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- Barkhordari, Mahnaz, et al.
(author)
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Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models
- 2016
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In: International Journal of Endocrinology and Metabolism. - : Briefland. - 1726-9148 .- 1726-913X. ; 14:1
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Journal article (peer-reviewed)abstract
- Background: Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers.Objectives: Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills.Materials and Methods: We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm.Results: The command is addpred for logistic regression models.Conclusions: The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.
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| 2. |
- Barkhordari, Mahnaz, et al.
(author)
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Survival Regression Modeling Strategies in CVD Prediction
- 2016
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In: International journal of endocrinology and metabolism. - : Research Institute For Endocrine Sciences and Iran Endocrine Society. - 1726-9148 .- 1726-913X. ; 14:2
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Journal article (peer-reviewed)abstract
- Background: A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. Objectives: User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. Materials and Methods: We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X-2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. Results: The command is adpredsurv for survival models. Conclusions: Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this work encourages the use of novel methods in examining predictive capacity of the emerging plethora of novel biomarkers.
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| 3. |
- Bozorgmanesh, Mohammad Reza, et al.
(author)
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Temporal Changes in Anthropometric Parametersand Lipid Profile according to Body Mass Indexamong an Adult Iranian Urban Population
- 2008
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In: Annals of Nutrition and Metabolism. - 0250-6807 .- 1421-9697. ; 53, s. 13-22
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Journal article (peer-reviewed)abstract
- Aims: To examine changes in anthropometric parametersand lipid profiles over a period of 3.6 years in an Iranian adultpopulation according to body mass index (BMI) groups.Methods: Between 1998 and 2001 (phase 1) and 2002 and2005 (phase 2), 5,618 nondiabetic Iranian adults aged 6 20years were examined. Analysis of covariance was used to delineatetrends in anthropometric parameters as well as totaland low- and high-density lipoprotein cholesterol (TC, LDL-Cand HDL-C, respectively) across BMI groups. Results: AlthoughBMI increased in women, this increase was not significantin obese persons. Among the men, however, a significantincrease in BMI was observed only in lean persons.Waist circumference (WC) increased across all BMI groupsin both sexes. A significant decrease was observed in TC[men: –0.83 mmol/l, 95% confidence interval (CI) –1.27 to–0.40; women: –0.78 mmol/l, CI –0.97 to –0.60] and LDL-C(men: –0.63 mmol/l, CI –1.13 to –0.13; women: –0.51 mmol/l,CI –0.78 to –0.24). A significant decrease in mean HDL-Cwas observed only among men (–0.09 mmol/l, CI –0.13to –0.04), with no difference among BMI groups (p = 0.3).There were no significant decreases in TC/HDL-C and LDLC/HDL-C ratios in men or women. Conclusions: Despite an increase in WC, favorable trends were observed in TC andLDL-C levels. The favorable trend in TC levels was counterbalancedby changes in HDL-C, as reflected by the absence of asignificant decrease in TC/HDL-C or LDL-C/HDL-C
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| 4. |
- Hadaegh, Farzad, et al.
(author)
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The metabolic syndrome and incident diabetes: Assessmentof alternative definitions of the metabolic syndrome in anIranian urban population
- 2008
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In: Diabetes Research and Clinical Practice. - 0168-8227 .- 1872-8227.
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Journal article (peer-reviewed)abstract
- Aims: To compare the ability of definitions of Metabolic Syndrome (MetS) in the predictionof type 2 diabetes.Methods: We examined 4756 subjects in an Iranian population who were non-diabetic atbaseline. After 3.6 years, 188 individuals developed diabetes.Results: Impaired glucose tolerance (IGT) and MetS definitions predicted type 2 diabetes withodds ratios ranging from 3.7 to 11.9 (all P < 0.05) although IGT had the highest area under thereceiver operator characteristic (aROC) curve than all the MetS definitions. Reduction ofglucose in the National Cholesterol Education Program (NCEP) definition of the MetSincreased diabetes prediction, but adding the family history of diabetes did not changeaROC curves. The International Diabetes Federation (IDF) definition had the highest sensitivityand false positive rate (72.2 and 35.1%, respectively) and the WHO definition had thelowest ones (54.8 and 9.2%, respectively) for predicting diabetes. The positive predictivevalues of all definitions were low (8.6–19.7%) but their negative predictive values werearound 98%.Conclusions: In Iranian population, the MetS was inferior to IGT for predicting type 2diabetes. The NCEP definition of the MetS with reduced level of glucose (not includingthe family history of diabetes) and IDF definition predicted type 2 diabetes at least as well asWHO definition.
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