Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics

The goal of this paper is to present an implementation of stochastic search variable selection (SSVS) to multilevel model from item response theory (IRT). As experimental settings get more complex and models are required to integrate multiple (and sometimes massive) sources of information, a model that can jointly summarize and select the most relevant characteristics can provide better interpretation and a deeper insight into the problem. A multilevel IRT model recently proposed in the literature for modeling multifactorial diseases is extended to perform variable selection in the presence of thousands of covariates using SSVS. We derive conditional distributions required for such a task as well as an acceptance‐rejection step that allows for the SSVS in high dimensional settings using a Markov Chain Monte Carlo algorithm. We validate the variable selection procedure through simulation studies, and illustrate its application on a study with genetic markers associated with the metabolic syndrome.     

An Investigation of Age-Related Differential Item Functioning in the EQ-5D-5L Using Item Response Theory and Logistic Regression

ObjectivesIn economic evaluations, quality of life is measured using patient-reported outcome measures (PROMs), such as the EQ-5D-5L. A key assumption for the validity of PROMs data is measurement invariance, which requires that PROM items and response options are interpreted the same across respondents. If measurement invariance is violated, PROMs exhibit differential item functioning (DIF), whereby individuals from different groups with the same underlying health respond differently, potentially biasing scores. One important group of healthcare consumers who have been shown to have different views or priorities over health is older adults. This study investigates age-related DIF in the EQ-5D-5L using item response theory (IRT) and ordinal logistic regression approaches.MethodsMultiple-group IRT models were used to investigate DIF, by assessing whether older adults aged 65+ years and younger adults aged 18 to 64 years with the same underlying health had different IRT parameter estimates and expected item and EQ-5D-5L level sum scores. Ordinal logistic regression was also used to examine whether DIF resulted in meaningful differences in expected EQ level sum scores. Effect sizes examined whether DIF indicated meaningful score differences.ResultsThe anxiety/depression item exhibited meaningful DIF in both approaches, with older adults less likely to report problems. Pain/discomfort and mobility exhibited DIF to a lesser extent.ConclusionsWhen using the EQ-5D-5L to evaluate interventions and make resource allocation decisions, scoring bias due to DIF should be controlled for to prevent inefficient service provision, where the most cost-effective services are not provided, which could be detrimental to patients and the efficiency of health budgets.     

Unveiling the Correlation between Inadequate Energy/Macronutrient Intake and Clinical Alterations in Volunteers at Risk of Metabolic Syndrome by a Predictive Model

Although lifestyle-based interventions are the most effective to prevent metabolic syndrome (MetS), there is no definitive agreement on which nutritional approach is the best. The aim of the present retrospective analysis was to identify a multivariate model linking energy and macronutrient intake to the clinical features of MetS. Volunteers at risk of MetS (F = 77, M = 80) were recruited in four European centres and finally eligible for analysis. For each subject, the daily energy and nutrient intake was estimated using the EPIC questionnaire and a 24-h dietary recall, and it was compared with the dietary reference values. Then we built a predictive model for a set of clinical outcomes computing shifts from recommended intake thresholds. The use of the ridge regression, which optimises prediction performances while retaining information about the role of all the nutritional variables, allowed us to assess if a clinical outcome was manly dependent on a single nutritional variable, or if its prediction was characterised by more complex interactions between the variables. The model appeared suitable for shedding light on the complexity of nutritional variables, which effects could be not evident with univariate analysis and must be considered in the framework of the reciprocal influence of the other variables.     

Combining Family and Twin Data in Association Studies to Estimate the Noninherited Maternal Antigens Effect

It is hypothesized that certain alleles can have a protective effect not only when inherited by the offspring but also as noninherited maternal antigens (NIMA). To estimate the NIMA effect, large samples of families are needed. When large samples are not available, we propose a combined approach to estimate the NIMA effect from ascertained nuclear families and twin pairs. We develop a likelihood‐based approach allowing for several ascertainment schemes, to accommodate for the outcome‐dependent sampling scheme, and a family‐specific random term, to take into account the correlation between family members. We estimate the parameters using maximum likelihood based on the combined joint likelihood () approach. Simulations show that the is more efficient for estimating the NIMA odds ratios as compared to a families‐only approach. To illustrate our approach, we used data from a family and a twin study from the United Kingdom on rheumatoid arthritis, and confirmed the protective NIMA effect, with an odds ratio of 0.477 (95% CI 0.264–0.864).     

Multiple imputation of completely missing repeated measures data within person from a complex sample: application to accelerometer data in the National Health and Nutrition Examination Survey

The Physical Activity Monitor component was introduced into the 2003–2004 National Health and Nutrition Examination Survey (NHANES) to collect objective information on physical activity including both movement intensity counts and ambulatory steps. Because of an error in the accelerometer device initialization process, the steps data were missing for all participants in several primary sampling units, typically a single county or group of contiguous counties, who had intensity count data from their accelerometers. To avoid potential bias and loss in efficiency in estimation and inference involving the steps data, we considered methods to accurately impute the missing values for steps collected in the 2003–2004 NHANES. The objective was to come up with an efficient imputation method that minimized model‐based assumptions. We adopted a multiple imputation approach based on additive regression, bootstrapping and predictive mean matching methods. This method fits alternative conditional expectation (ace) models, which use an automated procedure to estimate optimal transformations for both the predictor and response variables. This paper describes the approaches used in this imputation and evaluates the methods by comparing the distributions of the original and the imputed data. A simulation study using the observed data is also conducted as part of the model diagnostics. Finally, some real data analyses are performed to compare the before and after imputation results. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.     

The Difference in the Prevalence of Metabolic Syndrome According to Meeting Guidelines for Aerobic Physical Activity and Muscle-Strengthening Exercise: A Cross-Sectional Study Performed Using the Korea National Health and Nutrition Examination Survey, 2014–2019

Physical activity and muscle strengthening are essential for preventing and managing metabolic syndrome. This study was conducted to investigate the relationship between the prevalence of metabolic syndrome and meeting the guidelines for aerobic physical activity (APA), muscle strengthening exercise (MSE), and combined exercise. We used data from 22,467 Koreans aged 40 years or older, who participated in in the Korea National Health and Nutrition Examination Survey (KNHANES) 2014–2019. We used the Global Physical Activity Questionnaire (GPAQ) to measure physical activity and surveyed frequency of MSE through a questionnaire. Metabolic syndrome was defined according to the American heart association and the National Heart, Lung, and Blood Institute. Compared with none exercise group, odds ratios of APA, MSE, and combined exercise group (CEG) on metabolic syndrome prevalence were 0.85 (95% confidence interval (CI), 0.74–0.98), 0.81 (95% CI, 0.67–0.99), and 0.65 (95% CI, 0.54–0.78) among men, respectively. Among women, ORs of APA, MSE, and CEG were 0.83 (95% CI, 0.73–0.93), 0.73 (95% CI, 0.58–0.91), and 0.74 (95% CI, 0.58–0.93), respectively. This study showed that meeting guidelines for APA and MSE was associated with lower prevalence of metabolic syndrome. Furthermore, subjects who met both APA and MSE had the lowest metabolic syndrome prevalence.