Package 'metalite.ae' reference manual

Title:	Adverse Events Analysis Using 'metalite'
Description:	Analyzes adverse events in clinical trials using the 'metalite' data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the adverse events analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.
Authors:	Yilong Zhang [aut], Yujie Zhao [aut, cre], Benjamin Wang [aut], Nan Xiao [aut], Sarad Nepal [aut], Madhusudhan Ginnaram [aut], Venkatesh Burla [ctb], Ruchitbhai Patel [aut], Brian Lang [aut], Xuan Deng [aut], Hiroaki Fukuda [aut], Bing Liu [aut], Jeetender Chauhan [aut], Li Ma [ctb], Merck Sharp & Dohme Corp [cph]
Maintainer:	Yujie Zhao <[email protected]>
License:	GPL-3
Version:	0.1.3
Built:	2024-10-24 04:27:23 UTC
Source:	CRAN

Add average duration information for AE specific analysis

Description

Add average duration information for AE specific analysis

Usage

extend_ae_specific_duration(outdata, duration_var, duration_unit = "Day")
extend_ae_specific_duration(outdata, duration_var, duration_unit = "Day")

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`duration_var`	A character value of variable name for adverse event duration.
`duration_unit`	A character value of adverse event duration unit.

Value

A list of analysis raw datasets.

Examples

meta <- meta_ae_example()
tbl <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_duration(duration_var = "ADURN") |>
  format_ae_specific(display = c("n", "prop", "dur"))
head(tbl$tbl)
meta <- meta_ae_example()
tbl <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_duration(duration_var = "ADURN") |>
  format_ae_specific(display = c("n", "prop", "dur"))
head(tbl$tbl)

Add average number of events information for AE specific analysis

Description

Add average number of events information for AE specific analysis

Usage

extend_ae_specific_events(outdata)
extend_ae_specific_events(outdata)

Arguments

outdata

An outdata object created by prepare_ae_specific().

Value

A list of analysis raw datasets.

Examples

meta <- meta_ae_example()
tbl <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_events() |>
  format_ae_specific(display = c("n", "prop", "events_avg"))
head(tbl$tbl)
meta <- meta_ae_example()
tbl <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_events() |>
  format_ae_specific(display = c("n", "prop", "events_avg"))
head(tbl$tbl)

Add inference information for AE specific analysis

Description

Add inference information for AE specific analysis

Usage

extend_ae_specific_inference(outdata, ..., ci = 0.95)
extend_ae_specific_inference(outdata, ..., ci = 0.95)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`...`	Other options passed on to `rate_compare_sum()`
`ci`	A numeric value for the percentile of confidence interval.

Value

A list of analysis raw datasets.

Examples

meta <- meta_ae_example()
tbl <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_inference(eps = 1e-6, bisection = 200) |>
  format_ae_specific(display = c("n", "prop", "diff", "diff_ci"))
head(tbl$tbl)
meta <- meta_ae_example()
tbl <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_inference(eps = 1e-6, bisection = 200) |>
  format_ae_specific(display = c("n", "prop", "diff", "diff_ci"))
head(tbl$tbl)

Add subgroup analysis in AE specific analysis

Description

Add subgroup analysis in AE specific analysis

Usage

extend_ae_specific_subgroup(outdata, subgroup_var)
extend_ae_specific_subgroup(outdata, subgroup_var)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`subgroup_var`	a character string for subgroup variable name

Value

A list of analysis raw datasets.

Examples

meta <- meta_ae_example()
tbl <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_subgroup(subgroup_var = "SEX")
meta <- meta_ae_example()
tbl <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_subgroup(subgroup_var = "SEX")

Add exposure-adjusted rate information for AE summary analysis

Description

Add exposure-adjusted rate information for AE summary analysis

Usage

extend_ae_summary_eaer(
  outdata,
  duration_var = "TRTDUR",
  adj_unit = c("year", "month", "week", "day")
)
extend_ae_summary_eaer(
  outdata,
  duration_var = "TRTDUR",
  adj_unit = c("year", "month", "week", "day")
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_summary()`.
`duration_var`	A character value of duration variable name. By default, `"TRTDUR"` is used.
`adj_unit`	A character value of exposure adjusted unit. It could be select from `"year"`, `"month"`, `"week"`, and `"day"`.

