Barkhordari Mahnaz, Padyab Mojgan, Sardarinia Mahsa, Hadaegh Farzad, Azizi Fereidoun, Bozorgmanesh Mohammadreza
Department of Mathematics, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, IR Iran.
Centre for Population Studies, Ageing and Living Conditions, Umea University, Sweden.
Int J Endocrinol Metab. 2016 Mar 23;14(2):e32156. doi: 10.5812/ijem.32156. eCollection 2016 Apr.
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.
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.
We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 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.
The command is adpredsurv for survival models.
Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 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.
预防的一个基本要素是预测。潜在预测因子是预测模型的必要条件。然而，将新的预测因子纳入预测模型是否能直接转化为额外的预测价值仍是一个存在争议的领域。具有某个预测因子的预测模型（增强模型）和没有该预测因子的预测模型（基线模型）之间的预测能力差异通常被视为该预测因子增加的预测价值的指标。长期以来，诸如区分度和校准等指标一直用于此。最近，有人建议在比较有和没有新型生物标志物的预测模型的预测性能时使用额外预测价值。
明显缺乏能够实施新型统计程序的用户友好型统计软件。这一缺陷限制了此类新型模型评估方法的实施。我们旨在构建Stata命令，以帮助研究人员获得上述统计指标。
我们编写了Stata命令，旨在帮助研究人员获得以下内容。1. Nam-D'Agostino X拟合优度检验；2. 无切点和基于切点的净重新分类改善指数（NRI）、相对绝对综合区分改善指数（IDI）以及基于生存的回归分析。我们将这些命令应用于参与德黑兰血脂与血糖研究（TLGS）的女性的实际数据，以检验与早发性心血管疾病（CVD）家族史、腰围和空腹血糖相关的信息是否能改善弗雷明汉姆一般CVD风险算法的预测性能。
生存模型的命令是adpredsurv。
在此，我们描述了用于计算Nam-D'Agostino X拟合优度检验以及无切点和基于切点的NRI、相对和绝对IDI以及基于生存的回归分析的Stata软件包“adpredsurv”。我们希望这项工作能鼓励在检验大量新型生物标志物的预测能力时使用新方法。
