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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
)
)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>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 38 1/22 (4.5%) 4/16 (25%) 17
#> 4 Yes 186 25/114 (21.9%) 33/73 (45.2%) 83
#> 5 <NA> <NA> <NA> <NA> <NA>
#> 6 kk1 <NA> <NA> <NA> <NA>
#> 7 No 8 0/4 (0%) 2/4 (50%) 3.6
#> 8 Yes 216 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>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>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"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 (%) "**"
#>
#> $caption
#> [1] "Stratified by ph.ecog"library(survey)
data(nhanes)
nhanes$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
#> n "" "41633251.6" "181802696.6"
#> HI_CHOL (%) NA "34942048.8 ( 89.9) " "148741789.8 ( 87.8) "
#> NA " 3946904.7 ( 10.1) " " 20600334.9 ( 12.2) "
#> race (%) "White" "41633251.6 (100.0) " " 0.0 ( 0.0) "
#> "Black" " 0.0 ( 0.0) " "181802696.6 (100.0) "
#> "Hispanic" " 0.0 ( 0.0) " " 0.0 ( 0.0) "
#> "Asian" " 0.0 ( 0.0) " " 0.0 ( 0.0) "
#> agecat (%) "(0,19]" "11800237.9 ( 28.3) " " 33019782.9 ( 18.2) "
#> "(19,39]" "15552222.8 ( 37.4) " " 47901111.6 ( 26.3) "
#> "(39,59]" "10267085.3 ( 24.7) " " 58158804.5 ( 32.0) "
#> "(59,Inf]" " 4013705.6 ( 9.6) " " 42722997.6 ( 23.5) "
#> RIAGENDR (%) "1" "21381884.2 ( 51.4) " " 89315751.4 ( 49.1) "
#> "2" "20251367.4 ( 48.6) " " 92486945.1 ( 50.9) "
#> Hispanic Asian p test
#> n "33012683.8" "20087814.0" "" ""
#> HI_CHOL (%) "26641367.6 ( 92.1) " "16385458.6 ( 90.0) " " 0.059" ""
#> " 2273898.3 ( 7.9) " " 1814107.4 ( 10.0) " "" ""
#> race (%) " 0.0 ( 0.0) " " 0.0 ( 0.0) " " NA" ""
#> " 0.0 ( 0.0) " " 0.0 ( 0.0) " "" ""
#> "33012683.8 (100.0) " " 0.0 ( 0.0) " "" ""
#> " 0.0 ( 0.0) " "20087814.0 (100.0) " "" ""
#> agecat (%) " 8064159.5 ( 24.4) " " 4566126.4 ( 22.7) " "<0.001" ""
#> "10459873.4 ( 31.7) " " 7224766.9 ( 36.0) " "" ""
#> " 9630300.4 ( 29.2) " " 5814433.3 ( 28.9) " "" ""
#> " 4858350.5 ( 14.7) " " 2482487.5 ( 12.4) " "" ""
#> RIAGENDR (%) "15045455.5 ( 45.6) " " 9201462.9 ( 45.8) " " 0.042" ""
#> "17967228.3 ( 54.4) " "10886351.1 ( 54.2) " "" ""
#> p(White vs Black) p(White vs Hispanic) p(White vs Asian)
#> n "" "" ""
#> HI_CHOL (%) " 0.031" " 0.103" " 0.947"
#> "" "" ""
#> race (%) "<0.001" "<0.001" "<0.001"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) "<0.001" " 0.002" " 0.079"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) " 0.016" " 0.001" " 0.081"
#> "" "" ""
#> p(Black vs Hispanic) p(Black vs Asian) p(Hispanic vs Asian) sig
#> n "" "" "" NA
#> HI_CHOL (%) " 0.003" " 0.429" " 0.341" ""
#> "" "" "" NA
#> race (%) "<0.001" "<0.001" "<0.001" NA
#> "" "" "" NA
#> "" "" "" NA
#> "" "" "" NA
#> agecat (%) "<0.001" "<0.001" " 0.455" "**"
#> "" "" "" NA
#> "" "" "" NA
#> "" "" "" NA
#> RIAGENDR (%) " 0.008" " 0.262" " 0.944" "**"
#> "" "" "" NA
#>
#> $caption
#> [1] "Stratified by race- weighted data"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
