临床试验常用统计分析方法-多因素课件

1、临床试验常用统计分析方法多因素分析HEPATOLOGY 2015; 61: 1809-1820如何选择？临床研究常用统计分析方法匹维溴铵治疗IBSClinical Gastroenterology and Hepatology. 2015;动物喂养实验 (评价不同的膳食组合)2 组兔子抓一组放在新笼子：新膳食组合剩余留在旧笼子中：标准膳食组合3个月后评价组间体重增加 的差异动物实验的提示 -1问题的引入动物喂养实验 - 3个月后结果试验组：平均体重比常规膳食组显著增加问题：新膳食组基线的平均体重，比常规组重 (有显著性差异)原因?!?动物实验的提示 -2问题的引入DES(n = 11516)B

2、MS(n =6210)p valueAge yrs64.4 12.365.3 13.00.001Female (%)32.0 32.2 0.85Diabetes Mellitus (%)28.927.40.04 Insulin Dependent Diabetes7Hyperlipidemia (%)01Hypertension (%)01Current Smoker (%)19.623.20.001Prior PCI (%)22.520.70.004Prior MI (%)26.928.70.01Prior CABG (%)13.

3、016.10.001Laura Mauri, N Engl J Med 2008; 359: 1330-42.非随机试验中的基线差异AMI患者DES vs. BMS - 马萨诸塞注册登记BMS(n=768/6210)DES(n=769/11516)12.4%6.7%MortalityP 0.0001 RevascularizationDES(n=2297/11516)BMS(n=1488/6210)P 0.000124.0%20.0%MIDES(n=1049/11516)BMS(n=754/6210)P 0.000112.1%9.1%BMS(n=647/5441)DES(n=514/5441)

4、11.9%9.4%MortalityP 0.0001 RevascularizationDES(n=1095/5441)BMS(n=1303/5441)P 0.000123.9%20.1%MIDES(n=590/5441)BMS(n=643/5441)P = 0.1111.8%10.8%Before AdjustmentAfter Adjustment预后因素结局指标混杂混杂因素临床研究基本概念假设的试验组与对照组真实的事件率假设的试验组与对照组真实的事件率年龄60岁5%15%试验组对照组10%20%模拟基线不均衡导致的结果 -1混杂的影响试验组对照组指标试验组 (N=100)对照组 (N=1

5、00)p基线：age6050501.0000主要终点：死亡9190.0396模拟基线不均衡导致的结果 -2指标试验组 (N=100)对照组 (N=100)p基线：age606733 死不掉河里 = 死活不知怎么办：尝试过河关心的问题：成功的可能性？删失生存时间生存函数12345678910开始过河！INTERVALSTARTED INTERVALDIEDCENSORED0-120221-21612-315413-410114-5815-676-77117-858-9519-10420只兔子过河的结果INTERVALSTARTED INTERVALDIEDCENSOREDP(SURV)S(t)0

6、-12022.900.9001-2161.937.8442-31541.733.6193-41011.900.5574-581.875.4875-671.00.4876-7711.857.4187-851.00.4188-9518.00.3349-1041.00.334生存函数的估计石头编号生存函数Kaplan-Meier曲线成功吃到萝卜的概率=0.334,是否值得？12345678910过河不易Cox回归Eur Heart J 2004;25:201318.PRAIS-UK研究注册登记Non-STEMI患者 653例随访4年研究与死亡相关的危险因素Cox回归HEPATOLOGY 2015;6

7、1:1809-1820Factors for Transplant-Free Survival8.7PCICABG比例风险假设N Engl J Med 2011;364:1718-27.PROCOMBAT study8,6888,763010203086420Cumulative incidence (%)ClopidogrelTicagrelor4.775.43HR 0.88 (95% CI 0.771.00), p=0.045No. at riskClopidogrelTicagrelor9,2919,3338,8758,9428,7638,827Days after

8、randomisation319015021027033086420ClopidogrelTicagrelor5.286.608,6888,7638,2868,3976,3796,480Days after randomisation*HR 0.80 (95% CI 0.700.91), p0.0018,4378,5436,9457,0284,7514,822Cumulative incidence (%)Landmark analysis: Primary efficacy endpoint (composite of CV death, MI or stroke)*Excludes pat

