hist(master$LOS)
hist(log(master$LOS))
# hist(
# master$LOS[master$group == "MGH"],
# main = "Histogram of LOS (Group A)",
# xlab = "Length of Stay",
# col = "lightblue",
# breaks = 30
# )
#
# # 3. Histogram for group B
# hist(
# master$LOS[master$group == "BWH"],
# main = "Histogram of LOS (Group B)",
# xlab = "Length of Stay",
# col = "lightgreen",
# breaks = 30
# )
Table 1
all_var <- c("Gender","Age","Race_Group","LOS",
"HTN","DM","CAD","Hgb","HCT_admit","HCT_discharge","Iron",
"Ferritin","NT_proBNP",
"UF_rate",
"EDW","admit_Weight","HF_score","Dialysis_Vintage","Access_type",
"n_90","n_180")
cat_var <- c("Gender","Race_Group","HTN","DM","CAD")
num_var <- setdiff(all_var,cat_var)
master <- master %>% mutate_at(cat_var,as.factor)
non_num_var <- setdiff(num_var,"Age")
T1_before <- CreateTableOne(vars = all_var,strata ="group" ,includeNA = F,addOverall = TRUE,data = master, factorVars = cat_var)
###show SMD
print(T1_before,
nonnormal=non_num_var,showAllLevels = T,missing = T,quote = FALSE, noSpaces = TRUE, printToggle = FALSE,
contDigits = 3,
catDigits = 2,
pDigits = 4)
table1(~Gender+Age+Race_Group+LOS+HTN+DM+CAD+Hgb+HCT_admit+HCT_discharge+Iron+Ferritin+NT_proBNP+UF_rate+EDW+admit_Weight+HF_score+Dialysis_Vintage+Access_type+n_90+n_180|treated,
render.continuous = c(.="Mean (SD)", .="Median [Q1,Q3]"),data=master)
|
0
(N=66) |
1
(N=34) |
Overall
(N=100) |
| Gender |
|
|
|
| Female |
29 (43.9%) |
13 (38.2%) |
42 (42.0%) |
| Male |
37 (56.1%) |
21 (61.8%) |
58 (58.0%) |
| Age |
|
|
|
| Mean (SD) |
68.7 (14.5) |
72.1 (12.4) |
69.8 (13.9) |
| Median [Q1,Q3] |
71.0 [58.3,81.8] |
74.0 [64.5,79.8] |
72.5 [59.0,81.3] |
| Race_Group |
|
|
|
| White |
31 (47.0%) |
20 (58.8%) |
51 (51.0%) |
| Asian |
1 (1.5%) |
6 (17.6%) |
7 (7.0%) |
| Black |
20 (30.3%) |
6 (17.6%) |
26 (26.0%) |
| Other |
14 (21.2%) |
2 (5.9%) |
16 (16.0%) |
| LOS |
|
|
|
| Mean (SD) |
11.2 (10.2) |
10.9 (5.83) |
11.1 (8.92) |
| Median [Q1,Q3] |
8.00 [4.00,13.8] |
9.50 [7.00,14.5] |
9.00 [5.00,14.3] |
| HTN |
|
|
|
| N |
2 (3.0%) |
1 (2.9%) |
3 (3.0%) |
| Y |
64 (97.0%) |
33 (97.1%) |
97 (97.0%) |
| DM |
|
|
|
| N |
13 (19.7%) |
6 (17.6%) |
19 (19.0%) |
| Y |
53 (80.3%) |
28 (82.4%) |
81 (81.0%) |
| CAD |
|
|
|
| N |
2 (3.0%) |
4 (11.8%) |
6 (6.0%) |
| Y |
64 (97.0%) |
30 (88.2%) |
94 (94.0%) |
| Hgb |
|
|
|
| Mean (SD) |
10.1 (1.72) |
10.1 (1.34) |
10.1 (1.59) |
| Median [Q1,Q3] |
9.80 [8.88,11.1] |
10.2 [9.23,10.9] |
9.85 [9.10,11.0] |
| HCT_admit |
|
|
|
| Mean (SD) |
31.5 (5.41) |
31.9 (4.31) |
31.6 (5.05) |
| Median [Q1,Q3] |
30.9 [27.6,35.5] |
32.2 [29.2,34.6] |
31.2 [28.1,35.1] |
| HCT_discharge |
|
|
|
| Mean (SD) |
30.1 (4.67) |
29.8 (4.16) |
30.0 (4.49) |
| Median [Q1,Q3] |
29.9 [26.5,33.5] |
29.8 [26.9,32.1] |
29.9 [26.8,32.8] |
| Iron |
|
|
|
| Mean (SD) |
56.1 (29.5) |
46.1 (22.7) |
52.5 (27.6) |
| Median [Q1,Q3] |
48.0 [34.0,74.0] |
44.0 [31.0,54.0] |