Value

A list of analysis raw datasets.

Examples

meta <- meta_ae_example()
prepare_ae_summary(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
) |>
  extend_ae_summary_eaer()
meta <- meta_ae_example()
prepare_ae_summary(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
) |>
  extend_ae_summary_eaer()

Format confidence interval

Description

Format confidence interval

Usage

fmt_ci(lower, upper, digits = 2, width = 3 + digits)
fmt_ci(lower, upper, digits = 2, width = 3 + digits)

Arguments

`lower`	A numeric value of lower value of CI.
`upper`	A numeric value of upper value of CI.
`digits`	Digits of each column, i.e., format as (x.x, x.x).
`width`	Width of each column.

Value

A numeric vector with the expected format.

Examples

fmt_ci(0.2356, 0.3871)
fmt_ci(0.2356, 0.3871)

Format model estimator

Description

Formats mean sd/se to a format as x.x or x.x (x.xx) if both mean and sd/sd are defined.

Usage

fmt_est(
  mean,
  sd = rep(NA, length(mean)),
  digits = c(1, 1),
  width = c(4, 3) + digits
)
fmt_est(
  mean,
  sd = rep(NA, length(mean)),
  digits = c(1, 1),
  width = c(4, 3) + digits
)

Arguments

`mean`	A numeric vector of mean value.
`sd`	A numeric vector of standard deviation value.
`digits`	Digits of each column, i.e., format as x.x (x.xx).
`width`	Width of each column.

Details

The function assumes 1 column or 2 columns:

If there is only 1 column, only represent mean.
If there are 2 columns, represent mean (sd) or mean(se). Decimals will understand the number will be formatted as x.x (x.xx).

Value

The same data frame with additional attributes for page features.

Specification

The contents of this section are shown in PDF user manual only.

Examples

fmt_est(mean(iris$Petal.Length), sd(iris$Petal.Length))
fmt_est(mean(iris$Petal.Length), sd(iris$Petal.Length), digits = c(2, 3))
fmt_est(mean(iris$Petal.Length), sd(iris$Petal.Length))
fmt_est(mean(iris$Petal.Length), sd(iris$Petal.Length), digits = c(2, 3))

Format percentage

Description

Format percentage

Usage

fmt_pct(x, digits = 1, pre = "(", post = ")")
fmt_pct(x, digits = 1, pre = "(", post = ")")

Arguments

`x`	A numeric vector.
`digits`	Number of digits.
`pre`	Text before the number.
`post`	Text after the number.

Value

A numeric vector with the expected format.

Examples

fmt_pct(c(1, 1.52, 0.3, 100))
fmt_pct(c(1, 1.52, 0.3, 100))

Format p-value

Description

Format p-value

Usage

fmt_pval(p, digits = 3, width = 3 + digits)
fmt_pval(p, digits = 3, width = 3 + digits)

Arguments

`p`	A numeric vector of p-values.
`digits`	Digits of each column, i.e., format as x.xxx.
`width`	Width of each column.

Value

A numeric vector with the expected format.

Examples

fmt_pval(c(0.1234, 0.00002))
fmt_pval(c(0.1234, 0.00002))

Format exposure-adjusted AE summary

Description

Format exposure-adjusted AE summary

Usage

format_ae_exp_adj(
  outdata,
  display = c("n", "total_exp", "events", "eaer", "total"),
  digits_total_exp = 2,
  digits_eaer = 2,
  mock = FALSE
)
format_ae_exp_adj(
  outdata,
  display = c("n", "total_exp", "events", "eaer", "total"),
  digits_total_exp = 2,
  digits_eaer = 2,
  mock = FALSE
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`display`	A character vector of measurement to be displayed: `n`: Number of subjects exposed. `total_exp`: Total exposure in person-time. `events`: Number of AE. `eaer`: Exposure adjusted event rate. `total`: Total columns.
`digits_total_exp`	A numeric value of number of digits for total exposure value.
`digits_eaer`	A numeric value of number of digits for exposure-adjusted event rate.
`mock`	A boolean value to display mock table.

Value

A list of analysis raw datasets.

