Generate cohort characteristics

Usage

doCohortCharacteristics(
  input.d,
  marker.name,
  marker.description,
  var.names,
  is.var.continuous,
  var.descriptions,
  marker.value.labels.tolower = TRUE,
  show.missing = TRUE,
  show.missing.continuous = TRUE,
  do.droplevels = TRUE,
  show.percent = "both",
  stat.tests = NULL,
  chisq.test.simulate.p.value = FALSE,
  stat.test.column.header = "association/correlation test",
  show.test.name = TRUE,
  round.digits.p.value = 4,
  num.boot = 1000,
  missing.codes.highlight = NULL,
  missing.codes = c("N/A", "", "Unk"),
  decimal = 0,
  caption = NA,
  html.table.border = 0,
  banded.rows = FALSE,
  css.class.name.odd = "odd",
  css.class.name.even = "even",
  custom.marker.labels = NULL,
  custom.total.label = NULL,
  split.table = 200,
  ...
)

Arguments

input.d: The data.frame containing the data
marker.name: The variable that you want to split into different columns
marker.description: The description for the variable(s) to split
var.names: The variables that you want the statistics for
is.var.continuous: Vector of length equal to the length of var.names with 1 indicating a continuous variable and 0 otherwise (this should be inferred in the function)
var.descriptions: Vector of strings to describe the variables as they are to appear in the table
marker.value.labels.tolower: Indicator as to whether to put marker value labels to lower case
show.missing: an indicator to whether to show missing values
show.missing.continuous: if set to FALSE and show.missing == FALSE, will not show the number of missing cases for continuous variables. Otherwise, it shows the number of missing for continuous variables even if show.missing == FALSE.
do.droplevels: drop categories of unobserved levels set to TRUE
show.percent: defaults to "both" which shows both rows and columns other possible values: "column", "row".
stat.tests: statistical test to perform. NULL indicates do not do test for all variables, NA indicates do not do test for specified variable. Tests: chisq, fisher, ttest, wilcox, kendall, spearman, pearson, kruskal, confusionMarkerAsRef, confusionVarAsRef
chisq.test.simulate.p.value: Whether to simulate p-value for chi-square test. this parameter is ignored if chi-square is not used. Default value=FALSE
stat.test.column.header: The name to show on the header defaults to "association/correlation test"
show.test.name: logical; if TRUE (default), the name of each test is prepended to the p-value
round.digits.p.value: The number of digits to round the P values
num.boot: the number of bootstrap samples for any bootstrap method that may be used
missing.codes.highlight: default to NULL this indicates whether we wanted the missing values broken down down or lumped together.
missing.codes: a vector to indicate how missing values are coded, default is c("N/A", "", "Unk")
decimal: number of decimal places to show for aggregate numbers such as proportions or averages; default to 0
caption: caption to use for the Table
html.table.border: the border type to use for html tables
banded.rows: If TRUE, rows have alternating shading colour
css.class.name.odd: Used to set the row colour for odd rows
css.class.name.even: Used to set the row colour for even rows
custom.marker.labels: labels of marker to show; default NULL means using existing value label of the marker
custom.total.label: label of the "Total" column; default NULL means show "Total"
split.table: number of chars per row before table is split.
...: additional arguments to pander

Value

A table with statistics reported for multiple variables, such as mean, median, and range for continuous variables and proportions and percentages for categorical variables. Relevant association and correlation tests are performed as well.

Author

Aline Talhouk

Examples

dcc <- doCohortCharacteristics( input.d = mtcars, marker.name = "cyl",
marker.description = "cylinders", var.names = c("disp", "hp"),
var.descriptions = c("displacement", "horsepower"), is.var.continuous =
c(TRUE, TRUE), caption = "Some mtcars summaries")
htmlTable::htmlTable(dcc$result.table.html)
#> <table class='gmisc_table' style='border-collapse: collapse; margin-top: 1em; margin-bottom: 1em;'  id='table_4'>
#> <tbody>
#> <tr style='border-top: 2px solid grey;'>
#> <td style='border-top: 2px solid grey; border-bottom: 2px solid grey; text-align: center;'><table border=0><caption style='display: table-caption; text-align: left;'>Some mtcars summaries</caption><tr><th style='border-bottom: 1px solid grey; border-top: 4px double grey; text-align: center; padding-right:10px; padding-right:10px;' colspan=2></th><th style='border-bottom: 1px solid grey; border-top: 4px double grey; text-align: center; padding-right:10px; padding-right:10px;'>total</th><th style='border-bottom: 1px solid grey; border-top: 4px double grey; text-align: center; padding-right:10px; padding-right:10px;'>cylinders 4</th><th style='border-bottom: 1px solid grey; border-top: 4px double grey; text-align: center; padding-right:10px; padding-right:10px;'>cylinders 6</th><th style='border-bottom: 1px solid grey; border-top: 4px double grey; text-align: center; padding-right:10px; padding-right:10px;'>cylinders 8</th></tr><tr><th style='text-align: left; padding-right:10px; padding-right:10px;' colspan=2>total</th><td>32 (100%)</td><td>11 (34%)</td><td>7 (22%)</td><td>14 (44%)</td><td></td></tr><tr><th style='text-align: left; padding-right:10px; padding-right:10px;' colspan=2>displacement</th><td></td><td></td><td></td><td></td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>mean (SD)</th><td>231 (124)</td><td>105 (27)</td><td>183 (42)</td><td>353 (68)</td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>median</th><td>196</td><td>108</td><td>168</td><td>350</td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>IQR</th><td>121 to 326</td><td> 79 to 121</td><td>160 to 196</td><td>302 to 390</td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>range</th><td> 71 to 472</td><td> 71 to 147</td><td>145 to 258</td><td>276 to 472</td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>missing</th><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th style='text-align: left; padding-right:10px; padding-right:10px;' colspan=2>horsepower</th><td></td><td></td><td></td><td></td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>mean (SD)</th><td>147 (69)</td><td>83 (21)</td><td>122 (24)</td><td>209 (51)</td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>median</th><td>123</td><td>91</td><td>110</td><td>192</td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>IQR</th><td> 96 to 180</td><td>66 to 96</td><td>110 to 123</td><td>176 to 241</td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>range</th><td> 52 to 335</td><td> 52 to 113</td><td>105 to 175</td><td>150 to 335</td></tr><tr><th>&nbsp;&nbsp;&nbsp;&nbsp;</th><th style='text-align: left; padding-right:10px; padding-right:10px;'>missing</th><td>0</td><td>0</td><td>0</td><td>0</td></tr></table></td>
#> </tr>
#> </tbody>
#> </table>