULTS: In this study, the prevalence of overweight and obesity was estimated to be 10.2 and 5.1%, respectively. Overall, the major portion of the total variance in TST (97.1%), AST (97.7%), and SST (97.5%) was found at the child level. The results of multivariate multilevel method revealed that being girls, having a family history of obesity, and SES were significantly associated with increasing of three body fat measures (all the p-values were less than 0.05). There were significant positive associations between moderate to vigorous physical activities with AST and SST (for AST: ß =2.54, SE = 1.40, p = 0.05; for SST: ß =2.24, SE = 1.20, p = 0.05). Compared to children in 14-16 age group, children in age group 16-18 years had less TST (ß = - 0.67, SE = 0.34, p = 0.04). Furthermore, other age groups and screen time did not play an important role in three outcome variables. CONCLUSIONS: The results showed some factors that contribute to three body fat measures. Therefore, it is necessary to develop effective interventions to prevent the effects of individual and environmental undesirable factors on childhood obesity in both family and community levels.

Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran.

Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This study proposes a Bayesian spatial joint model of Bernoulli distribution and Poisson distribution to map disease count data with excessive zeros. Here, the spatial random effect is simultaneously considered into both logistic and log-linear models in a Bayesian hierarchical framework. In addition, we focus on the BYM2 model, a re-parameterization of the common BYM model, with penalized complexity priors for the latent level modeling in the joint model and zero-inflated Poisson models with different type of zeros. To avoid model fitting and convergence issues, Bayesian inferences are implemented using the integrated nested Laplace approximation (INLA) method. The models are compared according to the deviance information criterion and the logarithmic scoring. A simulation study with different proportions of zero exhibits INLA ability in running the models and also shows slight differences between the popular BYM and BYM2 models in terms of model choice criteria. In an application, we apply the fitting models on male breast cancer data in Iran at county level in 2014.

Network Clustering and Bibliometrics of Pharmacology and Pharmacy Research Outputs Published by Iranian Authors.

OBJECTIVE: Evidence-based practice in medical sciences needs to publish confidential evidence that strongly depends on the research publications. This bibliometrics and network analytic study aims to evaluate the research publications of Iranian authors in pharmacology and pharmacy. METHODS: Through the pharmacology and pharmacy category of Web of Science (WOS), all published articles affiliated with an Iranian researcher as an author were retrieved. Full records of retrieved articles in the WOS, including author name and affiliation, journal name, citation number, cited references, and keywords, were exported to a plain text file. Network analysis through VOSviewer was used for mapping the characteristics of the retrieved articles. All statistical analyses were done using the Microsoft Excel and SPSS version 25. FINDINGS: The total number of Iran's publications (citations) rose from 1557 articles (10,085 citations) in 2000-2009 years to 6271 articles (77791 citations) in 2010-2018 years. Tehran University of Medical Sciences was the most productive university. The total number of RCTs rose from 82 publications in 2000-2009 to 278 publications in 2010-2018. The same numbers for systematic reviews and meta-analyses were four publications in 2000-2009 and 169 publications in 2010-2018. The five major topics of researches in pharmacology and pharmacy were drug delivery, basic pharmacology, oxidative stress, animal study, and molecular aspect of pharmacy. CONCLUSION: This study showed a marked increasing rate of publications and received citations by Iranians in pharmacology and pharmacy. After 2010, the rate of articles in the high-impact journals had growth. Furthermore, research articles in the highest level of evidence were more published by Iranians.

Prevalence, Awareness, Treatment, Control, and Correlates of Hypertension in the Pars Cohort Study.