Examples

meta <- meta_ae_example()

outdata <- meta |>
  prepare_ae_summary(
    population = "apat",
    observation = "wk12",
    parameter = "any;ser;rel"
  ) |>
  extend_ae_summary_eaer(adj_unit = "month")
tbl <- outdata |>
  format_ae_exp_adj()
head(tbl$tbl)
meta <- meta_ae_example()

outdata <- meta |>
  prepare_ae_summary(
    population = "apat",
    observation = "wk12",
    parameter = "any;ser;rel"
  ) |>
  extend_ae_summary_eaer(adj_unit = "month")
tbl <- outdata |>
  format_ae_exp_adj()
head(tbl$tbl)

Format AE specific analysis

Description

Format AE specific analysis

Usage

format_ae_specific(
  outdata,
  display = c("n", "prop", "total"),
  hide_soc_stats = FALSE,
  digits_prop = 1,
  digits_ci = 1,
  digits_p = 3,
  digits_dur = c(1, 1),
  digits_events = c(1, 1),
  filter_method = c("percent", "count"),
  filter_criteria = 0,
  sort_order = c("alphabetical", "count_des", "count_asc"),
  sort_column = NULL,
  mock = FALSE
)
format_ae_specific(
  outdata,
  display = c("n", "prop", "total"),
  hide_soc_stats = FALSE,
  digits_prop = 1,
  digits_ci = 1,
  digits_p = 3,
  digits_dur = c(1, 1),
  digits_events = c(1, 1),
  filter_method = c("percent", "count"),
  filter_criteria = 0,
  sort_order = c("alphabetical", "count_des", "count_asc"),
  sort_column = NULL,
  mock = FALSE
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`display`	A character vector of measurement to be displayed: `n`: Number of subjects with adverse event. `prop`: Proportion of subjects with adverse event. `total`: Total columns. `diff`: Risk difference. `diff_ci`: 95% confidence interval of risk difference using M&N method. `diff_p`: p-value of risk difference using M&N method. `dur`: Average of adverse event duration. `events_avg`: Average number of adverse event per subject. `events_count`: Count number of adverse event per subject.
`hide_soc_stats`	A boolean value to hide stats for SOC rows.
`digits_prop`	A numeric value of number of digits for proportion value.
`digits_ci`	A numeric value of number of digits for confidence interval.
`digits_p`	A numeric value of number of digits for p-value.
`digits_dur`	A numeric value of number of digits for average duration of adverse event.
`digits_events`	A numeric value of number of digits for average of number of adverse events per subject.
`filter_method`	A character value to specify how to filter rows: `count`: Filtered based on participant count. `percent`: Filtered based percent incidence.
`filter_criteria`	A numeric value to display rows where at least one therapy group has a percent incidence or participant count greater than or equal to the specified value. If `filter_method` is `percent`, the value should be between 0 and 100. If `filter_method` is `count`, the value should be greater than 0.
`sort_order`	A character value to specify sorting order: `alphabetical`: Sort by alphabetical order. `count_des`: Sort by count in descending order. `count_asc`: Sort by count in ascending order.
`sort_column`	A character value of `group` in `outdata` used to sort a table with.
`mock`	A boolean value to display mock table.

Value

A list of analysis raw datasets.

Examples

meta <- meta_ae_example()

outdata <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
)

# Basic example
tbl <- outdata |>
  format_ae_specific()
head(tbl$tbl)

# Filtering
tbl <- outdata |>
  format_ae_specific(
    filter_method = "percent",
    filter_criteria = 10
  )
head(tbl$tbl)

# Display different measurements
tbl <- outdata |>
  extend_ae_specific_events() |>
  format_ae_specific(display = c("n", "prop", "events_count"))
head(tbl$tbl)
meta <- meta_ae_example()

outdata <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
)

# Basic example
tbl <- outdata |>
  format_ae_specific()
head(tbl$tbl)

# Filtering
tbl <- outdata |>
  format_ae_specific(
    filter_method = "percent",
    filter_criteria = 10
  )
head(tbl$tbl)

# Display different measurements
tbl <- outdata |>
  extend_ae_specific_events() |>
  format_ae_specific(display = c("n", "prop", "events_count"))
head(tbl$tbl)

