Ravi_HMW_v3
Dihua Xu
May 22, 2017
Adiponectin versus Phenotype (0=Control, 1=Case)
##
## Shapiro-Wilk normality test
##
## data: dat$Adiponectin
## W = 0.97262, p-value = 0.009439##
## Welch Two Sample t-test
##
## data: Adiponectin by factor(Phenotype)
## t = 2.0083, df = 76.821, p-value = 0.04813
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 16.82847 3964.77211
## sample estimates:
## mean in group 0 mean in group 1
## 10560.685 8569.885##
## Wilcoxon rank sum test with continuity correction
##
## data: Adiponectin by factor(Phenotype)
## W = 2291.5, p-value = 0.03742
## alternative hypothesis: true location shift is not equal to 0P values from both tests are < 0.05.
##
## Call:
## lm(formula = Adiponectin ~ factor(Phenotype) + `GA in weeks`,
## data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8758.4 -4071.2 -906.5 3685.9 13487.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6271.1 3800.3 1.650 0.1014
## factor(Phenotype)1 -1849.1 979.6 -1.888 0.0614 .
## `GA in weeks` 387.7 339.8 1.141 0.2561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5191 on 128 degrees of freedom
## Multiple R-squared: 0.04119, Adjusted R-squared: 0.02621
## F-statistic: 2.749 on 2 and 128 DF, p-value: 0.06775##
## Call:
## lm(formula = Adiponectin ~ factor(Phenotype) + bmi, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10758.7 -3550.2 -530.8 3443.1 11720.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19888.0 2633.1 7.553 7.09e-12 ***
## factor(Phenotype)1 -1221.4 954.3 -1.280 0.20292
## bmi -376.6 104.2 -3.615 0.00043 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4969 on 128 degrees of freedom
## Multiple R-squared: 0.1212, Adjusted R-squared: 0.1075
## F-statistic: 8.825 on 2 and 128 DF, p-value: 0.0002567##
## Call:
## lm(formula = Adiponectin ~ factor(Phenotype) + `GA in weeks` +
## bmi, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10227.8 -3356.5 -556.6 3308.7 12537.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15900.7 4520.9 3.517 0.000606 ***
## factor(Phenotype)1 -1099.3 960.3 -1.145 0.254454
## `GA in weeks` 352.8 325.3 1.085 0.280159
## bmi -373.2 104.1 -3.584 0.000481 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4966 on 127 degrees of freedom
## Multiple R-squared: 0.1293, Adjusted R-squared: 0.1087
## F-statistic: 6.284 on 3 and 127 DF, p-value: 0.0005205After adjusting for BMI and/or Gastational age, the Phenotype effect becomes non-significant.
## Area under the curve: 0.613
## threshold specificity sensitivity
## 1.028655e+04 4.943820e-01 7.380952e-01## dat$Adiponectin < 10287
## dat$Phenotype FALSE TRUE
## 0 44 45
## 1 11 31Area under the curve (AUC) for Adiponectin is 0.613. Best thresthold is “Adiponectin < 10287” as case (Phenotype = 1). The corresponding sensitivity is 0.738 and specificity is 0.494.
PEDF versus Phenotype (0=Control, 1=Case)
##
## Shapiro-Wilk normality test
##
## data: dat.demog$`PEDF, ug/ml`
## W = 0.98492, p-value = 0.02078##
## Welch Two Sample t-test
##
## data: PEDF, ug/ml by factor(Phenotype)
## t = -2.0427, df = 95.896, p-value = 0.04383
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.62344416 -0.06621052
## sample estimates:
## mean in group 0 mean in group 1
## 20.75604 23.10087##
## Wilcoxon rank sum test with continuity correction
##
## data: PEDF, ug/ml by factor(Phenotype)
## W = 3311, p-value = 0.02662
## alternative hypothesis: true location shift is not equal to 0P values from both tests are < 0.05.
##
## Call:
## lm(formula = `PEDF, ug/ml` ~ factor(Phenotype) + `GA in weeks`,
## data = dat.demog)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.6309 -5.3676 0.1946 5.2796 28.2995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.7261 3.8163 6.741 1.44e-10 ***
## factor(Phenotype)1 2.1294 1.2796 1.664 0.0976 .
## `GA in weeks` -0.4432 0.3360 -1.319 0.1885
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.873 on 214 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.02352, Adjusted R-squared: 0.01439
## F-statistic: 2.577 on 2 and 214 DF, p-value: 0.07837##
## Call:
## lm(formula = `PEDF, ug/ml` ~ factor(Phenotype) + bmi, data = dat.demog)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.7468 -4.6919 0.3974 4.8366 27.5109
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.79801 2.34186 5.038 9.99e-07 ***
## factor(Phenotype)1 2.22450 1.23056 1.808 0.072056 .
## bmi 0.33240 0.08409 3.953 0.000105 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.631 on 214 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.08256, Adjusted R-squared: 0.07399
## F-statistic: 9.629 on 2 and 214 DF, p-value: 9.9e-05##
## Call:
## lm(formula = `PEDF, ug/ml` ~ factor(Phenotype) + `GA in weeks` +
## bmi, data = dat.demog)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.312 -5.108 0.550 4.844 27.699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.96989 4.47480 3.569 0.000443 ***
## factor(Phenotype)1 2.05328 1.23992 1.656 0.099198 .
## `GA in weeks` -0.35690 0.32626 -1.094 0.275230
## bmi 0.32610 0.08425 3.871 0.000144 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.628 on 213 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.08769, Adjusted R-squared: 0.07484
## F-statistic: 6.824 on 3 and 213 DF, p-value: 0.0002061After adjusting for BMI and/or Gastational age, the Phenotype effect becomes non-significant.
## Area under the curve: 0.6035
## threshold specificity sensitivity
## 20.3077350 0.4670659 0.7400000## dat.demog$`PEDF, ug/ml` > 20.3
## dat.demog$Phenotype FALSE TRUE
## 0 78 89
## 1 13 37Area under the curve (AUC) for PEDF is 0.6035. Best thresthold is “PEDF > 20.3” as case (Phenotype = 1). The corresponding sensitivity is 0.74 and specificity is 0.467.
combined figure