46.0 [33.0,70.0] |
| Missing |
7 (10.6%) |
1 (2.9%) |
8 (8.0%) |
| Ferritin |
|
|
|
| Mean (SD) |
1150 (1570) |
1100 (951) |
1130 (1370) |
| Median [Q1,Q3] |
768 [277,1490] |
852 [458,1490] |
834 [291,1490] |
| Missing |
8 (12.1%) |
1 (2.9%) |
9 (9.0%) |
| NT_proBNP |
|
|
|
| Mean (SD) |
46100 (52600) |
26700 (19700) |
38800 (44100) |
| Median [Q1,Q3] |
24600 [8020,70000] |
25900 [8850,38100] |
25400 [7900,48800] |
| Missing |
21 (31.8%) |
7 (20.6%) |
28 (28.0%) |
| UF_rate |
|
|
|
| Mean (SD) |
39.3 (245) |
9.20 (4.73) |
29.0 (199) |
| Median [Q1,Q3] |
8.74 [7.10,11.6] |
8.66 [6.35,10.6] |
8.72 [6.44,10.9] |
| EDW |
|
|
|
| Mean (SD) |
77.7 (23.4) |
73.0 (26.5) |
76.1 (24.5) |
| Median [Q1,Q3] |
74.2 [63.0,86.9] |
67.7 [56.5,80.0] |
72.0 [60.0,85.6] |
| admit_Weight |
|
|
|
| Mean (SD) |
80.7 (24.4) |
76.1 (26.6) |
79.1 (25.1) |
| Median [Q1,Q3] |
76.1 [65.1,88.6] |
71.0 [59.9,82.3] |
74.9 [62.9,87.4] |
| HF_score |
|
|
|
| 1 |
19 (28.8%) |
10 (29.4%) |
29 (29.0%) |
| 2 |
11 (16.7%) |
8 (23.5%) |
19 (19.0%) |
| 3 |
36 (54.5%) |
16 (47.1%) |
52 (52.0%) |
| Dialysis_Vintage |
|
|
|
| Mean (SD) |
2.23 (2.06) |
2.26 (2.02) |
2.24 (2.04) |
| Median [Q1,Q3] |
1.00 [1.00,3.00] |
1.00 [1.00,3.00] |
1.00 [1.00,3.00] |
| Access_type |
|
|
|
| AVF |
25 (37.9%) |
15 (44.1%) |
40 (40.0%) |
| TDC |
41 (62.1%) |
15 (44.1%) |
56 (56.0%) |
| AVG |
0 (0%) |
4 (11.8%) |
4 (4.0%) |
| n_90 |
|
|
|
| Mean (SD) |
1.18 (0.975) |
0.882 (1.01) |
1.08 (0.992) |
| Median [Q1,Q3] |
1.00 [0,2.00] |
1.00 [0,1.75] |
1.00 [0,2.00] |
| n_180 |
|
|
|
| Mean (SD) |
1.71 (1.43) |
1.41 (1.28) |
1.61 (1.38) |
| Median [Q1,Q3] |
1.50 [1.00,3.00] |
1.00 [0,2.75] |
1.00 [0,3.00] |
Balance check before
library("cobalt")
bal.tab(master %>% select("Age","Race_Group" ,"Gender","CAD","HTN","DM","HF_score","Dialysis_Vintage"), treat = master$treated,disp=c("means","sds"))
## Balance Measures
## Type M.0.Un SD.0.Un M.1.Un SD.1.Un Diff.Un
## Age Contin. 68.6667 14.5119 72.0588 12.3973 0.2513
## Race_Group_White Binary 0.4697 . 0.5882 . 0.1185
## Race_Group_Asian Binary 0.0152 . 0.1765 . 0.1613
## Race_Group_Black Binary 0.3030 . 0.1765 . -0.1266
## Race_Group_Other Binary 0.2121 . 0.0588 . -0.1533
## Gender_Male Binary 0.5606 . 0.6176 . 0.0570
## CAD_Y Binary 0.9697 . 0.8824 . -0.0873
## HTN_Y Binary 0.9697 . 0.9706 . 0.0009
## DM_Y Binary 0.8030 . 0.8235 . 0.0205
## HF_score_1 Binary 0.2879 . 0.2941 . 0.0062
## HF_score_2 Binary 0.1667 . 0.2353 . 0.0686
## HF_score_3 Binary 0.5455 . 0.4706 . -0.0749
## Dialysis_Vintage Contin. 2.2273 2.0591 2.2647 2.0197 0.0184
##
## Sample sizes
## Control Treated
## All 66 34
Balance check after
bal.tab(master %>% select("Age","Race_Group" ,"Gender","CAD","HTN","DM","HF_score","Dialysis_Vintage"), treat = master$treated, weights = master$weight.ATE,disp=c("means","sds"))
