Get summary comparisons and statistics between variables as raw data.

Usage

extract_comparison(
  df,
  ...,
  label_fn = label_extractor(df),
  override_type = list(),
  p_format = names(.pvalue.defaults),
  override_method = list(),
  power_analysis = FALSE,
  override_power = list(),
  raw_output = FALSE
)

Arguments

df: a dataframe of individual observations. If using the tidyselect syntax data grouping defines the intervention group and should be present. if the formula interface is used the first variable in the right hand side of the formula is used as the intervention, in which case grouping is ignored.
...: the outcomes are specified either as a tidyselect specification, in which case the grouping of the df input determines the intervention and the output is the same a compare_population() call with a tidyselect. Alternatively a set of formulae can be provided that specify the outcomes on the left hand side, e.g. outcome1 ~ intervention + cov1, outcome2 ~ intervention + cov1, ... in this case the intervention must be the same for all formulae and used to determine the comparison groups.
label_fn: (optional) a function for mapping a co-variate column name to printable label. This is by default a no-operation and the output table will contain the dataframe column names as labels. A simple alternative would be some form of dplyr::case_when lookup, or a string function such as stringr::str_to_sentence. (N.b. this function must be vectorised). Any value provided here will be overridden by the options("tableone.labeller" = my_label_fn) which allows global setting of the labeller.
override_type: (optional) a named list of data summary types. The default type for a column in a data set are calculated using heurisitics depending on the nature of the data (categorical or continuous), and result of normality tests. if you want to override this the options are "subtype_count","median_iqr","mean_sd","skipped" and you specify this on a column by column bases with a named list (e.g c("Petal.Width"="mean_sd")). Overriding the default does not check the type of data is correct for the summary type and will potentially cause errors if this is not done correctly.
p_format: the format of the p-values: one of "sampl", "nejm", "jama", "lancet", "aim" but any value here is overridden by the option("tableone.pvalue_formatter"=function(...))
override_method: if you want to override the comparison method for a particular variable the options are "chi-sq trend","fisher","t-test","2-sided wilcoxon","2-sided ks","anova","kruskal-wallis","no comparison" and you specify this on a column by column bases with a named list (e.g c("Petal.Width"="t-test"))
power_analysis: conduct sample size based power analysis.
override_power: if you want to override the power calculation method for a particular variable the options are "fisher","t-test","2-sided wilcoxon","2-sided ks","anova","kruskal-wallis","no comparison" and you specify this on a column by column bases with a named list (e.g c("Petal.Width"="t-test"))
raw_output: return comparison as t1_signif dataframe rather than formatted table

Value

a list of accessor functions for the summary data allowing granular access to the results of the analysis:

comparison$compare(.variable, .characteristic = NULL) - prints a comparison between the different intervention groups for the specified variable (and optionally the given characteristic if it is a categorical variable).
comparison$filter(.variable, .intervention = NULL, .characteristic = NULL) extracts a given variable (e.g. gender), optionally for a given level of intervention (e.g. control) and if categorical a given characteristic (e.g. male). This will output a dataframe with all the calculated summary variables, for all qualifying intervention, variable and characteristic combinations, significance tests (and power analyses) for the qualifying variable (comparing intervention groups).
comparison$signif_tests(.variable) - extracts for a given variable (e.g. gender) the significance tests (and optionally power analyses) of the univariate comparison between different interventions and the variable.
comparison$summary_stats(.variable, .intervention = NULL, .characteristic = NULL) extracts a given variable (e.g. gender), optionally for a given level of intervention (e.g. control) and if categorical a given characteristic (e.g. male). This returns only the summary stats for all qualifying intervention, variable and characteristic combinations.