Introducing jstable options
Jinseob Kim
2025-07-15
Source:vignettes/jstable_options.Rmd
jstable_options.RmdTableSubgroupMultiCox
lung %>%
mutate(
status = as.integer(status == 1),
sex = factor(sex),
kk = factor(as.integer(pat.karno >= 70)),
kk1 = factor(as.integer(pat.karno >= 60)),
ph.ecog = factor(ph.ecog)
) -> lung
lung.label <- mk.lev(lung)
lung.label <- lung.label %>%
mutate(
val_label = case_when(
variable == "sex" & level == "1" ~ "Male",
variable == "sex" & level == "2" ~ "Female",
variable == "kk" & level == "0" ~ "No",
variable == "kk" & level == "1" ~ "Yes",
variable == "kk1" & level == "0" ~ "No",
variable == "kk1" & level == "1" ~ "Yes",
TRUE ~ val_label
)
)Counting the Number of Independent Variables for Comparison
The default option for count_by is set to NULL. By specifying an independent variable in the count_by option, the table will display the counts for each level of the independent variable.
TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = lung, time_eventrate = 100, line = TRUE, cluster = "inst", strata = "inst", weights = "age", event = FALSE, count_by = "sex", labeldata = lung.label)
#> Variable Count Count(sex=Male) Count(sex=Female) Percent Point Estimate
#> sex Overall 227 138 90 100 1.66
#> 1 <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 kk <NA> <NA> <NA> <NA> <NA>
#> 3 No 38 22 16 17 193294637.42
#> 4 Yes 186 114 73 83 1.44
#> 5 <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 kk1 <NA> <NA> <NA> <NA> <NA>
#> 7 No 8 4 4 3.6 <NA>
#> 8 Yes 216 132 85 96.4 1.55
#> Lower Upper sex=1 sex=2 P value P for interaction
#> sex 1.09 2.53 0 1.2 0.019 <NA>
#> 1 <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> 0.562
#> 3 27228158.02 1372212428.98 0 0 <0.001 <NA>
#> 4 0.95 2.18 0 1.5 0.084 <NA>
#> 5 <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <0.001
#> 7 <NA> <NA> 0 0 <NA> <NA>
#> 8 1 2.41 0 1.3 0.049 <NA>Calculate crude incidence rate of event
The default value for the event option is set to FALSE. By setting event to TRUE, the table will display the crude incidence rate of events. This rate is calculated using the number of events as the numerator and the count of the independent variable as the denominator.(Different from Kaplan-Meier Estimates)
TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = lung, time_eventrate = 100, line = TRUE, cluster = "inst", strata = "inst", weights = "age", event = TRUE, count_by = "sex", labeldata = lung.label)
#> Variable Count Count(sex=Male) Count(sex=Female) Percent
#> sex Overall 63/228 (27.6%) 26/138 (18.8%) 37/90 (41.1%) 100
#> 1 <NA> <NA> <NA> <NA> <NA>
#> 2 kk <NA> <NA> <NA> <NA>
#> 3 No 5/38 (13.2%) 1/22 (4.5%) 4/16 (25%) 17
#> 4 Yes 58/187 (31%) 25/114 (21.9%) 33/73 (45.2%) 83
#> 5 <NA> <NA> <NA> <NA> <NA>
#> 6 kk1 <NA> <NA> <NA> <NA>
#> 7 No 2/8 (25%) 0/4 (0%) 2/4 (50%) 3.6
#> 8 Yes 61/217 (28.1%) 26/132 (19.7%) 35/85 (41.2%) 96.4
#> Point Estimate Lower Upper sex=1 sex=2 P value
#> sex 1.66 1.09 2.53 0 1.2 0.019
#> 1 <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 193294637.42 27228158.02 1372212428.98 0 0 <0.001
#> 4 1.44 0.95 2.18 0 1.5 0.084
#> 5 <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 <NA> <NA> <NA> 0 0 <NA>
#> 8 1.55 1 2.41 0 1.3 0.049
#> P for interaction
#> sex <NA>
#> 1 <NA>
#> 2 0.562
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 <0.001
#> 7 <NA>
#> 8 <NA>Using both count_by and event option is also available
By using both count_by and event option, the table will display crude incidence rate and the counts for each level of the independant variable.
TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = lung, time_eventrate = 100, line = TRUE, cluster = "inst", strata = "inst", weights = "age", event = TRUE, count_by = NULL, labeldata = lung.label)
#> Variable Count Percent Point Estimate Lower Upper
#> sex Overall 63/228 (27.6%) 100 1.66 1.09 2.53
#> 1 <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 kk <NA> <NA> <NA> <NA> <NA>
#> 3 No 38 17 193294637.42 27228158.02 1372212428.98
#> 4 Yes 186 83 1.44 0.95 2.18
#> 5 <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 kk1 <NA> <NA> <NA> <NA> <NA>
#> 7 No 8 3.6 <NA> <NA> <NA>
#> 8 Yes 216 96.4 1.55 1 2.41
#> sex=1 sex=2 P value P for interaction
#> sex 0 1.2 0.019 <NA>
#> 1 <NA> <NA> <NA> <NA>
#> 2 <NA> <NA> <NA> 0.562
#> 3 0 0 <0.001 <NA>
#> 4 0 1.5 0.084 <NA>
#> 5 <NA> <NA> <NA> <NA>
#> 6 <NA> <NA> <NA> <0.001
#> 7 0 0 <NA> <NA>
#> 8 0 1.3 0.049 <NA>Introducing pairwise option
Introducing pairwise, pairwise.showtest option in CreateTableOneJS
The default value for the pairwise option is FALSE. By setting pairwise to TRUE, the table will display p-values for pairwise comparisons of stratified groups.
CreateTableOneJS(vars = names(lung), strata = "ph.ecog", data = lung, showAllLevels = F, labeldata = lung.label, Labels = T, pairwise = T)
#> $table
#> 0 1 2
#> n " 63" " 113" " 50"
#> inst " 9.60 ± 8.20" " 11.41 ± 7.99" " 12.02 ± 9.08"
#> time "351.87 ± 220.47" "314.44 ± 207.08" "234.08 ± 189.85"
#> status " 0.41 ± 0.50" " 0.27 ± 0.45" " 0.12 ± 0.33"
#> age " 61.16 ± 9.57" " 61.45 ± 8.87" " 66.22 ± 8.11"
#> sex = Female (%) " 27 ( 42.9) " " 42 ( 37.2) " " 21 ( 42.0) "
#> ph.ecog (%) " " " " " "
#> 0 " 63 (100.0) " " 0 ( 0.0) " " 0 ( 0.0) "
#> 1 " 0 ( 0.0) " " 113 (100.0) " " 0 ( 0.0) "
#> 2 " 0 ( 0.0) " " 0 ( 0.0) " " 50 (100.0) "
#> 3 " 0 ( 0.0) " " 0 ( 0.0) " " 0 ( 0.0) "
#> ph.karno " 93.97 ± 5.83" " 82.65 ± 7.44" " 65.71 ± 7.91"
#> pat.karno " 87.42 ± 10.23" " 82.30 ± 11.95" " 65.21 ± 15.16"
#> meal.cal "924.84 ± 388.40" "992.26 ± 384.22" "796.12 ± 435.37"
#> wt.loss " 6.00 ± 8.81" " 10.59 ± 14.18" " 12.51 ± 14.40"
#> kk = Yes (%) " 61 ( 98.4) " " 101 ( 89.4) " " 23 ( 47.9) "
#> kk1 = Yes (%) " 62 (100.0) " " 113 (100.0) " " 40 ( 83.3) "
#> 3 p test p(0 vs 1) p(0 vs 2)
#> n " 1" "" "" "" ""
#> inst " 13.00 ± NA" " NA" "" " 0.161" " 0.148"
#> time " 118.00 ± NA" " NA" "" " 0.272" " 0.003"
#> status " 0.00 ± NA" " NA" "" " 0.069" "<0.001"
#> age " 70.00 ± NA" " NA" "" " 0.842" " 0.003"
#> sex = Female (%) " 0 ( 0.0) " " 0.823" "exact" " 0.562" " 1.000"
#> ph.ecog (%) " " " NA" "exact" "" ""