Format AE specific subgroup analysis

Description

Format AE specific subgroup analysis

Usage

format_ae_specific_subgroup(
  outdata,
  display = c("n", "prop"),
  digits_prop = 1,
  digits_ci = 1,
  digits_p = 3,
  digits_dur = c(1, 1),
  digits_events = c(1, 1),
  mock = FALSE
)
format_ae_specific_subgroup(
  outdata,
  display = c("n", "prop"),
  digits_prop = 1,
  digits_ci = 1,
  digits_p = 3,
  digits_dur = c(1, 1),
  digits_events = c(1, 1),
  mock = FALSE
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`display`	A character vector of measurement to be displayed. `n`: Number of subjects with adverse event. `prop`: Proportion of subjects with adverse event. `total`: Total columns. `dur`: Average of adverse event duration. `events`: Average number of adverse event per subject.
`digits_prop`	A numeric value of number of digits for proportion value.
`digits_ci`	A numeric value of number of digits for confidence interval.
`digits_p`	A numeric value of number of digits for p-value.
`digits_dur`	A numeric value of number of digits for average duration of adverse event.
`digits_events`	A numeric value of number of digits for average of number of adverse event per subjects.
`mock`	Logical. Display mock table or not.

Value

A list of analysis raw datasets for subgroup analysis.

Examples

meta <- meta_ae_example()
prepare_ae_specific_subgroup(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel",
  subgroup_var = "SEX",
  display_subgroup_total = TRUE
) |>
  format_ae_specific_subgroup()
meta <- meta_ae_example()
prepare_ae_specific_subgroup(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel",
  subgroup_var = "SEX",
  display_subgroup_total = TRUE
) |>
  format_ae_specific_subgroup()

Format AE summary analysis

Description

Format AE summary analysis

Usage

format_ae_summary(
  outdata,
  display = c("n", "prop", "total"),
  hide_soc_stats = FALSE,
  digits_prop = 1,
  digits_ci = 1,
  digits_p = 3,
  digits_dur = c(1, 1),
  digits_events = c(1, 1),
  filter_method = c("percent", "count"),
  filter_criteria = 0,
  sort_order = c("alphabetical", "count_des", "count_asc"),
  sort_column = NULL,
  mock = FALSE
)
format_ae_summary(
  outdata,
  display = c("n", "prop", "total"),
  hide_soc_stats = FALSE,
  digits_prop = 1,
  digits_ci = 1,
  digits_p = 3,
  digits_dur = c(1, 1),
  digits_events = c(1, 1),
  filter_method = c("percent", "count"),
  filter_criteria = 0,
  sort_order = c("alphabetical", "count_des", "count_asc"),
  sort_column = NULL,
  mock = FALSE
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`display`	A character vector of measurement to be displayed: `n`: Number of subjects with adverse event. `prop`: Proportion of subjects with adverse event. `total`: Total columns. `diff`: Risk difference. `diff_ci`: 95% confidence interval of risk difference using M&N method. `diff_p`: p-value of risk difference using M&N method. `dur`: Average of adverse event duration. `events_avg`: Average number of adverse event per subject. `events_count`: Count number of adverse event per subject.
`hide_soc_stats`	A boolean value to hide stats for SOC rows.
`digits_prop`	A numeric value of number of digits for proportion value.
`digits_ci`	A numeric value of number of digits for confidence interval.
`digits_p`	A numeric value of number of digits for p-value.
`digits_dur`	A numeric value of number of digits for average duration of adverse event.
`digits_events`	A numeric value of number of digits for average of number of adverse events per subject.
`filter_method`	A character value to specify how to filter rows: `count`: Filtered based on participant count. `percent`: Filtered based percent incidence.
`filter_criteria`	A numeric value to display rows where at least one therapy group has a percent incidence or participant count greater than or equal to the specified value. If `filter_method` is `percent`, the value should be between 0 and 100. If `filter_method` is `count`, the value should be greater than 0.
`sort_order`	A character value to specify sorting order: `alphabetical`: Sort by alphabetical order. `count_des`: Sort by count in descending order. `count_asc`: Sort by count in ascending order.
`sort_column`	A character value of `group` in `outdata` used to sort a table with.
`mock`	A boolean value to display mock table.

Value

A list of analysis raw datasets.

Examples

meta <- meta_ae_example()
outdata <- prepare_ae_summary(meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)
tbl <- outdata |>
  format_ae_summary()
head(tbl$tbl)
meta <- meta_ae_example()
outdata <- prepare_ae_summary(meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)
tbl <- outdata |>
  format_ae_summary()
head(tbl$tbl)

Create an example `meta_adam` object

Description

This function is only for illustration purpose. r2rtf is required.