## Balance Measures
## Type M.0.Adj SD.0.Adj M.1.Adj SD.1.Adj Diff.Adj
## Age Contin. 69.8706 13.9749 71.1316 14.3433 0.0934
## Race_Group_White Binary 0.5262 . 0.5053 . -0.0209
## Race_Group_Asian Binary 0.0309 . 0.0986 . 0.0677
## Race_Group_Black Binary 0.2739 . 0.2919 . 0.0180
## Race_Group_Other Binary 0.1690 . 0.1041 . -0.0648
## Gender_Male Binary 0.5775 . 0.5568 . -0.0207
## CAD_Y Binary 0.9705 . 0.9119 . -0.0585
## HTN_Y Binary 0.9735 . 0.9840 . 0.0105
## DM_Y Binary 0.8006 . 0.8505 . 0.0500
## HF_score_1 Binary 0.2862 . 0.2621 . -0.0241
## HF_score_2 Binary 0.1898 . 0.2271 . 0.0373
## HF_score_3 Binary 0.5239 . 0.5107 . -0.0132
## Dialysis_Vintage Contin. 2.1915 1.9458 2.3499 2.0210 0.0777
##
## Effective sample sizes
## Control Treated
## Unadjusted 66. 34.
## Adjusted 61.87 26.31
Univariate linear regression outcome los
outcome los
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
186 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
-1.6 |
-5.4, 2.2 |
0.413 |
| Age |
186 |
-0.06 |
-0.20, 0.07 |
0.381 |
| Gender |
186 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
-0.69 |
-4.5, 3.1 |
0.724 |
| Race_Group |
186 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
-6.0 |
-14, 1.7 |
0.132 |
| Black |
|
-4.0 |
-8.3, 0.31 |
0.072 |
| Other |
|
-6.7 |
-12, -1.1 |
0.020 |
| HTN |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
2.0 |
-11, 15 |
0.762 |
| DM |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
3.4 |
-1.6, 8.3 |
0.183 |
| CAD |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
-1.8 |
-9.9, 6.3 |
0.664 |
| Dialysis_Vintage |
186 |
-0.50 |
-1.5, 0.47 |
0.313 |
| HF_score |
186 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
4.7 |
-0.75, 10 |
0.095 |
| 3 |
|
-0.08 |
-4.5, 4.3 |
0.973 |
| weight.ATE |
186 |
-0.62 |
-1.8, 0.55 |
0.300 |
Multivariate linear regression los: age gender race
outcome los
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
-1.5 |
-5.3, 2.2 |
0.416 |
| Age |
186 |
-0.12 |
-0.26, 0.02 |
0.099 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
-1.8 |
-5.7, 2.0 |
0.348 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
-5.1 |
-13, 2.6 |
0.199 |
| Black |
52 |
-5.6 |
-10, -0.95 |
0.020 |
| Other |
25 |
-8.0 |
-14, -2.3 |
0.007 |
Multivariate linear regression los ： group+
Age+Gender+Race_Group+HTN+DM+CAD+Dialysis_Vintage+HF_score
outcome los
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
-2.0 |
-5.8, 1.7 |
0.283 |
| Age |
186 |
-0.12 |
-0.26, 0.02 |
0.103 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
-3.3 |
-7.3, 0.82 |
0.121 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
-3.5 |
-11, 4.4 |
0.383 |
| Black |
52 |
-5.3 |
-10, -0.45 |
0.035 |
| Other |
25 |
-9.2 |
-15, -3.4 |
0.003 |
| HTN |
|
|
|
|
| N |
4 |
— |
— |
|
| Y |
182 |
-1.5 |
-16, 13 |
0.841 |
| DM |
|
|
|
|
| N |
32 |
— |
— |
|
| Y |
154 |
4.3 |
-0.89, 9.5 |
0.108 |
| CAD |
|
|
|
|
| N |
10 |
— |
— |
|
| Y |
176 |
1.1 |
-7.9, 10 |
0.810 |
