危险度分析和 Logistic 回归

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危险度分析和 Logistic 回归. 第十七章. 上海第二医科大学 生物统计教研室. 危险度分析和 LOGISTIC 回归主要用于研究影响肿瘤和其它各种疾病的发病因素或预后因素。 一般的相对危险度计算通常用于单因素分析。 LOGISTIC 回归可用于多因素分析。. 第十七章危险度分析和 Logistic 回归. 第一节 发病危险度比较的统计指标. 病因分析(或预后分析)的目的:找出影响疾病发生(或预后好坏)的原因及其影响的强度。 如果某因素对疾病发生有影响,就称该因素与疾病发病有联系,而联系的强度则反映该因素对疾病发生影响的大小。 - PowerPoint PPT Presentation

Transcript of 危险度分析和 Logistic 回归

  • Logistic

  • LOGISTIC

    LOGISTIC Logistic

  • ,,

  • RISK , ,

  • (relative risk)RR,P1P0,: RR>1,,;RR
  • 17.1 ,, ,,:

    (%)

    12 351 363 P1=3.3 1 817 818 P0=0.12 13 1168 1181

  • RR= =27.627.6

  • (cohort study), ,,,,, ,

  • : a b c d

  • =a/(a+b)

    =c/(c+d)

    =[a/(a+b)]/[c/(c+d)]

    :

    a

    b

    a+b

    c

    d

    c+d

    a+c

    b+d

    n=a+b+c+d

  • RR H0:1, RR=1 H1:1,RR1 2MH=[(ad-bc)2(n-1)]/[(a+b)(c+d)(a+c)(b+d)] 22n/(n-1) 2MH =[(n-1)/n]2 2=(ad-bc)2n/[(a+b)(c+d)(a+c)(b+d)] n,,,2Yates2

  • 95%,99%

    AR95%

    _758896506.unknown

    _758896569.unknown

    _758896664.unknown

    _758896465.unknown

  • 21.2 ,609,,17.1 27(a) 95(b) 122 44(c) 443(d) 487 71 538 609

  • ,,2.45

    :

    =(27/122)/(44/487)=2.45

    H0: RR=1

    H1:RR1

    =[(27443-4495)2(609-1)]/(12248771538)=16.22

    =16.22>20.01 , P

  • (Case-Control Study),,,,, ,,,,

  • a

    b

    c

    d

    a

    b

    c

    d

    ,t

  • (Grouped Case-Control Study)(odds),(odds ratio)

  • (odds), Odds=P/1-P=[a/(a+b)]/[b/(a+b)]=a/b=[c/(c+d)]/[d/(c+d)]=c/d

    OR(odds ratio) OR=[a/b]/[c/d]=ad/bc (P320)

  • 21.3 ,,183()183(),:17.2 () 55 128 183 () 19 164 183 74 292 366

  • (17.19), =(55164)/(19128)=3.71H0:=1H1:1 =[(55164-19128)2(366-1)]/(74292183183)=21.89 >20.01=6.635,P
  • , ,,, ,,,,2(,)

  • 17.3 300 56 294 21 6 35 700 944 606 79 94 865

    =(300944)/(56700)=7.22

    =(29479)/(60621)=1.83

    =(6865)/(9435)=1.58

    =7.22, ,

    _758908104.unknown

  • ,,1.83,1.58 (Stratified Analysis)(,,),,

  • K22:P32317.4512 3 41

  • ,,,,, ,,(Paired Case-Contral Study)

  • , , ,,,,,,

  • Logistic ,y Logisticy,,, 10SASlogistic12Y=1 Y=2 Logistic

  • Yresponse variable)X: (covariate),(explanatory) , X, logisticPY1,P=P(Y=1)Q=1-P,Q=P(Y=2) PXi Logistic

  • P=[exp(b0+b1x1++bmxm)]/[1+exp(b0+b1x1++bmxm)] Q=1-P=1/[1+exp(b0+b1x1++bmxm)] P/(1-P)=exp(b0+b1x1++bmxm)y=ln[P/(1-P)]logit,, y=ln[p/(1-p)]= b0+b1x1++bmxm Logistic

  • SASLogisticLogistic Logistic

    Logistic

  • 1. P2.

    Logistic

  • 3. Xibi Xiexp(bi)(1) Xi1=0= exp(bi)Logistic

  • 2Xi0=1=2=3= exp(bi) exp(3bi)3Xi exp(bi)6035exp(25bi)Logistic

  • 4. bi 5. XLXKXLK= XL*XK bLKXLXK6. LogisticLogistic

  • 17 6 23 5 87 92 22 93 115

  • Sensitivity= 17/23=73.9%specificity= 87/92=94.6%correct= / 17+87/ 115=90.4%false positive rate= 5/22=22.7%false negative rate= 6/93=6.5%

  • LOGISTIC P/1PEXP-17.96+3.63X5+1.60X6+1.91X7+2.57X8 X

  • 12X1A, =1, =0YY3=1=0 3 SAS6.12 t LOGISTIC3YYP0.05

  • 1 RCPP 2LOGISTIC

  • P

    -2.8770 2.0954 0.1697 . X3C 2.2844 1.0210 0.0253 0.519452 X5 1.0102 0.3147 0.0013 0.931773 X6 1.6321 0.5449 0.0027 0.837360 X8 -0.5764 0.1874 0.0021 -0.917033 X9 0.000386 0.000186 0.0384 0.392918

  • 3 e2.2844=9.821 e1.0102=2.751 e1.6321=5.111g/L e0.5764=1.781000/mm3e0.386 =1.47

  • 33

  • LOGISTICy=ln(P/(1-P))= -2.8770+2.2844 +1.0102 +1.6321 -0.5764 g/L +0.000386 /mm3

  • LOGISTIC98P0.5P0.5 47 7 54 9 35 44 56 42 98

  • Sensitivity= 47/54=87.0%specificity= 35/44=79.5%correct= / 47+35/ 98=83.7%false positive rate= 9/56=16.1%false negative rate= 7/42=16.7%