Usage

meta_ae_example()
meta_ae_example()

Value

A metadata object.

Examples

meta <- meta_ae_example()
meta <- meta_ae_example()

ADEX dataset

Description

A dataset containing exposure details.

Usage

metalite_ae_adex
metalite_ae_adex

Format

A data frame with 591 rows and 41 variables.

Value

An analysis data frame.

Source

https://github.com/phuse-org/phuse-scripts/tree/master/data/sdtm/cdiscpilot01

ADEXSUM dataset

Description

A dataset containing exposure details in Basic Data Structure (BDS).

Usage

metalite_ae_adexsum
metalite_ae_adexsum

Format

A data frame with 254 rows and 30 variables.

Value

An analysis data frame.

Source

https://github.com/phuse-org/phuse-scripts/tree/master/data/sdtm/cdiscpilot01

Prepare datasets for AE listing

Description

Prepare datasets for AE listing

Usage

prepare_ae_listing(meta, analysis, population, observation, parameter)
prepare_ae_listing(meta, analysis, population, observation, parameter)

Arguments

`meta`	A metadata object created by metalite.
`analysis`	Analysis name from `meta`.
`population`	A character value of population term name. The term name is used as key to link information.
`observation`	A character value of observation term name. The term name is used as key to link information.
`parameter`	A character value of parameter term name. The term name is used as key to link information.

Value

A list of analysis datasets needed for AE listing.

Examples

meta <- meta_ae_example()
str(prepare_ae_listing(meta, "ae_listing", "apat", "wk12", "ser"))
meta <- meta_ae_example()
str(prepare_ae_listing(meta, "ae_listing", "apat", "wk12", "ser"))

Prepare datasets for AE specific analysis

Description

Prepare datasets for AE specific analysis

Usage

prepare_ae_specific(
  meta,
  population,
  observation,
  parameter,
  components = c("soc", "par"),
  reference_group = NULL
)
prepare_ae_specific(
  meta,
  population,
  observation,
  parameter,
  components = c("soc", "par"),
  reference_group = NULL
)

Arguments

`meta`	A metadata object created by metalite.
`population`	A character value of population term name. The term name is used as key to link information.
`observation`	A character value of observation term name. The term name is used as key to link information.
`parameter`	A character value of parameter term name. The term name is used as key to link information.
`components`	A character vector of components name.
`reference_group`	An integer to indicate reference group. Default is 2 if there are 2 groups, otherwise, the default is 1.

Value

A list of analysis datasets needed for AE specific analysis.

Examples

meta <- meta_ae_example()
str(prepare_ae_specific(meta, "apat", "wk12", "rel"))

# Allow to extract each components
prepare_ae_specific(meta, "apat", "wk12", "rel", components = NULL)$data
prepare_ae_specific(meta, "apat", "wk12", "rel", components = "soc")$data
prepare_ae_specific(meta, "apat", "wk12", "rel", components = "par")$data
meta <- meta_ae_example()
str(prepare_ae_specific(meta, "apat", "wk12", "rel"))

# Allow to extract each components
prepare_ae_specific(meta, "apat", "wk12", "rel", components = NULL)$data
prepare_ae_specific(meta, "apat", "wk12", "rel", components = "soc")$data
prepare_ae_specific(meta, "apat", "wk12", "rel", components = "par")$data

Prepare datasets for AE specific subgroup analysis

Description

Prepare datasets for AE specific subgroup analysis

Usage

prepare_ae_specific_subgroup(
  meta,
  population,
  observation,
  parameter,
  subgroup_var,
  subgroup_header = c(meta$population[[population]]$group, subgroup_var),
  components = c("soc", "par"),
  display_subgroup_total = TRUE
)
prepare_ae_specific_subgroup(
  meta,
  population,
  observation,
  parameter,
  subgroup_var,
  subgroup_header = c(meta$population[[population]]$group, subgroup_var),
  components = c("soc", "par"),
  display_subgroup_total = TRUE
)

Arguments

`meta`	A metadata object created by metalite.
`population`	A character value of population term name. The term name is used as key to link information.
`observation`	A character value of observation term name. The term name is used as key to link information.
`parameter`	A character value of parameter term name. The term name is used as key to link information.
`subgroup_var`	A character value of subgroup variable name in observation data saved in `meta$data_observation`.
`subgroup_header`	A character vector for column header hierarchy. The first element will be the first level header and the second element will be second level header.
`components`	A character vector of components name.
`display_subgroup_total`	Logical. Display total column for subgroup analysis or not.

