a_NMR_master_v4
Dihua Xu
June 6, 2016
demographic
## Variable Summary
## 1 age 66.6 + 12.5 (22)
## 2 gender_0 16/22 (72.7 %)
## 3 gender_1 6/22 (27.3 %)
## 4 race_3 3/22 (13.6 %)
## 5 race_4 18/22 (81.8 %)
## 6 race_6 1/22 (4.5 %)
## 7 ethnicity_0 19/22 (86.4 %)
## 8 ethnicity_1 3/22 (13.6 %)
## 9 bmi 27.6 + 5.6 (22)
## 10 dialysis_vintage 889.8 + 920.5 (10)
## 11 access_0 1/21 (4.8 %)
## 12 access_1 2/21 (9.5 %)
## 13 access_2 10/21 (47.6 %)
## 14 access_3 4/21 (19 %)
## 15 access_4 2/21 (9.5 %)
## 16 access_6 2/21 (9.5 %)
## 17 cad_0 12/22 (54.5 %)
## 18 cad_1 10/22 (45.5 %)
## 19 dm_0 9/22 (40.9 %)
## 20 dm_1 13/22 (59.1 %)
## 21 chf_0 14/22 (63.6 %)
## 22 chf_1 8/22 (36.4 %)
## 23 htn_0 2/22 (9.1 %)
## 24 htn_1 20/22 (90.9 %)
## 25 pvd_0 20/22 (90.9 %)
## 26 pvd_1 2/22 (9.1 %)
## 27 Net.Fluid.Balance 1670 + 1220.9 (22)
## 28 race2_4 18/22 (81.8 %)
## 29 race2_other 4/22 (18.2 %)
## 30 access2_2 10/21 (47.6 %)
## 31 access2_other 11/21 (52.4 %)Finger measure
## [1] 0.4670751##
## Call:
## lm(formula = Finger.delta ~ Net.Fluid.Balance, data = dat1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -170.72 -75.71 -35.32 6.12 552.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -80.02290 57.62437 -1.389 0.1802
## Net.Fluid.Balance 0.06633 0.02808 2.362 0.0284 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 157.1 on 20 degrees of freedom
## Multiple R-squared: 0.2182, Adjusted R-squared: 0.1791
## F-statistic: 5.581 on 1 and 20 DF, p-value: 0.02841Leg measure
## [1] 0.0675898##
## Call:
## lm(formula = Leg.delta ~ Net.Fluid.Balance, data = dat1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -87.657 -32.929 4.419 29.696 72.468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -16.845464 16.317229 -1.032 0.314
## Net.Fluid.Balance 0.002409 0.007951 0.303 0.765
##
## Residual standard error: 44.48 on 20 degrees of freedom
## Multiple R-squared: 0.004568, Adjusted R-squared: -0.0452
## F-statistic: 0.09179 on 1 and 20 DF, p-value: 0.765Finger measurement is significantly correlated (r=0.47, p=.028) with net fluid balance; Leg measurement is not associated with net fluid balance.
Subgroup analysis
## [1] 0.5223434##
## Call:
## lm(formula = Finger.delta ~ Net.Fluid.Balance, data = subset(dat1,
## Net.Fluid.Balance > 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -176.17 -61.51 -18.06 16.76 525.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -120.37064 68.71277 -1.752 0.0968 .
## Net.Fluid.Balance 0.08301 0.03194 2.599 0.0181 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 154.5 on 18 degrees of freedom
## Multiple R-squared: 0.2728, Adjusted R-squared: 0.2324
## F-statistic: 6.754 on 1 and 18 DF, p-value: 0.01814Using Net.Fluid.Balance > 2 (I am not sure about the unit), the correlation is stronger. Note only 2 subjects’ Net.Fluid.Balance is less than or equal to two.
## [1] 0.183363##
## Call:
## lm(formula = Leg.delta ~ Net.Fluid.Balance, data = subset(dat1,
## Net.Fluid.Balance > 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -82.794 -32.869 7.386 30.190 78.010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -29.019997 20.218770 -1.435 0.168
## Net.Fluid.Balance 0.007437 0.009398 0.791 0.439
##
## Residual standard error: 45.47 on 18 degrees of freedom
## Multiple R-squared: 0.03362, Adjusted R-squared: -0.02007
## F-statistic: 0.6263 on 1 and 18 DF, p-value: 0.439Leg measurement is not associated with net fluid balance.