9、ients with any primary event during the first 30 days三种常见回归模型的比较线性回归Logistic回归Cox回归结局指标类型连续二分事件时间删失不允许不允许允许建模对象结局的均值Log (结局的odds)Log (结局的Hazard)预后变量连续x一个单位:y的均数改变x一个单位:y的OR改变x一个单位:y的HR改变二分x两水平间:y的均数改变x两水平间:y的OR改变x两水平间:y的HR改变多分类x各水平比参照:y的均数改变x各水平比参照:y的OR改变x各水平比参照:y的HR改变BMJ 2017;356:j273校正策略的选择如何评估分析结

10、果的可靠性？美国以外其他国家医学院毕业的内科医生负责诊治的患者病死率较低来自发表文章的提示 -1多因素分析时如何选择自变量？Eur J Epidemiol (2009) 24:733736常用方法占比：28% for PK & 20% for SW值得注意的数字0% vs. 35%根据既往临床经验指定变量N Engl J Med 2014;371:612-23.Forward selection algorithm(i) Fit all possible simple linear models of the response variable against each separate co

11、variate. Select the covariate with the lowest P-value and include it in the models of the subsequent steps.(ii) Fit all possible models with the covariate(s) selected in the preceding step(s) plus one other of the remaining covariates. Select the new covariate that has the lowest P-value and add it

12、to all subsequent models.(iii) Repeat step (ii) to add additional variables, one variable at a time. Continue this process until either none of the remaining covariates has a P-value less than some threshold or all of the covariates have been selected.经典统计变量选择方法回顾来自发表文章的提示 -2问题的引入BMC Medical Researc

13、h Methodology 2010, 10:8704, 06和08年发表的1882篇，仅287(15%)谈及UnivariteMultivariate*ItemOR (95%CI)POR (95%CI)pNutrition support0.46(0.25-0.82)0.0090.28(0.14-0.57)0.003Severity of disease score4.91(2.08-11.57)0.00032.65(0.99-7.00)0.051Nutritional status score1.02(0.77-1.34)0.9011.66 (1.21 -2.28)0.002Age 1.0

14、2(1.00-1.04)0.0481.02(1.00-1.04)0.038Sex 1.13(0.66-1.94)0.6481.02 (0.68-1.51)0.939Medical versus Surgical admissions 0.19(0.08-0.45)0.00020.28(0.10- 0.79)0.017Insurance2.26(0.79-6.48)0.1312.35(0.57 -9.78)0.239变量筛选存在的问题: 单因素多因素现有方法的局限性全模型样本量不足导致过度拟合临床经验设定遗漏有显著影响的变量Stepwise假阳性增加、外推性减低多因素分析的目的？评价“因果关联”

15、时，控制混杂建立预测模型必须思考的问题变量选择：共线性的影响？发生终点OR值仅SBP仅DBP全模型逐步回归SBP1.04 (1.02, 1.07)-1.07 (0.98, 1.16)1.04 (1.02, 1.07)DBP-1.03 (1.01, 1.06)0.98 (0.91, 1.05)-c统计量0.7190.6870.7280.719发生终点OR值仅SBP仅DBP全模型逐步回归SBP1.04 (1.01, 1.06)-0.97 (0.90, 1.04)-DBP-1.05 (1.02, 1.08)1.08 (1.01, 1.17)1.05 (1.02, 1.08)c统计量0.6600.68

16、60.7020.686模拟100例患者的血压与终点事件发生的数据识别共线性的方法相关分析容差 (tolerance), VIF, 条件指数判定标准 ?消除共线性的方法逐步回归 ?对存在共线性的指标进行敏感性分析扩大样本，减小标准误，减弱共线性影响共线性问题 (Colinearity)外投手稿的审稿问题回归分析潜在的问题VariableUnivariateP valueMultivariteP valueOR Value95% CIOR Value95% CIAge1.071.04-1.110.00011.061.03-1.100.0001Family history of CHD2.101.2

17、0-3.680.0101.630.86-3.090.133Diabetes2.831.48-5.420.0022.231.10-4.520.027Hypertension2.151.20-3.850.0101.300.66-2.600.449Dyslipidemia1.931.07-3.480.0301.110.55-2.240.762RDW1.971.28-3.030.0022.001.24-3.220.004hs-CRP1.191.05-1.340.0051.141.01-1.290.029Table X Logistic regression analysis of independen