Value

A list of analysis datasets needed for AE specific subgroup analysis.

Examples

meta <- meta_ae_example()
prepare_ae_specific_subgroup(meta, "apat", "wk12", "rel", subgroup_var = "SEX")$data
meta <- meta_ae_example()
prepare_ae_specific_subgroup(meta, "apat", "wk12", "rel", subgroup_var = "SEX")$data

Prepare datasets for AE summary

Description

Prepare datasets for AE summary

Usage

prepare_ae_summary(meta, population, observation, parameter, ...)
prepare_ae_summary(meta, population, observation, parameter, ...)

Arguments

`meta`	A metadata object created by metalite.
`population`	A character value of population term name. The term name is used as key to link information.
`observation`	A character value of observation term name. The term name is used as key to link information.
`parameter`	A character value of parameter term name. The term name is used as key to link information.
`...`	Additional arguments passed to `prepare_ae_specific()`.

Value

A list of analysis datasets needed for AE summary.

Examples

meta <- meta_ae_example()
prepare_ae_summary(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)
meta <- meta_ae_example()
prepare_ae_summary(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)

Unstratified and stratified Miettinen and Nurminen test

Description

Unstratified and stratified Miettinen and Nurminen test details can be found in vignette("rate-compare").

Usage

rate_compare(
  formula,
  strata,
  data,
  delta = 0,
  weight = c("ss", "equal", "cmh"),
  test = c("one.sided", "two.sided"),
  bisection = 100,
  eps = 1e-06,
  alpha = 0.05
)
rate_compare(
  formula,
  strata,
  data,
  delta = 0,
  weight = c("ss", "equal", "cmh"),
  test = c("one.sided", "two.sided"),
  bisection = 100,
  eps = 1e-06,
  alpha = 0.05
)

Arguments

`formula`	A symbolic description of the model to be fitted, which has the form `y ~ x`. Here, `y` is the numeric vector with values of 0 or 1. `x` is the group information.
`strata`	An optional vector of weights to be used in the analysis. If not specified, unstratified MN analysis is used. If specified, stratified MN analysis is conducted.
`data`	An optional data frame, list, or environment containing the variables in the model. If not found in data, the variables are taken from `environment (formula)`, typically the environment from which `rate_compare` is called.
`delta`	A numeric value to set the difference of two group under the null.
`weight`	Weighting schema used in stratified MN method. Default is `"ss"`: `"equal"` for equal weighting. `"ss"` for sample size weighting. `"cmh"` for Cochran–Mantel–Haenszel's weights.
`test`	A character string specifying the side of p-value, must be one of `"one.sided"`, or `"two.sided"`.
`bisection`	The number of sections in the interval used in bisection method. Default is 100.
`eps`	The level of precision. Default is 1e-06.
`alpha`	Pre-defined alpha level for two-sided confidence interval.

Value

A data frame with the test results.

References

Miettinen, O. and Nurminen, M, Comparative Analysis of Two Rates. Statistics in Medicine, 4(2):213–226, 1985.

Examples

# Conduct the stratified MN analysis with sample size weights
treatment <- c(rep("pbo", 100), rep("exp", 100))
response <- c(rep(0, 80), rep(1, 20), rep(0, 40), rep(1, 60))
stratum <- c(rep(1:4, 12), 1, 3, 3, 1, rep(1:4, 12), rep(1:4, 25))
rate_compare(
  response ~ factor(treatment, levels = c("pbo", "exp")),
  strata = stratum,
  delta = 0,
  weight = "ss",
  test = "one.sided",
  alpha = 0.05
)
# Conduct the stratified MN analysis with sample size weights
treatment <- c(rep("pbo", 100), rep("exp", 100))
response <- c(rep(0, 80), rep(1, 20), rep(0, 40), rep(1, 60))
stratum <- c(rep(1:4, 12), 1, 3, 3, 1, rep(1:4, 12), rep(1:4, 25))
rate_compare(
  response ~ factor(treatment, levels = c("pbo", "exp")),
  strata = stratum,
  delta = 0,
  weight = "ss",
  test = "one.sided",
  alpha = 0.05
)