| Dialysis_Vintage |
186 |
-0.61 |
-1.6, 0.39 |
0.234 |
| HF_score |
|
|
|
|
| 1 |
51 |
— |
— |
|
| 2 |
38 |
6.4 |
0.90, 12 |
0.025 |
| 3 |
96 |
-0.01 |
-4.6, 4.6 |
0.998 |
90 days poisson regression group
glm(n_90 ~ group,
family = poisson(link = "log"),
weights = weight.ATE,
data = master) %>% tbl_regression(exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
add_n(location = "level")
| Characteristic |
N |
IRR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
0.71 |
0.52, 0.95 |
0.025 |
90 days poisson regression group Age Gender Race_Group
glm(n_90 ~ group+ Age+Gender+Race_Group,
family = poisson(link = "log"),
weights = weight.ATE,
data = master) %>% tbl_regression(exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
add_n(location = "level")
| Characteristic |
N |
IRR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
0.70 |
0.51, 0.95 |
0.022 |
| Age |
186 |
1.00 |
0.99, 1.02 |
0.538 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
0.92 |
0.68, 1.25 |
0.601 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
1.48 |
0.82, 2.50 |
0.169 |
| Black |
52 |
0.82 |
0.55, 1.21 |
0.331 |
| Other |
25 |
1.31 |
0.86, 1.97 |
0.196 |
90 days poisson regression： group+
Age+Gender+Race_Group+HTN+DM+CAD+Dialysis_Vintage+HF_score
glm(n_90 ~ group+ Age+Gender+Race_Group+HTN+DM+CAD+Dialysis_Vintage+HF_score,
family = poisson(link = "log"),
weights = weight.ATE,
data = master) %>% tbl_regression(exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
add_n(location = "level")
| Characteristic |
N |
IRR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
0.68 |
0.49, 0.93 |
0.017 |
| Age |
186 |
1.00 |
0.99, 1.01 |
0.769 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
0.71 |
0.50, 0.99 |
0.045 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
1.91 |
1.02, 3.38 |
0.034 |
| Black |
52 |
0.96 |
0.63, 1.45 |
0.860 |
| Other |
25 |
1.59 |
1.01, 2.44 |
0.039 |
| HTN |
|
|
|
|
| N |
4 |
— |
— |
|
| Y |
182 |
0.96 |
0.26, 5.27 |
0.959 |
| DM |
|
|
|
|
| N |
32 |
— |
— |
|
| Y |
154 |
1.87 |
1.19, 3.07 |
0.009 |
| CAD |
|
|
|
|
| N |
10 |
— |
— |
|
| Y |
176 |
1.12 |
0.51, 2.89 |
0.791 |
| Dialysis_Vintage |
186 |
0.86 |
0.77, 0.95 |
0.005 |
| HF_score |
|
|
|
|
| 1 |
51 |
— |
— |
|
| 2 |
38 |
1.24 |
0.76, 2.01 |
0.382 |
| 3 |
96 |
1.59 |
1.08, 2.40 |
0.022 |
180 days poisson regression group
glm(n_180 ~ group,
family = poisson(link = "log"),
weights = weight.ATE,
data = master) %>% tbl_regression(exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
add_n(location = "level")
| Characteristic |
N |
IRR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
0.90 |
0.71, 1.13 |
0.355 |
180 days poisson regression： Age Gender Race_Group
glm(n_180 ~ group+ Age+Gender+Race_Group,
family = poisson(link = "log"),
weights = weight.ATE,
data = master) %>% tbl_regression(exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