BACKGROUND: High blood pressure is an important risk factor for all-cause mortality and cardiovascular mortality and morbidity among Iranians. We aimed to estimate its prevalence, correlates, and its rate of awareness, treatment, and control in South of Iran. METHODS: The Pars Cohort Study (PCS) was launched in a district of Fars province. All residents between 40 and 75 years old in the district were recruited from 2012 to 2014. Hypertension was defined as either systolic/diastolic blood pressure ≥ 140/90 mm Hg or taking medications. Logistic regression was used to identify the correlates of hypertension and awareness and its treatment and control. A total of 9264 participants were recruited. Of the total participants, 46.2% were men. The mean age was 52.6 years (SD: 9.7). RESULTS: Prevalence of hypertension was 26.9%. Of the total 2489 hypertensives, 49.6% were aware and 55.7% were under treatment. Blood pressure was controlled in 69.2% of treated hypertensives. In the adjusted model, female sex and history of cardiovascular disease (CVD) were positively associated with having hypertension, higher awareness, and better treatment and control. Older age, being overweight or obese, and having a history of diabetes were also positively associated with having hypertension and higher awareness and treatment; however, being overweight or obese was associated with poorer hypertension control. Older age and having a history of diabetes did not show a statistically significant association with control. CONCLUSION: Being underweight and higher physical activity were inversely associated with having hypertension but were not associated with awareness, treatment, or control. Prevalence of hypertension is high but the rates of awareness, treatment, and control are not adequate.

The Effect of Cross-loading on Measurement Equivalence of Psychometric Multidimensional Questionnaires in MIMIC Model: a Simulation Study.

INTRODUCTION: In recent years, Multiple Indicators Multiple Causes (MIMIC) model has been widely used to assess measurement in variance, called Differential Item Functioning (DIF) analyses, in psychological and medical studies. AIM: This simulation study aimed at assessing the effect of sample size, scale length, and magnitude of the uniform-DIF on detecting uniform-DIF with the MIMIC model when it has cross-loading in multidimensional scales. MATERIAL AND METHODS: In this Monte Carlo simulation study, we calculated power, Type I error rates, the bias of parameters estimation, Coverage Probability (CP), and Convergence Rate (CR) was used to assess the performance of the MIMIC model. The means of RMSEA, SRMR, CFI, and TLI, as indices of the goodness-of-fit for the MIMIC model, were computed across 1000 replications for each simulation condition. RESULT: Approximately, in all scenarios simulated, the bias of DIF parameters estimation was negligible. The existence of cross-loading caused a decrease of approximately 11.8% in the power and increase of 0.04-unit in bias parameter estimation. By increasing the relationship between dimensions, the power and CP of MIMIC model decreased, however, bias and CR were increased. In all scenarios that were performed in this study, all goodness-of-fit indices were at an acceptable level. CONCLUSION: Our results indicated that the performance of the MIMIC model improved, when sample size, the number of items, and the magnitude of DIF increased. When the scale is multidimensional and model have cross-loading, the performance of the MIMIC model becomes questionable.

A Proposed Approach for Joint Modeling of the Longitudinal and Time-To-Event Data in Heterogeneous Populations: An Application to HIV/AIDS's Disease.

In recent years, the joint models have been widely used for modeling the longitudinal and time-to-event data simultaneously. In this study, we proposed an approach (PA) to study the longitudinal and survival outcomes simultaneously in heterogeneous populations. PA relaxes the assumption of conditional independence (CI). We also compared PA with joint latent class model (JLCM) and separate approach (SA) for various sample sizes (150, 300, and 600) and different association parameters (0, 0.2, and 0.5). The average bias of parameters estimation (AB-PE), average SE of parameters estimation (ASE-PE), and coverage probability of the 95% confidence interval (CP) among the three approaches were compared. In most cases, when the sample sizes increased, AB-PE and ASE-PE decreased for the three approaches, and CP got closer to the nominal level of 0.95. When there was a considerable association, PA in comparison with SA and JLCM performed better in the sense that PA had the smallest AB-PE and ASE-PE for the longitudinal submodel among the three approaches for the small and moderate sample sizes. Moreover, JLCM was desirable for the none-association and the large sample size. Finally, the evaluated approaches were applied on a real HIV/AIDS dataset for validation, and the results were compared.

A comparison of breast cancer survival among young, middle-aged, and elderly patients in southern Iran using Cox and empirical Bayesian additive hazard models.

