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Proportion Difference

Source: R/prop_diff.R
prop_diff.Rd

[Stable]

Usage 

d_proportion_diff(conf_level, method, long = FALSE)

prop_diff_wald(rsp, grp, conf_level = 0.95, correct = FALSE)

prop_diff_ha(rsp, grp, conf_level)

prop_diff_nc(rsp, grp, conf_level, correct = FALSE)

prop_diff_cmh(rsp, grp, strata, conf_level = 0.95)

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

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

estimate_proportion_diff(
  lyt,
  vars,
  ...,
  var_labels = vars,
  show_labels = "hidden",
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

conf_level

(proportion)
confidence level of the interval.

method

(string)
the method used for the confidence interval estimation.

long

(logical)
Whether a long or a short (default) description is required.

rsp

(logical)
whether each subject is a responder or not.

grp

(factor)
vector assigning observations to one out of two groups (e.g. reference and treatment group).

correct

logical
include the continuity correction.

strata

(factor)
with one level per stratum and same length as rsp.

df

(data frame)
data set containing all analysis variables.

.var

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

.ref_group

(data frame or vector)
the data corresponding to the reference group.

.in_ref_col

(logical)
TRUE when working with the reference level, FALSE otherwise.

variables

(named list of string)
list of additional analysis variables.

lyt

(layout)
input layout where analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

...

arguments passed to s_proportion_diff().

var_labels

character for label.

show_labels

label visibility: one of "default", "visible" and "hidden".

table_names

(character)
this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels.

Value

String describing the analysis.

Functions

  • d_proportion_diff(): This is an auxiliary function that describes the analysis in s_proportion_diff.

  • prop_diff_wald(): The Wald interval follows the usual textbook definition for a single proportion confidence interval using the normal approximation. It is possible to include a continuity correction for Wald's interval.

  • prop_diff_ha(): Anderson-Hauck confidence interval.

  • prop_diff_nc(): Newcombe confidence interval. It is based on the Wilson score confidence interval for a single binomial proportion.

  • prop_diff_cmh(): Calculates the weighted difference. This is defined as the difference in response rates between the experimental treatment group and the control treatment group, adjusted for stratification factors by applying Cochran-Mantel-Haenszel (CMH) weights. For the CMH chi-squared test, use stats::mantelhaen.test().

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

  • a_proportion_diff(): Formatted Analysis function which can be further customized by calling rtables::make_afun() on it. It is used as afun in rtables::analyze().

  • estimate_proportion_diff(): Adds a descriptive analyze layer to rtables pipelines. The analysis is applied to a dataframe and return the estimations, in rcells. The ellipsis (...) conveys arguments to s_proportion_diff(), for instance na.rm = FALSE if missing data should be accounted for.

Examples 

# Wald confidence interval
set.seed(2)
rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20)
grp <- factor(c(rep("A", 10), rep("B", 10)))
prop_diff_wald(rsp = rsp, grp = grp, conf_level = 0.95, correct = FALSE)
#> $diff
#> [1] 0
#> 
#> $diff_ci
#> [1] -0.4382613  0.4382613
#> 

# Anderson-Hauck confidence interval
## "Mid" case: 3/4 respond in group A, 1/2 respond in group B.
rsp <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE)
grp <- factor(c("A", "B", "A", "B", "A", "A"), levels = c("B", "A"))
prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.90)
#> $diff
#> [1] 0.25
#> 
#> $diff_ci
#> [1] -0.9195011  1.0000000
#> 

## Edge case: Same proportion of response in A and B.
rsp <- c(TRUE, FALSE, TRUE, FALSE)
grp <- factor(c("A", "A", "B", "B"), levels = c("A", "B"))
prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.6)
#> $diff
#> [1] 0
#> 
#> $diff_ci
#> [1] -0.8451161  0.8451161
#> 

# Newcombe confidence interval

set.seed(1)
rsp <- c(
  sample(c(TRUE, FALSE), size = 40, prob = c(3 / 4, 1 / 4), replace = TRUE),
  sample(c(TRUE, FALSE), size = 40, prob = c(1 / 2, 1 / 2), replace = TRUE)
)
grp <- factor(rep(c("A", "B"), each = 40), levels = c("B", "A"))
table(rsp, grp)
#>        grp
#> rsp      B  A
#>   FALSE 20 10
#>   TRUE  20 30
prop_diff_nc(rsp = rsp, grp = grp, conf_level = 0.9)
#> $diff
#> [1] 0.25
#> 
#> $diff_ci
#> [1] 0.07193388 0.40725819
#> 

# Cochran-Mantel-Haenszel confidence interval

set.seed(2)
rsp <- sample(c(TRUE, FALSE), 100, TRUE)
grp <- sample(c("Placebo", "Treatment"), 100, TRUE)
grp <- factor(grp, levels = c("Placebo", "Treatment"))
strata_data <- data.frame(
  "f1" = sample(c("a", "b"), 100, TRUE),
  "f2" = sample(c("x", "y", "z"), 100, TRUE),
  stringsAsFactors = TRUE
)

prop_diff_cmh(
  rsp = rsp, grp = grp, strata = interaction(strata_data),
  conf_level = 0.90
)
#> $prop
#>   Placebo Treatment 
#> 0.5331117 0.3954251 
#> 
#> $prop_ci
#> $prop_ci$Placebo
#> [1] 0.4306536 0.6355698
#> 
#> $prop_ci$Treatment
#> [1] 0.2890735 0.5017768
#> 
#> 
#> $diff
#> [1] -0.1376866
#> 
#> $diff_ci
#> [1] -0.285363076  0.009989872
#> 

# Summary

## "Mid" case: 4/4 respond in group A, 1/2 respond in group B.
dta <- data.frame(
  rsp = c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE),
  grp = factor(c("A", "B", "A", "B", "A", "A"), levels = c("B", "A"))
)

s_proportion_diff(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  conf_level = 0.90,
  method = "ha"
)
#> $diff
#> [1] 25
#> attr(,"label")
#> [1] "Difference in Response rate (%)"
#> 
#> $diff_ci
#> [1] -91.95011 100.00000
#> attr(,"label")
#> [1] "90% CI (Anderson-Hauck)"
#> 

a_proportion_diff(
  df = subset(dta, grp == "A"),
  .var = "rsp",
  .ref_group = subset(dta, grp == "B"),
  .in_ref_col = FALSE,
  conf_level = 0.90,
  method = "ha"
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name formatted_cell indent_mod                       row_label
#> 1     diff           25.0          0 Difference in Response rate (%)
#> 2  diff_ci (-92.0, 100.0)          1         90% CI (Anderson-Hauck)

l <- basic_table() %>%
  split_cols_by(var = "grp", ref_group = "B") %>%
  estimate_proportion_diff(
    vars = "rsp",
    conf_level = 0.90,
    method = "ha"
  )

build_table(l, df = dta)
#>                                   B         A       
#> ————————————————————————————————————————————————————
#> Difference in Response rate (%)            25.0     
#>   90% CI (Anderson-Hauck)             (-92.0, 100.0)