add_n(location = "level")
| Characteristic |
N |
IRR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
0.92 |
0.72, 1.16 |
0.466 |
| Age |
186 |
1.00 |
0.99, 1.01 |
0.868 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
0.97 |
0.76, 1.24 |
0.817 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
1.16 |
0.69, 1.86 |
0.550 |
| Black |
52 |
1.12 |
0.82, 1.51 |
0.478 |
| Other |
25 |
1.62 |
1.16, 2.22 |
0.004 |
180 days poisson regression： group+
Age+Gender+Race_Group+HTN+DM+CAD+Dialysis_Vintage+HF_score
glm(n_180 ~ group+ Age+Gender+Race_Group+HTN+DM+CAD+Dialysis_Vintage+HF_score,
family = poisson(link = "log"),
weights = weight.ATE,
data = master) %>% tbl_regression(exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
bold_labels()%>%
add_n(location = "level")
| Characteristic |
N |
IRR |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
0.93 |
0.73, 1.19 |
0.585 |
| Age |
186 |
1.00 |
0.99, 1.01 |
0.738 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
0.80 |
0.61, 1.06 |
0.126 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
1.41 |
0.84, 2.35 |
0.195 |
| Black |
52 |
1.21 |
0.89, 1.65 |
0.232 |
| Other |
25 |
1.66 |
1.18, 2.33 |
0.004 |
| HTN |
|
|
|
|
| N |
4 |
— |
— |
|
| Y |
182 |
6,273,484 |
0.00, Inf |
0.988 |
| DM |
|
|
|
|
| N |
32 |
— |
— |
|
| Y |
154 |
1.45 |
1.01, 2.08 |
0.045 |
| CAD |
|
|
|
|
| N |
10 |
— |
— |
|
| Y |
176 |
2.63 |
0.98, 7.05 |
0.054 |
| Dialysis_Vintage |
186 |
0.86 |
0.79, 0.93 |
<0.001 |
| HF_score |
|
|
|
|
| 1 |
51 |
— |
— |
|
| 2 |
38 |
0.99 |
0.68, 1.43 |
0.944 |
| 3 |
96 |
1.15 |
0.85, 1.55 |
0.371 |
Univariate coxph time_1st_readmission
| Characteristic |
N |
HR |
95% CI |
p-value |
| group |
186 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
0.75 |
0.45, 1.27 |
0.291 |
| Age |
186 |
1.01 |
0.99, 1.03 |
0.507 |
| Gender |
186 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
0.83 |
0.50, 1.40 |
0.490 |
| Race_Group |
186 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
1.21 |
0.41, 3.56 |
0.731 |
| Black |
|
0.96 |
0.46, 2.02 |
0.922 |
| Other |
|
1.69 |
0.91, 3.14 |
0.094 |
| HTN |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
3.62 |
0.33, 39.2 |
0.290 |
| DM |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.28 |
0.62, 2.64 |
0.499 |
| CAD |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.83 |
0.72, 4.66 |
0.205 |
| Dialysis_Vintage |
186 |
0.84 |
0.68, 1.05 |
0.121 |
| HF_score |
186 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
1.77 |
1.00, 3.14 |
0.051 |
| 3 |
|
1.39 |
0.74, 2.59 |
0.307 |
| weight.ATE |
186 |
0.91 |
0.67, 1.25 |
0.562 |
coxph time_1st_readmission_180 Multivariate linear regression los:
age gender race
| Characteristic |
HR |
95% CI |
p-value |
| group |
|
|
|
| BWH |
— |
— |
|
| MGH |
0.74 |
0.43, 1.27 |
0.271 |
| Age |
1.01 |
0.99, 1.03 |
0.462 |
| Gender |
|
|
|
| Female |
— |
— |
|
| Male |
0.86 |
0.52, 1.42 |
