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.

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.

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.

Comparing the role of standard references on the prevalence of Iranian children and adolescents' overweight and obesity: A systematic review and meta-analysis.

BACKGROUND: Obesity is a major risk factor for chronic diseases and has a role on high blood pressure, diabetes type II, etc., This review assesses the prevalence of Iranian children obesity and overweight for different age categories and compares the three standard definitions of obesity. MATERIALS AND METHODS: To retrieve desirable studies concerning childhood anthropometric data from different area of Iran, the MEDLINE, Scopus, and different local databases such as Scientific Information database were used. The studies reported the prevalence of obesity or overweight of children < 6, 6-12, and 12-20 years old, despite differences between definitions of childhood obesity, were included in the study. We combined the reported prevalence of the overweight and obesity with regard to age and gender, and also by the different standard references which are the Centers for Disease Control and Prevention (CDC), the World Health Organization (WHO) definition, and the International Obesity Task Force (IOTF) references. The analysis was carried out using STATA software. RESULTS: Our review covered 75 articles reported the prevalence of overweight or obesity among children and adolescents for different age groups in Iran. Our meta-regression analysis showed that the prevalence of obesity and overweight did not vary significantly in gender and age categories, but different definitions provide different prevalence of overweight and obesity. CONCLUSION: The effective factors on obesity and overweight included administration policy and organizational, interpersonal, intrapersonal, and social factors. CDC and WHO references intended in monitoring children's growth and the IOTF cutoffs would rather provide a common set of definitions that researchers and policymakers could use for descriptive and comparative purposes.

Testing parent dyad interchangeability in the parent proxy-report of PedsQL™ 4.0: a differential item functioning analysis.

PURPOSE: In child-parent agreement studies in the field of paediatric health-related quality of life (HRQoL), little attention has been paid to the effect of gender in parental proxy rating of children's HRQoL. This study aims to test the potential interchangeability of parent dyads in reporting children's HRQoL on both item and scale levels of the PedsQL™ 4.0 instrument, using the approach of differential item functioning (DIF). METHODS: The PedsQL™ 4.0 Generic Core Scales were completed by 576 father-and-mother dyads. A polytomous item response theory model, graded response model, was used to detect DIF across fathers and mothers. RESULT: Assessment at item level showed that fathers and mothers perceived the meaning of items of the PedsQL™ 4.0 consistently. Regarding the scale level, a moderate to high level of agreement was observed between mothers' and fathers' reports on all similar subscales. Although the significant mean score differences in total, physical and emotional functioning indicated that fathers gave higher scores to their children, the small effect size implied that this difference may not be practically meaningful. CONCLUSION: Our findings revealed that discrepancy in parent dyads in rating children's HRQoL is a "real" difference and not an artefact due to measurement non-invariance. Fathers were seen to have slightly different insights into their children, especially for emotional functioning, but overall the results were not all that different. This suggests that paternal proxy-reports can be included in studies along with maternal proxy-reports, and the two may be combined when looking at parent-child agreement. Parent-child agreement studies in Iran are not affected by parents' gender, and therefore, researchers may rely on the assumption of the interchangeability of fathers and mothers in these studies.

Quality of life of childbearing age women and its associated factors: an application of seemingly unrelated regression (SUR) models.

PURPOSE: This article is a report of using seemingly unrelated regression (SUR) models to examine the determinants of different dimensions of quality of life (QoL) among childbearing age women. There are a limited number of studies on QoL and its associated factors among women in developing countries such as Iran. Therefore, more attention should be focused on identifying these issues. METHODS: We administered the Persian's abbreviated version of the World Health Organization Quality of Life (WHOQOL-BREF) questionnaire to 1,067 married women aged between 15 and 49 years. The women were chosen via a multistage research design from the rural region of Shiraz, the center of Fars Province in Iran in 2008. Clinical and socio-demographic characteristics as well as their reproductive health-related characteristics were investigated. To identify associated factors of QoL dimensions, ordinary least squares (OLS) regression and SUR were used and their findings were compared. RESULTS: The WHOQOL-BREF showed acceptable consistency (Cronbach's alpha range: 0.62-0.75 across domains). Lower age, absence of long-term illness, economic status satisfaction, higher level of education, lower number of pregnancies, and higher body mass index were important associated factors of different dimensions of the QoL among these women. The estimated parameters for these factors were in close agreement in both OLS and SUR estimation methods. However, the SUR estimator provided the higher precision of the estimates than the OLS estimator, as the parameters obtained by SUR are characterized by lower standard errors. Women's age, income satisfaction, and level of education were common for all domains. CONCLUSIONS: This study presents a novel approach to simultaneously predict QoL domains using the SUR estimators and the results are relevant for implementing objective QoL. SUR estimators performed consistently better than the OLS estimators, since SUR takes the correlation between error terms into account. Thus, the SUR method could be a useful methodology for predicting QoL domains.