Unstratified and stratified Miettinen and Nurminen test in aggregate data level

Description

Unstratified and stratified Miettinen and Nurminen test in aggregate data level

Usage

rate_compare_sum(
  n0,
  n1,
  x0,
  x1,
  strata = NULL,
  delta = 0,
  weight = c("ss", "equal", "cmh"),
  test = c("one.sided", "two.sided"),
  bisection = 100,
  eps = 1e-06,
  alpha = 0.05
)
rate_compare_sum(
  n0,
  n1,
  x0,
  x1,
  strata = NULL,
  delta = 0,
  weight = c("ss", "equal", "cmh"),
  test = c("one.sided", "two.sided"),
  bisection = 100,
  eps = 1e-06,
  alpha = 0.05
)

Arguments

`n0`, `n1`	The sample size in the control group and experimental group, separately. The length should be the same as the length for `x0/x1` and `strata`.
`x0`, `x1`	The number of events in the control group and experimental group, separately. The length should be the same as the length for `n0/n1` and `strata`.
`strata`	A vector of stratum indication to be used in the analysis. If `NULL` or the length of unique values of `strata` equals to 1, it is unstratified MN analysis. Otherwise, it is stratified MN analysis. The length of `strata` should be the same as the length for `x0/x1` and `n0/n1`.
`delta`	A numeric value to set the difference of two groups under the null.
`weight`	Weighting schema used in stratified MN method. Default is `"ss"`: `"equal"` for equal weighting. `"ss"` for sample size weighting. `"cmh"` for Cochran-Mantel-Haenszel's weights.
`test`	A character string specifying the side of p-value, must be one of `"one.sided"`, or `"two.sided"`.
`bisection`	The number of sections in the interval used in bisection method. Default is 100.
`eps`	The level of precision. Default is 1e-06.
`alpha`	Pre-defined alpha level for two-sided confidence interval.

Value

A data frame with the test results.

References

Miettinen, O. and Nurminen, M, Comparative Analysis of Two Rates. Statistics in Medicine, 4(2):213–226, 1985.

Examples

# Conduct the stratified MN analysis with sample size weights
treatment <- c(rep("pbo", 100), rep("exp", 100))
response <- c(rep(0, 80), rep(1, 20), rep(0, 40), rep(1, 60))
stratum <- c(rep(1:4, 12), 1, 3, 3, 1, rep(1:4, 12), rep(1:4, 25))
n0 <- sapply(split(treatment[treatment == "pbo"], stratum[treatment == "pbo"]), length)
n1 <- sapply(split(treatment[treatment == "exp"], stratum[treatment == "exp"]), length)
x0 <- sapply(split(response[treatment == "pbo"], stratum[treatment == "pbo"]), sum)
x1 <- sapply(split(response[treatment == "exp"], stratum[treatment == "exp"]), sum)
strata <- c("a", "b", "c", "d")
rate_compare_sum(
  n0, n1, x0, x1,
  strata,
  delta = 0,
  weight = "ss",
  test = "one.sided",
  alpha = 0.05
)
# Conduct the stratified MN analysis with sample size weights
treatment <- c(rep("pbo", 100), rep("exp", 100))
response <- c(rep(0, 80), rep(1, 20), rep(0, 40), rep(1, 60))
stratum <- c(rep(1:4, 12), 1, 3, 3, 1, rep(1:4, 12), rep(1:4, 25))
n0 <- sapply(split(treatment[treatment == "pbo"], stratum[treatment == "pbo"]), length)
n1 <- sapply(split(treatment[treatment == "exp"], stratum[treatment == "exp"]), length)
x0 <- sapply(split(response[treatment == "pbo"], stratum[treatment == "pbo"]), sum)
x1 <- sapply(split(response[treatment == "exp"], stratum[treatment == "exp"]), sum)
strata <- c("a", "b", "c", "d")
rate_compare_sum(
  n0, n1, x0, x1,
  strata,
  delta = 0,
  weight = "ss",
  test = "one.sided",
  alpha = 0.05
)