0.555 |
| Race_Group |
|
|
|
| White |
— |
— |
|
| Asian |
1.43 |
0.44, 4.60 |
0.553 |
| Black |
1.08 |
0.52, 2.23 |
0.844 |
| Other |
1.76 |
0.92, 3.38 |
0.089 |
coxph time_1st_readmission_180 Multivariate linear regression:group+
Age+Gender+Race_Group+HTN+DM+CAD+Dialysis_Vintage+HF_score
| Characteristic |
HR |
95% CI |
p-value |
| group |
|
|
|
| BWH |
— |
— |
|
| MGH |
0.75 |
0.45, 1.23 |
0.253 |
| Age |
1.01 |
0.99, 1.03 |
0.302 |
| Gender |
|
|
|
| Female |
— |
— |
|
| Male |
0.58 |
0.35, 0.98 |
0.041 |
| Race_Group |
|
|
|
| White |
— |
— |
|
| Asian |
2.19 |
0.66, 7.32 |
0.201 |
| Black |
1.79 |
1.06, 3.05 |
0.031 |
| Other |
2.18 |
1.12, 4.23 |
0.021 |
| HTN |
|
|
|
| N |
— |
— |
|
| Y |
3.48 |
0.13, 92.2 |
0.455 |
| DM |
|
|
|
| N |
— |
— |
|
| Y |
2.01 |
0.83, 4.89 |
0.124 |
| CAD |
|
|
|
| N |
— |
— |
|
| Y |
1.18 |
0.50, 2.76 |
0.702 |
| Dialysis_Vintage |
0.77 |
0.60, 0.97 |
0.028 |
| HF_score |
|
|
|
| 1 |
— |
— |
|
| 2 |
2.38 |
1.31, 4.32 |
0.004 |
| 3 |
2.00 |
1.05, 3.80 |
0.035 |
Univariate linear regression outcome n_90
outcome n_90
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
186 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
-0.32 |
-0.69, 0.04 |
0.086 |
| Age |
186 |
0.00 |
-0.01, 0.02 |
0.553 |
| Gender |
186 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
-0.02 |
-0.39, 0.36 |
0.924 |
| Race_Group |
186 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
0.31 |
-0.46, 1.1 |
0.432 |
| Black |
|
-0.19 |
-0.62, 0.24 |
0.393 |
| Other |
|
0.32 |
-0.24, 0.88 |
0.264 |
| HTN |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.35 |
-0.94, 1.6 |
0.596 |
| DM |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.24 |
-0.25, 0.72 |
0.343 |
| CAD |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.28 |
-0.52, 1.1 |
0.498 |
| Dialysis_Vintage |
186 |
-0.10 |
-0.19, 0.00 |
0.047 |
| HF_score |
186 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
0.03 |
-0.51, 0.57 |
0.911 |
| 3 |
|
0.27 |
-0.17, 0.70 |
0.236 |
| weight.ATE |
186 |
-0.14 |
-0.25, -0.03 |
0.015 |
Multivariate linear regression n_90: age gender race
outcome n_90
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
-0.33 |
-0.71, 0.04 |
0.083 |
| Age |
186 |
0.00 |
-0.01, 0.02 |
0.653 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
-0.07 |
-0.46, 0.31 |
0.706 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
0.41 |
-0.38, 1.2 |
0.313 |
| Black |
52 |
-0.17 |
-0.64, 0.30 |
0.474 |
| Other |
25 |
0.30 |
-0.27, 0.87 |
0.308 |
Multivariate linear regression n_90 ： group+
Age+Gender+Race_Group+HTN+DM+CAD+Dialysis_Vintage+HF_score
outcome n_90
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
-0.35 |
-0.72, 0.03 |
0.074 |
| Age |
186 |
0.00 |
-0.01, 0.02 |
0.871 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
-0.28 |
-0.70, 0.13 |
0.180 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
0.58 |