#> n "" "41633251.6" "181802696.6"
#> HI_CHOL (%) NA "34942048.8 ( 89.9) " "148741789.8 ( 87.8) "
#> NA " 3946904.7 ( 10.1) " " 20600334.9 ( 12.2) "
#> race (%) "White" "41633251.6 (100.0) " " 0.0 ( 0.0) "
#> "Black" " 0.0 ( 0.0) " "181802696.6 (100.0) "
#> "Hispanic" " 0.0 ( 0.0) " " 0.0 ( 0.0) "
#> "Asian" " 0.0 ( 0.0) " " 0.0 ( 0.0) "
#> agecat (%) "(0,19]" "11800237.9 ( 28.3) " " 33019782.9 ( 18.2) "
#> "(19,39]" "15552222.8 ( 37.4) " " 47901111.6 ( 26.3) "
#> "(39,59]" "10267085.3 ( 24.7) " " 58158804.5 ( 32.0) "
#> "(59,Inf]" " 4013705.6 ( 9.6) " " 42722997.6 ( 23.5) "
#> RIAGENDR (%) "1" "21381884.2 ( 51.4) " " 89315751.4 ( 49.1) "
#> "2" "20251367.4 ( 48.6) " " 92486945.1 ( 50.9) "
#> Hispanic Asian p test
#> n "33012683.8" "20087814.0" "" ""
#> HI_CHOL (%) "26641367.6 ( 92.1) " "16385458.6 ( 90.0) " " 0.059" ""
#> " 2273898.3 ( 7.9) " " 1814107.4 ( 10.0) " "" ""
#> race (%) " 0.0 ( 0.0) " " 0.0 ( 0.0) " " NA" ""
#> " 0.0 ( 0.0) " " 0.0 ( 0.0) " "" ""
#> "33012683.8 (100.0) " " 0.0 ( 0.0) " "" ""
#> " 0.0 ( 0.0) " "20087814.0 (100.0) " "" ""
#> agecat (%) " 8064159.5 ( 24.4) " " 4566126.4 ( 22.7) " "<0.001" ""
#> "10459873.4 ( 31.7) " " 7224766.9 ( 36.0) " "" ""
#> " 9630300.4 ( 29.2) " " 5814433.3 ( 28.9) " "" ""
#> " 4858350.5 ( 14.7) " " 2482487.5 ( 12.4) " "" ""
#> RIAGENDR (%) "15045455.5 ( 45.6) " " 9201462.9 ( 45.8) " " 0.042" ""
#> "17967228.3 ( 54.4) " "10886351.1 ( 54.2) " "" ""
#> p(White vs Black) test(White vs Black) p(White vs Hispanic)
#> n "" "" ""
#> HI_CHOL (%) " 0.031" "" " 0.103"
#> "" "" ""
#> race (%) "<0.001" "" "<0.001"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) "<0.001" "" " 0.002"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) " 0.016" "" " 0.001"
#> "" "" ""
#> test(White vs Hispanic) p(White vs Asian) test(White vs Asian)
#> n "" "" ""
#> HI_CHOL (%) "" " 0.947" ""
#> "" "" ""
#> race (%) "" "<0.001" ""
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) "" " 0.079" ""
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) "" " 0.081" ""
#> "" "" ""
#> p(Black vs Hispanic) test(Black vs Hispanic) p(Black vs Asian)
#> n "" "" ""
#> HI_CHOL (%) " 0.003" "" " 0.429"
#> "" "" ""
#> race (%) "<0.001" "" "<0.001"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) "<0.001" "" "<0.001"
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) " 0.008" "" " 0.262"
#> "" "" ""
#> test(Black vs Asian) p(Hispanic vs Asian) test(Hispanic vs Asian)
#> n "" "" ""
#> HI_CHOL (%) "" " 0.341" ""
#> "" "" ""
#> race (%) "" "<0.001" ""
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> agecat (%) "" " 0.455" ""
#> "" "" ""
#> "" "" ""
#> "" "" ""
#> RIAGENDR (%) "" " 0.944" ""
#> "" "" ""
#> sig
#> n NA
#> HI_CHOL (%) ""
#> NA
#> race (%) NA
#> NA
#> NA
#> NA
#> agecat (%) "**"
#> NA
#> NA
#> NA
#> RIAGENDR (%) "**"
#> NA
#>
#> $caption
#> [1] "Stratified by race- weighted data"These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.