Superiority of Bayesian Model Averaging to Stepwise Model in Selection of Factors Related to the Incidence of Type II diabetes in Pre-diabetic Women

Introduction:  The world prevalence of type 2 diabetes and its related increment mortality rate which needs high controls cost has attracted high scientific attention. Early detection of individuals who face this disease more than the others can prevent getting sick or at least reduce the disease consequences on public health. Regarding the costs and limitations of diagnostic tests, a statistical model is presented that helps predict the time of diabetes incidence and determines its risk factors. Furthermore, this model determines the significant predictor variables on response and considers them as model equation parameters.Materials and Methods: In this study, 803 pre-diabetic women in the age range of more than 20 years were selected from Tehran lipid and glucose study (TLGS) to examine the predictor variables on time of diabetes incidence. They were entered into the study in the phases 1 and 2 and were followed up to the phase 4. The predictor variables selection was performed using the Stepwise Model (SM) and the Bayesian Model Averaging (BMA). Then, the predictive discrimination was used to compare the results of both models. The Log-rank test was performed and the Kaplan-Meier Curve was plotted. The statistical analyses were performed using R software (version 3.1.3).Results: The Backward Stepwise Model (BSM), the Forward Stepwise Model (FSM) and the BMA have used 9, 10 and 6 variables, respectively. Although the BMA selected predictor variables number is much lower than the SM, the prediction ability remains nearly constant.Conclusions: The BMA has averaged on the supported models using dataset. This model has shown nearly constant accuracy despite the selection of lower predictor variables number in comparison to the SM

The magnitude of black/hispanic disparity in COVID-19 mortality across United States Counties during the first waves of the COVID-19 Pandemic

Objectives: To quantify the Black/Hispanic disparity in COVID-19 mortality in the United States (US). Methods: COVID-19 deaths in all US counties nationwide were analyzed to estimate COVID-19 mortality rate ratios by county-level proportions of Black/Hispanic residents, using mixed-effects Poisson regression. Excess COVID-19 mortality counts, relative to predicted under a counterfactual scenario of no racial/ethnic disparity gradient, were estimated. Results: County-level COVID-19 mortality rates increased monotonically with county-level proportions of Black and Hispanic residents, up to 5.4-fold (=43% Black) and 11.6-fold (=55% Hispanic) higher compared to counties with <5% Black and <15% Hispanic residents, respectively, controlling for county-level poverty, age, and urbanization level. Had this disparity gradient not existed, the US COVID-19 death count would have been 92.1% lower (177,672 fewer deaths), making the rate comparable to other high-income countries with substantially lower COVID-19 death counts. Conclusion: During the first 8 months of the SARS-CoV-2 pandemic, the US experienced the highest number of COVID-19 deaths. This COVID-19 mortality burden is strongly associated with county-level racial/ethnic diversity, explaining most US COVID-19 deaths.Peer ReviewedPostprint (published version

A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results

Objectives: Identifying an appropriate set of predictors for the outcome of interest is a major challenge in clinical prediction research. The aim of this study was to show the application of some variable selection methods, usually used in data mining, for an epidemiological study. We introduce here a systematic approach. Study Design and Setting: The P-value-based method, usually used in epidemiological studies, and several filter and wrapper methods were implemented to select the predictors of diabetes among 55 variables in 803 prediabetic females, aged >= 20 years, followed for 10-12 years. To develop a logistic model, variables were selected from a train data set and evaluated on the test data set. The measures of Akaike information criterion (AIC) and area under the curve (AUC) were used as performance criteria. We also implemented a full model with all 55 variables. Results: We found that the worst and the best models were the full model and models based on the wrappers, respectively. Among filter methods, symmetrical uncertainty gave both the best AUC and AIC. Conclusion: Our experiment showed that the variable selection methods used in data mining could improve the performance of clinical prediction models

Factors associated with incidence of type II diabetes in pre-diabetic women using Bayesian Model Averaging