#> 0 " 0 ( 0.0) " "" "" "" ""
#> 1 " 0 ( 0.0) " "" "" "" ""
#> 2 " 0 ( 0.0) " "" "" "" ""
#> 3 " 1 (100.0) " "" "" "" ""
#> ph.karno " 60.00 ± NA" " NA" "" "<0.001" "<0.001"
#> pat.karno " 70.00 ± NA" " NA" "" " 0.003" "<0.001"
#> meal.cal "1075.00 ± NA" " NA" "" " 0.329" " 0.139"
#> wt.loss " 20.00 ± NA" " NA" "" " 0.011" " 0.009"
#> kk = Yes (%) " 1 (100.0) " "<0.001" "exact" " 0.034" "<0.001"
#> kk1 = Yes (%) " 1 (100.0) " "<0.001" "exact" " 1.000" " 0.001"
#> p(0 vs 3) p(1 vs 2) p(1 vs 3) p(2 vs 3) sig
#> n "" "" "" "" NA
#> inst " NA" " 0.684" " NA" " NA" NA
#> time " NA" " 0.017" " NA" " NA" NA
#> status " NA" " 0.015" " NA" " NA" NA
#> age " NA" " 0.001" " NA" " NA" NA
#> sex = Female (%) " 1.000" " 0.682" " 1.000" " 1.000" ""
#> ph.ecog (%) "" "" "" "" NA
#> 0 "" "" "" "" NA
#> 1 "" "" "" "" NA
#> 2 "" "" "" "" NA
#> 3 "" "" "" "" NA
#> ph.karno " NA" "<0.001" " NA" " NA" NA
#> pat.karno " NA" "<0.001" " NA" " NA" NA
#> meal.cal " NA" " 0.014" " NA" " NA" NA
#> wt.loss " NA" " 0.455" " NA" " NA" NA
#> kk = Yes (%) " 1.000" "<0.001" " 1.000" " 0.490" "**"
#> kk1 = Yes (%) " 1.000" "<0.001" " 1.000" " 1.000" "**"
#>
#> $caption
#> [1] "Stratified by ph.ecog"By setting pairwise.showtest option to TRUE, the table will display test used to calculate p-values for pairwise comparisons of stratified groups. Default test for categorical variables are chi-sq test and continuous variables are t-test.
CreateTableOneJS(vars = names(lung), strata = "ph.ecog", data = lung, showAllLevels = F, labeldata = lung.label, Labels = T, pairwise = T, pairwise.showtest = T)
#> $table
#> 0 1 2
#> n " 63" " 113" " 50"
#> inst " 9.60 ± 8.20" " 11.41 ± 7.99" " 12.02 ± 9.08"
#> time "351.87 ± 220.47" "314.44 ± 207.08" "234.08 ± 189.85"
#> status " 0.41 ± 0.50" " 0.27 ± 0.45" " 0.12 ± 0.33"
#> age " 61.16 ± 9.57" " 61.45 ± 8.87" " 66.22 ± 8.11"
#> sex = Female (%) " 27 ( 42.9) " " 42 ( 37.2) " " 21 ( 42.0) "
#> ph.ecog (%) " " " " " "
#> 0 " 63 (100.0) " " 0 ( 0.0) " " 0 ( 0.0) "
#> 1 " 0 ( 0.0) " " 113 (100.0) " " 0 ( 0.0) "
#> 2 " 0 ( 0.0) " " 0 ( 0.0) " " 50 (100.0) "
#> 3 " 0 ( 0.0) " " 0 ( 0.0) " " 0 ( 0.0) "
#> ph.karno " 93.97 ± 5.83" " 82.65 ± 7.44" " 65.71 ± 7.91"
#> pat.karno " 87.42 ± 10.23" " 82.30 ± 11.95" " 65.21 ± 15.16"
#> meal.cal "924.84 ± 388.40" "992.26 ± 384.22" "796.12 ± 435.37"
#> wt.loss " 6.00 ± 8.81" " 10.59 ± 14.18" " 12.51 ± 14.40"
#> kk = Yes (%) " 61 ( 98.4) " " 101 ( 89.4) " " 23 ( 47.9) "
#> kk1 = Yes (%) " 62 (100.0) " " 113 (100.0) " " 40 ( 83.3) "
#> 3 p test p(0 vs 1) test(0 vs 1)
