title: Survival Regression Modeling Strategies in CVD Prediction.
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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X2goodness 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 X2goodness 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.
authors.affiliation: Department of Mathematics, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, IR Iran.
authors.forename: Mahnaz
authors.initials: M
authors.lastname: Barkhordari
authors.affiliation: Centre for Population Studies, Ageing and Living Conditions, Umea University, Sweden.
authors.forename: Mojgan
authors.initials: M
authors.lastname: Padyab
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.
authors.forename: Mahsa
authors.initials: M
authors.lastname: Sardarinia
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.
authors.forename: Farzad
authors.initials: F
authors.lastname: Hadaegh
authors.affiliation: Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.
authors.forename: Fereidoun
authors.initials: F
authors.lastname: Azizi
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.
authors.forename: Mohammadreza
authors.initials: M
authors.lastname: Bozorgmanesh
keywords: Added Predictive Ability; Calibration; Integrated Discrimination Improvement; Net Reclassification Improvement; Software; Stata