OBJECTIVES: A survival analysis of breast cancer patients in southern Iran according to age has yet to be conducted. This study aimed to quantify the factors contributing to a poor prognosis, using Cox and empirical Bayesian additive hazard (EBAH) models, among young (20-39 years), middle-aged (40-64 years), and elderly (≥ 65 years) women. METHODS: Data from 1,574 breast cancer patients diagnosed from 2002 to 2012 in the cancer registry of Fars Province (southern Iran) were stratified into 3 age groups. The Kaplan-Meier method was used to estimate the overall survival rates. Cox and EBAH models were applied to each age category, and the Akaike information criterion was used to assess the goodness-of-fit of the 2 hazard models. RESULTS: As of December 2012, 212 women (13.5%) in our study population had died, of whom 43 were young (15.3%), 134 middle-aged (11.8%), and 35 elderly (22.3%). The 5-year survival probability by age category was 0.83 (standard error [SE], 0.03), 0.88 (SE, 0.01), and 0.75 (SE, 0.04), respectively. CONCLUSIONS: The Nottingham Prognostic Index was the most effective prognostic factor. The model based on Bayesian methodology performed better with various sample sizes than the Cox model, which is the most widely used method of survival analysis.

A comparative study of the impacts of unbalanced sample sizes on the four synthesized methods of meta-analytic structural equation modeling.

BACKGROUND: In the first stage of meta-analytic structural equation modeling (MASEM), researchers synthesized studies using univariate meta-analysis (UM) and multivariate meta-analysis (MM) approaches. The MM approaches are known to be of better performance than the UM approaches in the meta-analysis with equal sized studies. However in real situations, where the studies might be of different sizes, the empirical performance of these approaches is yet to be studied in the first and second stages of MASEM. The present study aimed to evaluate the performance of the UM and MM methods, having unequal sample sizes in different primary studies. Testing the homogeneity of correlation matrices and the empirical power, estimating the pooled correlation matrix and also, estimating parameters of a path model were investigated using these approaches by simulation. RESULTS: The results of the first stage showed that Type I error rate was well under control at 0.05 level when the average sample sizes were 200 or more, irrespective of the types of the methods or the sample sizes used. Moreover, the relative percentage biases of the pooled correlation matrices were also lower than 2.5% for all methods. There was a dramatic decrease in the empirical power for all synthesis methods when the inequality of the sample sizes was increased. In fitting the path model at the second stage, MM methods provided better estimation of the parameters. CONCLUSIONS: This study showed the different performance of the four methods in the statistical power, especially when the sample sizes of primary studies were highly unequal. Moreover, in fitting the path model, the MM approaches provided better estimation of the parameters.

The Effect of Small Sample Size on Measurement Equivalence of Psychometric Questionnaires in MIMIC Model: A Simulation Study.

Evaluating measurement equivalence (also known as differential item functioning (DIF)) is an important part of the process of validating psychometric questionnaires. This study aimed at evaluating the multiple indicators multiple causes (MIMIC) model for DIF detection when latent construct distribution is nonnormal and the focal group sample size is small. In this simulation-based study, Type I error rates and power of MIMIC model for detecting uniform-DIF were investigated under different combinations of reference to focal group sample size ratio, magnitude of the uniform-DIF effect, scale length, the number of response categories, and latent trait distribution. Moderate and high skewness in the latent trait distribution led to a decrease of 0.33% and 0.47% power of MIMIC model for detecting uniform-DIF, respectively. The findings indicated that, by increasing the scale length, the number of response categories and magnitude DIF improved the power of MIMIC model, by 3.47%, 4.83%, and 20.35%, respectively; it also decreased Type I error of MIMIC approach by 2.81%, 5.66%, and 0.04%, respectively. This study revealed that power of MIMIC model was at an acceptable level when latent trait distributions were skewed. However, empirical Type I error rate was slightly greater than nominal significance level. Consequently, the MIMIC was recommended for detection of uniform-DIF when latent construct distribution is nonnormal and the focal group sample size is small.

Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models.

One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.

Pars cohort study of non-communicable diseases in Iran: protocol and preliminary results.

OBJECTIVES: The pars cohort study (PCS) is a 10-year cohort study aiming to investigate the burden and the major risk factors of non-communicable diseases, and to establish a setting to launch interventions for prevention of these diseases and controlling their risk factors. METHODS: All inhabitants of Valashahr district in South of Iran, aged 40-75 years, were invited to undergo interviews and physical examination, and to provide biological samples. A total of 9264 invitees accepted to participate in the study (95 % participation rate) and were recruited from 2012 to 2014. Active follow-up was also carried out after 12 months. RESULTS: About 46 % of participants were male and 54 % were female. About 14.0 % of the participants were current smokers and 8.4 % were ever opium users. The prevalence of overweight and obesity were 37.3 and 18.2 %, respectively. The prevalence of hypertension was 26.9 %. A total of 49 participants died during a median follow-up of one year. CONCLUSIONS: PCS with its large scale and wealth of socio-economic and medical data can be a unique platform for studying the etiology of non-communicable diseases and effective interventions in Iran.