-0.22, 1.4 |
0.159 |
| Black |
52 |
-0.04 |
-0.52, 0.45 |
0.882 |
| Other |
25 |
0.45 |
-0.14, 1.0 |
0.140 |
| HTN |
|
|
|
|
| N |
4 |
— |
— |
|
| Y |
182 |
0.06 |
-1.4, 1.5 |
0.938 |
| DM |
|
|
|
|
| N |
32 |
— |
— |
|
| Y |
154 |
0.52 |
-0.01, 1.0 |
0.057 |
| CAD |
|
|
|
|
| N |
10 |
— |
— |
|
| Y |
176 |
0.08 |
-0.83, 0.99 |
0.868 |
| Dialysis_Vintage |
186 |
-0.11 |
-0.21, -0.01 |
0.038 |
| HF_score |
|
|
|
|
| 1 |
51 |
— |
— |
|
| 2 |
38 |
0.15 |
-0.41, 0.71 |
0.600 |
| 3 |
96 |
0.41 |
-0.06, 0.88 |
0.088 |
Univariate linear regression outcome n_180
outcome n_180
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
186 |
|
|
|
| BWH |
|
— |
— |
|
| MGH |
|
-0.17 |
-0.69, 0.35 |
0.532 |
| Age |
186 |
0.00 |
-0.02, 0.02 |
0.801 |
| Gender |
186 |
|
|
|
| Female |
|
— |
— |
|
| Male |
|
-0.08 |
-0.61, 0.44 |
0.755 |
| Race_Group |
186 |
|
|
|
| White |
|
— |
— |
|
| Asian |
|
0.18 |
-0.90, 1.3 |
0.745 |
| Black |
|
0.15 |
-0.45, 0.76 |
0.620 |
| Other |
|
0.85 |
0.07, 1.6 |
0.036 |
| HTN |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.6 |
-0.23, 3.3 |
0.090 |
| DM |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
0.31 |
-0.37, 1.0 |
0.371 |
| CAD |
186 |
|
|
|
| N |
|
— |
— |
|
| Y |
|
1.2 |
0.09, 2.3 |
0.036 |
| Dialysis_Vintage |
186 |
-0.16 |
-0.29, -0.03 |
0.018 |
| HF_score |
186 |
|
|
|
| 1 |
|
— |
— |
|
| 2 |
|
-0.29 |
-1.1, 0.47 |
0.457 |
| 3 |
|
-0.13 |
-0.74, 0.49 |
0.692 |
| weight.ATE |
186 |
-0.07 |
-0.23, 0.09 |
0.386 |
Multivariate linear regression n_180: age gender race
outcome n_180
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
-0.13 |
-0.66, 0.40 |
0.625 |
| Age |
186 |
0.00 |
-0.02, 0.02 |
0.919 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
-0.05 |
-0.59, 0.50 |
0.872 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
0.22 |
-0.89, 1.3 |
0.700 |
| Black |
52 |
0.15 |
-0.51, 0.81 |
0.649 |
| Other |
25 |
0.83 |
0.02, 1.6 |
0.047 |
Multivariate linear regression n_180 ： group+
Age+Gender+Race_Group+HTN+DM+CAD+Dialysis_Vintage+HF_score
outcome n_180
| Characteristic |
N |
Beta |
95% CI |
p-value |
| group |
|
|
|
|
| BWH |
96 |
— |
— |
|
| MGH |
90 |
-0.10 |
-0.62, 0.42 |
0.710 |
| Age |
186 |
0.00 |
-0.02, 0.02 |
0.884 |
| Gender |
|
|
|
|
| Female |
80 |
— |
— |
|
| Male |
106 |
-0.28 |
-0.86, 0.29 |
0.339 |
| Race_Group |
|
|
|
|
| White |
96 |
— |
— |
|
| Asian |
11 |
0.47 |
-0.64, 1.6 |
0.410 |
| Black |
52 |
0.32 |
-0.36, 0.99 |
0.364 |
| Other |
25 |
0.88 |
0.05, 1.7 |
0.041 |
| HTN |
|
|
|
|
| N |
4 |
— |
— |
|
| Y |
182 |
0.55 |
-1.5, 2.6 |
0.600 |
| DM |
|
|
|
|
| N |
32 |
— |
— |
|
| Y |
154 |
0.51 |
-0.22, 1.2 |
0.173 |
| CAD |
|
|
|
|
| N |
10 |
— |
— |
|
| Y |
176 |
0.82 |
-0.45, 2.1 |
0.208 |
| Dialysis_Vintage |
186 |
-0.19 |
-0.33, -0.05 |
0.009 |
| HF_score |
|
|
|
|
| 1 |
51 |
— |
— |
|
| 2 |
38 |
-0.07 |
-0.85, 0.70 |
0.850 |
| 3 |
96 |
0.18 |
-0.47, 0.83 |
0.584 |