#> n " 1" "" "" "" ""
#> inst " 13.00 ± NA" " NA" "" " 0.161" ""
#> time " 118.00 ± NA" " NA" "" " 0.272" ""
#> status " 0.00 ± NA" " NA" "" " 0.069" ""
#> age " 70.00 ± NA" " NA" "" " 0.842" ""
#> sex = Female (%) " 0 ( 0.0) " " 0.823" "exact" " 0.562" ""
#> ph.ecog (%) " " " NA" "exact" "" ""
#> 0 " 0 ( 0.0) " "" "" "" ""
#> 1 " 0 ( 0.0) " "" "" "" ""
#> 2 " 0 ( 0.0) " "" "" "" ""
#> 3 " 1 (100.0) " "" "" "" ""
#> ph.karno " 60.00 ± NA" " NA" "" "<0.001" ""
#> pat.karno " 70.00 ± NA" " NA" "" " 0.003" ""
#> meal.cal "1075.00 ± NA" " NA" "" " 0.329" ""
#> wt.loss " 20.00 ± NA" " NA" "" " 0.011" ""
#> kk = Yes (%) " 1 (100.0) " "<0.001" "exact" " 0.034" "exact"
#> kk1 = Yes (%) " 1 (100.0) " "<0.001" "exact" " 1.000" "exact"
#> p(0 vs 2) test(0 vs 2) p(0 vs 3) test(0 vs 3) p(1 vs 2)
#> n "" "" "" "" ""
#> inst " 0.148" "" " NA" "" " 0.684"
#> time " 0.003" "" " NA" "" " 0.017"
#> status "<0.001" "" " NA" "" " 0.015"
#> age " 0.003" "" " NA" "" " 0.001"
#> sex = Female (%) " 1.000" "" " 1.000" "exact" " 0.682"
#> ph.ecog (%) "" "" "" "" ""
#> 0 "" "" "" "" ""
#> 1 "" "" "" "" ""
#> 2 "" "" "" "" ""
#> 3 "" "" "" "" ""
#> ph.karno "<0.001" "" " NA" "" "<0.001"
#> pat.karno "<0.001" "" " NA" "" "<0.001"
#> meal.cal " 0.139" "" " NA" "" " 0.014"
#> wt.loss " 0.009" "" " NA" "" " 0.455"
#> kk = Yes (%) "<0.001" "" " 1.000" "exact" "<0.001"
#> kk1 = Yes (%) " 0.001" "exact" " 1.000" "exact" "<0.001"
#> test(1 vs 2) p(1 vs 3) test(1 vs 3) p(2 vs 3) test(2 vs 3)
#> n "" "" "" "" ""
#> inst "" " NA" "" " NA" ""
#> time "" " NA" "" " NA" ""
#> status "" " NA" "" " NA" ""
#> age "" " NA" "" " NA" ""
#> sex = Female (%) "" " 1.000" "exact" " 1.000" "exact"
#> ph.ecog (%) "" "" "" "" ""
#> 0 "" "" "" "" ""
#> 1 "" "" "" "" ""
#> 2 "" "" "" "" ""
#> 3 "" "" "" "" ""
#> ph.karno "" " NA" "" " NA" ""
#> pat.karno "" " NA" "" " NA" ""
#> meal.cal "" " NA" "" " NA" ""
#> wt.loss "" " NA" "" " NA" ""
#> kk = Yes (%) "" " 1.000" "exact" " 0.490" "exact"
#> kk1 = Yes (%) "exact" " 1.000" "exact" " 1.000" "exact"
#> sig
#> n NA
#> inst NA
#> time NA
#> status NA
#> age NA
#> sex = Female (%) ""
#> ph.ecog (%) NA
#> 0 NA
#> 1 NA
#> 2 NA
#> 3 NA
#> ph.karno NA
#> pat.karno NA
#> meal.cal NA
#> wt.loss NA
#> kk = Yes (%) "**"
#> kk1 = Yes (%) "**"
#>
#> $caption
#> [1] "Stratified by ph.ecog"Introducing pairwise option in svyCreateTableOneJS
The default value for the pairwise option is FALSE. By setting pairwise to TRUE, the table will display p-values for pairwise comparisons of stratified groups.
