junco (version 0.1.2)

resp01_acfun: Formatted Analysis and Content Summary Function for Response Tables (RESP01)

Description

This function applies to both factor and logical columns called .var from df. Depending on the position in the split, it returns the right formatted results for the RESP01 and related layouts.

Usage

resp01_acfun(
  df,
  labelstr = NULL,
  label = NULL,
  .var,
  .spl_context,
  include_comp,
  .alt_df,
  conf_level,
  arm,
  strata,
  formats,
  methods
)

Value

The formatted result as rtables::in_rows() result.

Arguments

df

(data.frame)
data set containing all analysis variables.

labelstr

(character)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information.

label

(string)
only for logicals, which label to use. (For factors, the labels are the factor levels.)

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

.spl_context

(data.frame)
gives information about ancestor split states that is passed by rtables.

include_comp

(character or flag)
whether to include comparative statistic results, either character for factors or flag for logicals.

.alt_df

(data.frame)
alternative data frame used for denominator calculation.

conf_level

(proportion)
confidence level of the interval.

arm

(string)
column name in the data frame that identifies the treatment arms.

strata

(character or NULL)
variable names indicating stratification factors.

formats

(list)
containing formats for prop_ci, comp_stat_ci and pval.

methods

(list)
containing methods for comparative statistics. The element comp_stat_ci can be 'rr' (relative risk), 'or_cmh' (odds ratio with CMH estimation and p-value) or 'or_logistic' (odds ratio estimated by conditional or standard logistic regression). The element pval can be 'fisher' (Fisher's exact test) or 'chisq' (chi-square test), only used when using unstratified analyses with 'or_logistic'. The element prop_ci specifies the method for proportion confidence interval calculation.

Examples

fake_spl_context <- data.frame(
  cur_col_split_val = I(list(c(ARM = "A: Drug X", count_prop = "count_prop")))
)
dm <- droplevels(subset(DM, SEX %in% c("F", "M")))
resp01_acfun(
  dm,
  .alt_df = dm,
  .var = "COUNTRY",
  .spl_context = fake_spl_context,
  conf_level = 0.9,
  include_comp = c("USA", "CHN"),
  arm = "SEX",
  strata = "RACE",
  methods = list(
    comp_stat_ci = "or_cmh",
    pval = "",
    prop_ci = "wald"
  ),
  formats = list(
    prop_ci = jjcsformat_xx("xx.% - xx.%"),
    comp_stat_ci = jjcsformat_xx("xx.xx (xx.xx - xx.xx)"),
    pval = jjcsformat_pval_fct(0.05)
  )
)
fake_spl_context2 <- data.frame(
  cur_col_split_val = I(list(c(ARM = "Overall", comp_stat_ci = "comp_stat_ci")))
)
resp01_acfun(
  dm,
  .alt_df = dm,
  .var = "COUNTRY",
  .spl_context = fake_spl_context2,
  conf_level = 0.9,
  include_comp = c("USA", "CHN"),
  arm = "SEX",
  strata = "RACE",
  methods = list(
    comp_stat_ci = "or_cmh",
    pval = "",
    prop_ci = "wald"
  ),
  formats = list(
    prop_ci = jjcsformat_xx("xx.% - xx.%"),
    comp_stat_ci = jjcsformat_xx("xx.xx (xx.xx - xx.xx)"),
    pval = jjcsformat_pval_fct(0.05)
  )
)