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Proportion difference estimation

Source: R/estimate_proportion_diff.R
prop_diff.Rd

The analysis function a_proportion_diff_j() can be used to create a layout element to estimate the difference in proportion of responders within a studied population. The primary analysis variable, vars, is a logical variable indicating whether a response has occurred for each record. See the method parameter for options of methods to use when constructing the confidence interval of the proportion difference. A stratification variable can be supplied via the strata element of the variables argument.

Usage

a_proportion_diff_j(
  df,
  .var,
  ref_path,
  .spl_context,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_proportion_diff_j(
  df,
  .var,
  .ref_group,
  .in_ref_col,
  variables = list(strata = NULL),
  conf_level = 0.95,
  method = c("waldcc", "wald", "cmh", "ha", "newcombe", "newcombecc", "strat_newcombe",
    "strat_newcombecc"),
  weights_method = "cmh"
)

Arguments

df

(data.frame)
input data frame.

.var

(string)
name of the response variable.

ref_path

(character)
path to the reference group.

.spl_context

(environment)
split context environment.

...

Additional arguments passed to the statistics function.

.stats

(character)
statistics to calculate.

.formats

(list)
formats for the statistics.

.labels

(list)
labels for the statistics.

.indent_mods

(list)
indentation modifications for the statistics.

.ref_group

(data.frame)
reference group data frame.

.in_ref_col

(logical)
whether the current column is the reference column.

variables

(list)
list with strata variable names.

conf_level

(numeric)
confidence level for the confidence interval.

method

(string)
method to use for confidence interval calculation.

weights_method

(string)
method to use for weights calculation in stratified analysis.

Value

  • s_proportion_diff_j() returns a named list of elements diff, diff_ci, diff_est_ci and diff_ci_3d.

Functions

  • a_proportion_diff_j(): Formatted analysis function which is used as afun in estimate_proportion_diff().

  • s_proportion_diff_j(): Statistics function estimating the difference in terms of responder proportion.

Note

The a_proportion_diff_j() function has the _j suffix to distinguish it from tern::a_proportion_diff(). The functions here are a copy from the tern package with additional features:

  • Additional statistic diff_est_ci is returned.

  • ref_path needs to be provided as extra argument to specify the control group column.

When performing an unstratified analysis, methods 'cmh', 'strat_newcombe', and 'strat_newcombecc' are not permitted.

Examples

nex <- 100
dta <- data.frame(
  "rsp" = sample(c(TRUE, FALSE), nex, TRUE),
  "grp" = sample(c("A", "B"), nex, TRUE),
  "f1" = sample(c("a1", "a2"), nex, TRUE),
  "f2" = sample(c("x", "y", "z"), nex, TRUE),
  stringsAsFactors = TRUE
)

l <- basic_table() |>
  split_cols_by(var = "grp") |>
  analyze(
    vars = "rsp",
    afun = a_proportion_diff_j,
    show_labels = "hidden",
    na_str = tern::default_na_str(),
    extra_args = list(
      conf_level = 0.9,
      method = "ha",
      ref_path = c("grp", "B")
    )
  )

build_table(l, df = dta)
#>                                           A           B
#> ———————————————————————————————————————————————————————
#> Difference in Response rate (%)         12.0           
#>   90% CI (Anderson-Hauck)           (-5.4, 29.4)       
#> % Difference (90% CI)             12.0 (-5.4, 29.4)    

s_proportion_diff_j(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  conf_level = 0.90,
  method = "ha"
)
#> $diff
#> diff_ha 
#>      12 
#> attr(,"label")
#> [1] "Difference in Response rate (%)"
#> 
#> $diff_ci
#> diff_ci_ha_l diff_ci_ha_u 
#>    -5.374519    29.374519 
#> attr(,"label")
#> [1] "90% CI (Anderson-Hauck)"
#> 
#> $diff_est_ci
#>      diff_ha diff_ci_ha_l diff_ci_ha_u 
#>    12.000000    -5.374519    29.374519 
#> attr(,"label")
#> [1] "% Difference (90% CI)"
#> 
#> $diff_ci_3d
#>      diff_ha diff_ci_ha_l diff_ci_ha_u 
#>    12.000000    -5.374519    29.374519 
#> attr(,"label")
#> [1] "Relative Risk (90% CI)"
#> 

s_proportion_diff_j(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  variables = list(strata = c("f1", "f2")),
  conf_level = 0.90,
  method = "cmh"
)
#> $diff
#> diff_cmh 
#> 12.27932 
#> attr(,"label")
#> [1] "Difference in Response rate (%)"
#> 
#> $diff_ci
#> diff_ci_cmh_l diff_ci_cmh_u 
#>     -2.657093     27.215725 
#> attr(,"label")
#> [1] "90% CI (CMH, without correction)"
#> 
#> $diff_est_ci
#>      diff_cmh diff_ci_cmh_l diff_ci_cmh_u 
#>     12.279316     -2.657093     27.215725 
#> attr(,"label")
#> [1] "% Difference (90% CI)"
#> 
#> $diff_ci_3d
#>      diff_cmh diff_ci_cmh_l diff_ci_cmh_u 
#>     12.279316     -2.657093     27.215725 
#> attr(,"label")
#> [1] "Relative Risk (90% CI)"
#>