Introducing jstable options

Jinseob Kim

2025-04-25

Introducing count_by, event options in TableSubgroupMultiCox

TableSubgroupMultiCox

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"