A parametric method for cumulative incidence modeling with a new four-parameter log-logistic distribution.

BACKGROUND: Competing risks, which are particularly encountered in medical studies, are an important topic of concern, and appropriate analyses must be used for these data. One feature of competing risks is the cumulative incidence function, which is modeled in most studies using non- or semi-parametric methods. However, parametric models are required in some cases to ensure maximum efficiency, and to fit various shapes of hazard function. METHODS: We have used the stable distributions family of Hougaard to propose a new four-parameter distribution by extending a two-parameter log-logistic distribution, and carried out a simulation study to compare the cumulative incidence estimated with this distribution with the estimates obtained using a non-parametric method. To test our approach in a practical application, the model was applied to a set of real data on fertility history. CONCLUSIONS: The results of simulation studies showed that the estimated cumulative incidence function was more accurate than non-parametric estimates in some settings. Analyses of real data indicated that the proposed distribution showed a much better fit to the data than the other distributions tested. Therefore, the new distribution is recommended for practical applications to parameterize the cumulative incidence function in competing risk settings.

Comparison of three tests of homogeneity of odds ratios in multicenter trials with unequal sample sizes within and among centers.

BACKGROUND: Mixed effects logistic models have become a popular method for analyzing multicenter clinical trials with binomial data. However, the statistical properties of these models for testing homogeneity of odds ratios under various conditions, such as within-center and among-centers inequality, are still unknown and not yet compared with those of commonly used tests of homogeneity. METHODS: We evaluated the effect of within-center and among-centers inequality on the empirical power and type I error rate of the three homogeneity tests of odds ratios including likelihood ratio (LR) test of a mixed logistic model, DerSimonian-Laird (DL) statistic and Breslow-Day (BD) test by simulation study. Moreover, the impacts of number of centers (K), number of observations in each center and amount of heterogeneity were investigated by simulation. RESULTS: As compared with the equal sample size design, the power of the three tests of homogeneity will decrease if the same total sample size, which can be allocated equally within one center or among centers, is allocated unequally. The average reduction in the power of these tests was up to 11% and 16% for within-center and among-centers inequality, respectively. Moreover, in this situation, the ranking of the power of the homogeneity tests was BD≥DL≥LR and the power of these tests increased with increasing K. CONCLUSIONS: This study shows that the adverse effect of among-centers inequality on the power of the homogeneity tests was stronger than that of within-center inequality. However, the financial limitations make the use of unequal sample size designs inevitable in multicenter trials. Moreover, although the power of the BD is higher than that of the LR when K≤6, the proposed mixed logistic model is recommended when K≥8 due to its practical advantages.

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 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.

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).

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.

Triceps skinfold thickness centile charts in primary school children in Shiraz, Iran.

Triceps skinfold thickness charts of a random sample of 2,234 healthy school children (1,161 boys and 1,073 girls) in Shiraz, Iran are presented. Triceps skinfold thickness increases by age and is higher in girls than boys, except for upper extreme centiles. Triceps skinfold thickness may be used with reasonable success to detect childhood obesity, which would be of great importance in public health promotion. It favors adequacy and simplicity in screening for adiposity. The charts presented here are likely to be applied to urban population of school-aged children in Iran, however, it should be updated periodically.

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.

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.

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.

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.

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.