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data.

BACKGROUND: The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. METHODS: The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. RESULTS: Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. CONCLUSIONS: The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.

Identifying the Prognosis Factors in Death after Liver Transplantation via Adaptive LASSO in Iran.

Despite the widespread use of liver transplantation as a routine therapy in liver diseases, the effective factors on its outcomes are still controversial. This study attempted to identify the most effective factors on death after liver transplantation. For this purpose, modified least absolute shrinkage and selection operator (LASSO), called Adaptive LASSO, was utilized. One of the best advantages of this method is considering high number of factors. Therefore, in a historical cohort study from 2008 to 2013, the clinical findings of 680 patients undergoing liver transplant surgery were considered. Ridge and Adaptive LASSO regression methods were then implemented to identify the most effective factors on death. To compare the performance of these two models, receiver operating characteristic (ROC) curve was used. According to the results, 12 factors in Ridge regression and 9 ones in Adaptive LASSO regression were significant. The area under the ROC curve (AUC) of Adaptive LASSO was equal to 89% (95% CI: 86%-91%), which was significantly greater than Ridge regression (64%, 95% CI: 61%-68%) (p < 0.001). As a conclusion, the significant factors and the performance criteria revealed the superiority of Adaptive LASSO method as a penalized model versus traditional regression model in the present study.

A New Measurement Equivalence Technique Based on Latent Class Regression as Compared with Multiple Indicators Multiple Causes.

BACKGROUND: Measurement equivalence is an essential prerequisite for making valid comparisons in mental health questionnaires across groups. In most methods used for assessing measurement equivalence, which is known as Differential Item Functioning (DIF), latent variables are assumed to be continuous. OBJECTIVE: To compare a new method called Latent Class Regression (LCR) designed for discrete latent variable with the multiple indicators multiple cause (MIMIC) as a continuous latent variable technique to assess the measurement equivalence of the 12-item General Health Questionnaire (GHQ-12), which is a cross deferent subgroup of Iranian nurses. METHODS: A cross-sectional survey was conducted in 2014 among 771 nurses working in the hospitals of Fars and Bushehr provinces of southern Iran. To identify the Minor Psychiatric Disorders (MPD), the nurses completed self-report GHQ-12 questionnaires and sociodemographic questions. Two uniform-DIF detection methods, LCR and MIMIC, were applied for comparability when the GHQ-12 score was assumed to be discrete and continuous, respectively. RESULTS: The result of fitting LCR with 2 classes indicated that 27.4% of the nurses had MPD. Gender was identified as an influential factor of the level of MPD.LCR and MIMIC agree with detection of DIF and DIF-free items by gender, age, education and marital status in 83.3, 100.0, 91.7 and 83.3% cases, respectively. CONCLUSIONS: The results indicated that the GHQ-12 is to a great degree, an invariant measure for the assessment of MPD among nurses. High convergence between the two methods suggests using the LCR approach in cases of discrete latent variable, e.g. GHQ-12 and adequate sample size.

The Reliability of Classification of Terminal Nodes in GUIDE Decision Tree to Predict the Nonalcoholic Fatty Liver Disease.

Tree structured modeling is a data mining technique used to recursively partition a dataset into relatively homogeneous subgroups in order to make more accurate predictions on generated classes. One of the classification tree induction algorithms, GUIDE, is a nonparametric method with suitable accuracy and low bias selection, which is used for predicting binary classes based on many predictors. In this tree, evaluating the accuracy of predicted classes (terminal nodes) is clinically of special importance. For this purpose, we used GUIDE classification tree in two statuses of equal and unequal misclassification cost in order to predict nonalcoholic fatty liver disease (NAFLD), considering 30 predictors. Then, to evaluate the accuracy of predicted classes by using bootstrap method, first the classification reliability in which individuals are assigned to a unique class and next the prediction probability reliability as support for that are considered.