Introduction: Diabetes is a chronic disease which usually begins with impaired glucose tolerance. This step is known as pre-diabetes. People with pre-diabetes are at greater risk for diabetes. Typically for the variable selection, stepwise approach is used which does not take into account model uncertainties. In this study, Bayesian Model Averaging (BMA) method was used to sort out the above shortcoming. Materials and Methods: The study population was 734 pre-diabetic women with 20 years and older participated in Tehran Lipid and Glucose Study (TLGS). In this study, the stepwise and BMA variable selection methods were employed in logistic regression. Then area under curve (AUC) for both methods was computed and compared with Delong test. All analyses was done using R version 3.1.3. Results: BMA selected the fasting plasma glucose, 2 hours&rsquo; blood glucose, and family history of diabetes, body mass index and aspirin use at baseline as risk factors for diabetic. In addition to these factors, stepwise method selected diastolic blood pressure, history of past 3 months&rsquo; hospitalization, thyroid drug use and education. Although the number of variables selected by BMA (5 variables) was less than that of stepwise (9 variables), AUC for the two methods was not significant. Conclusion: It seems that the BMA provide better model for screening of diabetes because with selecting fewer variables, prediction ability of the model is preserve

Risk Factors for Incidence of Cardiovascular Diseases and All-Cause Mortality in a Middle Eastern Population over a Decade Follow-up: Tehran Lipid and Glucose Study.

To examine the association between potentially modifiable risk factors with cardiovascular disease (CVD) and all-cause mortality and to quantify their population attributable fractions (PAFs) among a sample of Tehran residents.Overall, 8108 participants (3686 men) aged≥30 years, were investigated. To examine the association between risk factors and outcomes, multivariate sex-adjusted Cox proportional hazard regression analysis were conducted, using age as time-scale in two models including general/central adiposity: 1)adjusted for different independent variables including smoking, education, family history of CVD and sex for both outcomes and additionally adjusted for prevalent CVD for all-cause mortality 2)further adjusted for obesity mediators (hypertension, diabetes, lipid profile and chronic kidney disease). Separate models were used including either general or central adiposity.During median follow-up of >10 years, 827 first CVD events and 551 deaths occurred. Both being overweight (hazard ratio (HR), 95%CI: 1.41, 1.18-1.66, PAF 13.66) and obese (1.51, 1.24-1.84, PAF 9.79) played significant roles for incident CVD in the absence of obesity mediators. Predicting CVD, in the presence of general adiposity and its mediators, significant positive associations were found for hypercholesterolemia (1.59, 1.36-1.85, PAF 16.69), low HDL-C (1.21, 1.03-1.41, PAF 12.32), diabetes (1.86, 1.57-2.27, PAF 13.87), hypertension (1.79, 1.46-2.19, PAF 21.62) and current smoking (1.61, 1.34-1.94, PAF 7.57). Central adiposity remained a significant positive predictor, even after controlling for mediators (1.17, 1.01-1.35, PAF 7.55). For all-cause mortality, general/central obesity did not have any risk even in the absence of obesity mediators. Predictors including diabetes (2.56, 2.08-3.16, PAF 24.37), hypertension (1.43, 1.11-1.84, PAF 17.13), current smoking (1.75, 1.38-2.22, PAF 7.71), and low education level (1.59, 1.01-2.51, PAF 27.08) were associated with higher risk, however, hypertriglyceridemia (0.83, 0.68-1.01) and being overweight (0.71, 0.58-0.87) were associated with lower risk.Modifiable risk factors account for more than 70% risk for both CVD and mortality events

Multivariate-adjusted hazard ratios and 95% confidence intervals (CIs) of potential cardiovascular risk factors for incident CVD and all-cause mortality, with obesity mediators (diabetes, hypertension, lipid profile and CKD), controlling for general adiposity status, Tehran lipid and Glucose study (1999–2012)

<p>Multivariate-adjusted hazard ratios and 95% confidence intervals (CIs) of potential cardiovascular risk factors for incident CVD and all-cause mortality, with obesity mediators (diabetes, hypertension, lipid profile and CKD), controlling for general adiposity status, Tehran lipid and Glucose study (1999–2012)</p

Baseline characteristics^* of respondent and non-respondent participants in both genders; Tehran Lipid and Glucose Study 1999–2005.

<p>Baseline characteristics^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167623#t002fn001" target="_blank">*</a>} of respondent and non-respondent participants in both genders; Tehran Lipid and Glucose Study 1999–2005.</p

Multivariate-adjusted hazard ratios and 95% confidence intervals (CIs) of potential cardiovascular risk factors for incident CVD and all-cause mortality, without obesity mediators controlling for general adiposity status, Tehran lipid and Glucose study (1999–2012)

<p>Multivariate-adjusted hazard ratios and 95% confidence intervals (CIs) of potential cardiovascular risk factors for incident CVD and all-cause mortality, without obesity mediators controlling for general adiposity status, Tehran lipid and Glucose study (1999–2012)</p