18、t predictor for CHD* Cohort study, consecutive recruit 233 patients.外投手稿的审稿问题回归分析潜在的问题Their multivariate statistical models are performed within only 233 patients, therefore a power analysis could be helpful in view of over fitting problems (RDW).回答审稿人的问题把握度分析 (事后)Over fitting 指的是什么?外投手稿的审稿问题回归分析潜在的

19、问题VariableCHD group(N=147)Control(N=86)P valueAge (years)63.08.7001BMI (kg/m2)25.43.618Smoking history11 (7.5)7 (8.1)1.000Diabetes55 (37.4)15 (17.4)0.002Hypertension114 (77.6)53 (61.6)0.013Dyslipidemia115 (78.2)56 (65.1)0.031Family history of CHD70 (47.6)26 (30.2)0.011Hemoglobin

20、(g/L)129.311.221RDW (%)12.90.701hs-CRP1.94 (0.85,3.35)1.58 (0.69,2.76)0.139外投手稿的审稿问题回归分析潜在的问题VariableUnivariateP valueMultivariteP valueOR Value95% CIOR Value95% CIAge1.071.04-1.110.00011.061.03-1.100.0001Family history of CHD2.101.20-3.680.0101.630.86-3.090.133Diabetes2.831.48-

21、5.420.0022.231.10-4.520.027Hypertension2.151.20-3.850.0101.300.66-2.600.449Dyslipidemia1.931.07-3.480.0301.110.55-2.240.762RDW1.971.28-3.030.0022.001.24-3.220.004hs-CRP1.191.05-1.340.0051.141.01-1.290.029OR=exp(beta), beta=log(OR), ORadjusted=exp(beta/10)ORadjusted=1.07, 95%CI: 1.02-1.12 生活质量评分1 = 非

22、常差, 3 = 一般, 5 = 感觉很好取平均得分意味着1和2分之间的差别与4和5分之间一致(1+5)/2 = (2+4)/2如果1 = 死亡将如何？多分类指标的处理回归分析中存在的问题地区赋值东部1中部2西部3地区赋值哑变量1哑变量2东部110中部201西部300通常，哑变量的数量等于原始变量的分类数减 1多分类自变量的哑变量处理回归分析中存在的问题具体研究举例哑变量的作用及意义评价某实验室指标与终点事件发生率的关系按四分位数分组分组变量对应的取值1-4Logistic回归不同策略的比较直接采用分组变量哑变量处理是否采用哑变量的区别多分类指标的问题变量p值OR值95%置信区间Group0.1

23、6091.1160.957, 1.301变量p值OR值95%置信区间Group 1 vs 40.39400.7850.498, 1.238Group 2 vs 40.01430.4590.277, 0.758Group 3 vs 40.57990.6340.396, 1.016直接采用分组变量哑变量处理指标Quintile1(N=200)Quintile2(N=200)Quintile3(N=200)Quintile4(N=200)事件数 (%)45 (22.5%)29 (14.5%)38 (19.0%)54 (27.0%)实验室指标按四分位处理回归分析潜在的问题缺失比例 5%, 10% or

24、 20%?缺失缺失缺失缺失缺失Missing缺失数据的影响CoCr (%)PtCr(%)Relative Risk (95% CI)Relative Risk (95% CI)P InteractionAll randomized (n=1507)7.1%5.9%0.84 0.56, 1.26Age 65 yrs (n=779)8.0%5.3%0.67 0.38, 1.160.29Age 65 yrs (n=728)6.8%6.9%1.02 0.58, 1.79Male (n=1074)6.3%6.3%1.00 0.62, 1.610.17Female (n=433)10.4%5.8%0.56

25、 0.28, 1.11Diabetic (n=351)12.7%8.0%0.63 0.32, 1.240.30Nondiabetic (n=1156)5.8%5.6%0.97 0.60, 1.58Single vessel Tx (n=1399)7.7%5.9%0.77 0.51, 1.160.25Dual vessel Tx (n=108)4.3%8.8%2.02 0.41, 9.92BMI 2.62 mm (n=728)6.2%4.9%0.80 0.43, 1.50Lesion 13.0 mm (n=870)7.2%6.0%0.84 0.50, 1.410.90Lesion 13.0 mm (n=636)7.9%6.3%0.80 0.44, 1.44PLATINUM Subgroup AnalysesTLF at 3 YearsPtCr betterCoCr betterBinary RatesGood OutcomeInt