library(survey)
data(nhanes)
nhanes$SDMVPSU <- as.factor(nhanes$SDMVPSU)
nhanes$race <- as.factor(nhanes$race)
nhanes$RIAGENDR <- as.factor(nhanes$RIAGENDR)
a.label <- mk.lev(nhanes)
a.label <- a.label %>%
dplyr::mutate(val_label = case_when(
variable == "race" & level == "1" ~ "White",
variable == "race" & level == "2" ~ "Black",
variable == "race" & level == "3" ~ "Hispanic",
variable == "race" & level == "4" ~ "Asian",
TRUE ~ val_label
))
nhanesSvy <- svydesign(ids = ~SDMVPSU, strata = ~SDMVSTRA, weights = ~WTMEC2YR, nest = TRUE, data = nhanes)
svyCreateTableOneJS(
vars = c("HI_CHOL", "race", "agecat", "RIAGENDR"),
strata = "race", data = nhanesSvy, factorVars = c("HI_CHOL", "race", "RIAGENDR"), labeldata = a.label, Labels = T, pairwise = T
)
#> $table
#> race White Black Hispanic
#> n "" "2717.0" "3743.0" "1623.0"
#> HI_CHOL (%) NA "2282.0 ( 89.9) " "3063.0 ( 87.8) " "1302.0 ( 92.1) "
#> NA " 250.0 ( 10.1) " " 387.0 ( 12.2) " " 104.0 ( 7.9) "
#> race (%) "White" "2717.0 (100.0) " " 0.0 ( 0.0) " " 0.0 ( 0.0) "
#> "Black" " 0.0 ( 0.0) " "3743.0 (100.0) " " 0.0 ( 0.0) "
#> "Hispanic" " 0.0 ( 0.0) " " 0.0 ( 0.0) " "1623.0 (100.0) "
#> "Asian" " 0.0 ( 0.0) " " 0.0 ( 0.0) " " 0.0 ( 0.0) "
#> agecat (%) "(0,19]" "1001.0 ( 28.3) " " 833.0 ( 18.2) " " 528.0 ( 24.4) "
#> "(19,39]" " 636.0 ( 37.4) " " 891.0 ( 26.3) " " 363.0 ( 31.7) "
#> "(39,59]" " 611.0 ( 24.7) " " 913.0 ( 32.0) " " 386.0 ( 29.2) "
#> "(59,Inf]" " 469.0 ( 9.6) " "1106.0 ( 23.5) " " 346.0 ( 14.7) "
#> RIAGENDR (%) "1" "1339.0 ( 51.4) " "1863.0 ( 49.1) " " 798.0 ( 45.6) "
#> "2" "1378.0 ( 48.6) " "1880.0 ( 50.9) " " 825.0 ( 54.4) "
#> Asian p test p(White vs Black)
#> n "508.0" "" "" ""
#> HI_CHOL (%) "412.0 ( 90.0) " " 0.059" "" " 0.031"
#> " 46.0 ( 10.0) " "" "" ""
#> race (%) " 0.0 ( 0.0) " " NA" "" "<0.001"
#> " 0.0 ( 0.0) " "" "" ""
#> " 0.0 ( 0.0) " "" "" ""
#> "508.0 (100.0) " "" "" ""
#> agecat (%) "170.0 ( 22.7) " "<0.001" "" "<0.001"
#> "143.0 ( 36.0) " "" "" ""
#> "111.0 ( 28.9) " "" "" ""
#> " 84.0 ( 12.4) " "" "" ""
#> RIAGENDR (%) "247.0 ( 45.8) " " 0.042" "" " 0.016"
#> "261.0 ( 54.2) " "" "" ""
#> p(White vs Hispanic) p(White vs Asian) p(Black vs Hispanic)
#> n "" "" ""
#> HI_CHOL (%) " 0.103" " 0.947" " 0.003"
#> "" "" ""
#> race (%) "<0.001" "<0.001" "<0.001"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) " 0.002" " 0.079" "<0.001"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) " 0.001" " 0.081" " 0.008"
#> "" "" ""
#> p(Black vs Asian) p(Hispanic vs Asian) sig
#> n "" "" NA
#> HI_CHOL (%) " 0.429" " 0.341" ""
#> "" "" NA
#> race (%) "<0.001" "<0.001" NA
#> "" "" NA
#> "" "" NA
#> "" "" NA
#> agecat (%) "<0.001" " 0.455" "**"
#> "" "" NA
#> "" "" NA
#> "" "" NA
#> RIAGENDR (%) " 0.262" " 0.944" "**"
#> "" "" NA
#>
#> $caption
#> [1] "Stratified by race- weighted data"By setting pairwise.showtest option to TRUE, the table will display test used to calculate p-values for pairwise comparisons of stratified groups.
