不同分析策略下应用二分类Logistic回归进行疾病风险评估的结果差异性分析  

Difference analysis of results of disease risk assessment by using binary Logistic regression under different analysis strategies

作　　者：冯磊[1] 年士艳[1] 赵阳 罗风 徐文波[1] 叶丹[1] 张兴锋[1] 

机构地区：[1]云南省玉溪市人民医院检验科,653100

出　　处：《中华检验医学杂志》2018年第3期232-236,共5页Chinese Journal of Laboratory Medicine

基　　金：国家自然科学基金（81460326）

摘　　要：目的探讨条件控制对二分类Logistic回归结果影响的重要性。方法通过病例对照研究方法，选择2010年10月至2013年3月云南省玉溪市人民医院心内科确诊的664例男性冠心病（CHD）患者及400名健康男性对照14项生理生化指标[包括：年龄、UA、TC、TG、HDL－C、LDL－C、APOA1、APOB100、Lp（a）、HCY、TBIL、DBIL、IBIL、γ－GT]，比较不同统计学分析策略下，运用二分类Logistic回归分析建立各生理生化指标与CHD相关关系及数学模型结果的差异，对该模型进行ROC曲线分析。结果（1）在未进行条件控制时，将全部14项生理生化指标直接进行二分类Logistic回归分析，经过11步回归之后，共有11个指标[年龄、UA、TG、HDL－C、LDL－C、APOA1、APOB100、Lp（a）、HCY、DBIL、γ－GT]入选，因Logistic回归本身的自我修正能力，能得到＂较满意＂结果，但部分结果难以解释。（2）按照Logistic回归应用条件，先对指标进行正态性检验、差异分析、相关分析等处理后，进行运用条件分析和控制，在考虑了各项指标的分布特性，排除了各变量间的内部混杂因素，筛选出9项独立性较强的变量[年龄、UA、TC、lnTG、lnLp（a）、lnHCY、HDL－C、LDL－C和lnTBIL]进行Logistic回归分析，经过7步回归后，最终有7个指标[年龄、UA、HDL－C、lnTG、lnLp（a）、lnHCY、lnTBIL]入选，曲线下面积为0.927。结论运用二分类Logistic回归分析进行复杂疾病危险因素确认及风险评估模型建立研究时，进行严格的条件控制，可提高分析结果的可靠性和有效性。（中华检验医学杂志，2018, 41：232－236）ObjectiveTo investigate the importance of conditional control on the results of binary Logistic regression analysis.Methods664 male patients diagnosed with CHD and 400 healthy controls who visited the Department of Cardiology at People’s Hospital of Yuxi City in Yunnan province from October 2010 to March 2013were enrolled consecutively in this case-control study, and 14 physiological and biochemical indexes [including： age, UA, TC, TG, HDL-C, LDL-C, APOA1, APOB100, Lp（a）, HCY, TBIL, DBIL, IBIL, γ-GT] were collected. The correlations and differences in mathematical model results between physiological, biochemical indexes and CHD were compared by binary Logistic regression analysis under different statistical analysis strategies and then the model was analyzed by the ROC curve.Results（1） With no conditional control, all of the 14 physiological and biochemical indexes were directly inputted in the binary Logistic regression analysis, and after 11 steps regression analysis, 11 indexes [age, UA, TG, HDL-C, LDL-C, APOA1, APOB100, Lp（a）, HCY, DBIL, γ-GT] were retained. Because of the self-correcting ability of the binary Logistic regression analysis, relatively satisfactory results can be obtained. However, someof the resultsarehard to explain. （2） According to the application conditions of Logistic regression analysis, after performing the normality test, difference analysis and correlation analysis of these indexes, using condition analysis and control, considering the distribution characteristics of the indexes, excluding internal confounding factors among variables, 9 more independent indexes [age, UA, TC, lnTG, lnLp（a）, lnHCY, HDL-C, LDL-C and TBIL] were selected for Logistic regression analysis. After 7 steps regression analysis, 7 indexes [age, UA, HDL-C, lnTG, lnLp（a）, lnHCY and lnTBIL] were finally selected, and the area under the curve is 0.927.ConclusionWhen the binary Logistic regression analysis was used to confirm risk factors and establish risk assessment models for complex d

关 键 词：冠心病 危险因素 LOGISTIC模型 危险性评估 

分 类 号：R541.4[医药卫生—心血管疾病]