svyCreateTableOneJS(
vars = c("HI_CHOL", "race", "agecat", "RIAGENDR"),
strata = "race", data = nhanesSvy, factorVars = c("HI_CHOL", "race", "RIAGENDR"), labeldata = a.label, Labels = T, pairwise = T, pairwise.showtest = T
)
#> $table
#> race White Black Hispanic
#> n "" "2717.0" "3743.0" "1623.0"
#> HI_CHOL (%) NA "2282.0 ( 89.9) " "3063.0 ( 87.8) " "1302.0 ( 92.1) "
#> NA " 250.0 ( 10.1) " " 387.0 ( 12.2) " " 104.0 ( 7.9) "
#> race (%) "White" "2717.0 (100.0) " " 0.0 ( 0.0) " " 0.0 ( 0.0) "
#> "Black" " 0.0 ( 0.0) " "3743.0 (100.0) " " 0.0 ( 0.0) "
#> "Hispanic" " 0.0 ( 0.0) " " 0.0 ( 0.0) " "1623.0 (100.0) "
#> "Asian" " 0.0 ( 0.0) " " 0.0 ( 0.0) " " 0.0 ( 0.0) "
#> agecat (%) "(0,19]" "1001.0 ( 28.3) " " 833.0 ( 18.2) " " 528.0 ( 24.4) "
#> "(19,39]" " 636.0 ( 37.4) " " 891.0 ( 26.3) " " 363.0 ( 31.7) "
#> "(39,59]" " 611.0 ( 24.7) " " 913.0 ( 32.0) " " 386.0 ( 29.2) "
#> "(59,Inf]" " 469.0 ( 9.6) " "1106.0 ( 23.5) " " 346.0 ( 14.7) "
#> RIAGENDR (%) "1" "1339.0 ( 51.4) " "1863.0 ( 49.1) " " 798.0 ( 45.6) "
#> "2" "1378.0 ( 48.6) " "1880.0 ( 50.9) " " 825.0 ( 54.4) "
#> Asian p test p(White vs Black)
#> n "508.0" "" "" ""
#> HI_CHOL (%) "412.0 ( 90.0) " " 0.059" "" " 0.031"
#> " 46.0 ( 10.0) " "" "" ""
#> race (%) " 0.0 ( 0.0) " " NA" "" "<0.001"
#> " 0.0 ( 0.0) " "" "" ""
#> " 0.0 ( 0.0) " "" "" ""
#> "508.0 (100.0) " "" "" ""
#> agecat (%) "170.0 ( 22.7) " "<0.001" "" "<0.001"
#> "143.0 ( 36.0) " "" "" ""
#> "111.0 ( 28.9) " "" "" ""
#> " 84.0 ( 12.4) " "" "" ""
#> RIAGENDR (%) "247.0 ( 45.8) " " 0.042" "" " 0.016"
#> "261.0 ( 54.2) " "" "" ""
#> test(White vs Black) p(White vs Hispanic) test(White vs Hispanic)
#> n "" "" ""
#> HI_CHOL (%) "" " 0.103" ""
#> "" "" ""
#> race (%) "" "<0.001" ""
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) "" " 0.002" ""
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) "" " 0.001" ""
#> "" "" ""
#> p(White vs Asian) test(White vs Asian) p(Black vs Hispanic)
#> n "" "" ""
#> HI_CHOL (%) " 0.947" "" " 0.003"
#> "" "" ""
#> race (%) "<0.001" "" "<0.001"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) " 0.079" "" "<0.001"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) " 0.081" "" " 0.008"
#> "" "" ""
#> test(Black vs Hispanic) p(Black vs Asian) test(Black vs Asian)
#> n "" "" ""
#> HI_CHOL (%) "" " 0.429" ""
#> "" "" ""
#> race (%) "" "<0.001" ""
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) "" "<0.001" ""
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) "" " 0.262" ""
#> "" "" ""
#> p(Hispanic vs Asian) test(Hispanic vs Asian) sig
#> n "" "" NA
#> HI_CHOL (%) " 0.341" "" ""
#> "" "" NA
#> race (%) "<0.001" "" NA
#> "" "" NA
#> "" "" NA
#> "" "" NA
#> agecat (%) " 0.455" "" "**"
#> "" "" NA
#> "" "" NA
#> "" "" NA
#> RIAGENDR (%) " 0.944" "" "**"
#> "" "" NA
#>
#> $caption
#> [1] "Stratified by race- weighted data"