Exposure-adjusted AE summary table

Description

Exposure-adjusted AE summary table

Usage

tlf_ae_exp_adj(
  outdata,
  source,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "portrait",
  title = c("analysis", "observation", "population"),
  footnotes = NULL,
  path_outdata = NULL,
  path_outtable = NULL
)
tlf_ae_exp_adj(
  outdata,
  source,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "portrait",
  title = c("analysis", "observation", "population"),
  footnotes = NULL,
  path_outdata = NULL,
  path_outtable = NULL
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`source`	A character value of the data source.
`col_rel_width`	Column relative width in a vector e.g. c(2,1,1) refers to 2:1:1. Default is NULL for equal column width.
`text_font_size`	Text font size. To vary text font size by column, use numeric vector with length of vector equal to number of columns displayed e.g. c(9,20,40).
`orientation`	Orientation in 'portrait' or 'landscape'.
`title`	Term "analysis", "observation"and "population") for collecting title from metadata or a character vector of table titles.
`footnotes`	A character vector of table footnotes.
`path_outdata`	A character string of the outdata path.
`path_outtable`	A character string of the outtable path.

Value

RTF file and source dataset for exposure-adjusted AE summary table.

Examples

meta <- meta_ae_example()
outdata <- meta |>
  prepare_ae_summary(
    population = "apat",
    observation = "wk12",
    parameter = "any;rel;ser"
  ) |>
  extend_ae_summary_eaer(adj_unit = "month")
outdata |>
  format_ae_exp_adj() |>
  tlf_ae_exp_adj(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )
meta <- meta_ae_example()
outdata <- meta |>
  prepare_ae_summary(
    population = "apat",
    observation = "wk12",
    parameter = "any;rel;ser"
  ) |>
  extend_ae_summary_eaer(adj_unit = "month")
outdata |>
  format_ae_exp_adj() |>
  tlf_ae_exp_adj(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )

Generate AE listing

Description

Generate AE listing

Usage

tlf_ae_listing(
  outdata,
  footnotes = NULL,
  source = NULL,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "landscape",
  path_outdata = NULL,
  path_outtable = NULL
)
tlf_ae_listing(
  outdata,
  footnotes = NULL,
  source = NULL,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "landscape",
  path_outdata = NULL,
  path_outtable = NULL
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_listing()`.
`footnotes`	A character vector of table footnotes.
`source`	A character value of the data source.
`col_rel_width`	Column relative width in a vector e.g. c(2,1,1) refers to 2:1:1. Default is NULL for equal column width.
`text_font_size`	Text font size. To vary text font size by column, use numeric vector with length of vector equal to number of columns displayed e.g. c(9,20,40).
`orientation`	Orientation in 'portrait' or 'landscape'.
`path_outdata`	A character string of the outdata path.
`path_outtable`	A character string of the outtable path.

Value

RTF file and the source dataset for AE listing.

Examples

library(r2rtf)
library(metalite)

meta <- meta_ae_example()
prepare_ae_listing(meta, "ae_listing", "apat", "wk12", "ser") |>
  tlf_ae_listing(
    footnotes = "footnote1",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )
library(r2rtf)
library(metalite)

meta <- meta_ae_example()
prepare_ae_listing(meta, "ae_listing", "apat", "wk12", "ser") |>
  tlf_ae_listing(
    footnotes = "footnote1",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )

Specific adverse events table

Description

Specific adverse events table

Usage

tlf_ae_specific(
  outdata,
  meddra_version,
  source,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "portrait",
  footnotes = NULL,
  title = c("analysis", "observation", "population"),
  path_outdata = NULL,
  path_outtable = NULL
)
tlf_ae_specific(
  outdata,
  meddra_version,
  source,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "portrait",
  footnotes = NULL,
  title = c("analysis", "observation", "population"),
  path_outdata = NULL,
  path_outtable = NULL
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`meddra_version`	A character value of the MedDRA version for this dataset.
`source`	A character value of the data source.
`col_rel_width`	Column relative width in a vector e.g. c(2,1,1) refers to 2:1:1. Default is NULL for equal column width.
`text_font_size`	Text font size. To vary text font size by column, use numeric vector with length of vector equal to number of columns displayed e.g. c(9,20,40).
`orientation`	Orientation in 'portrait' or 'landscape'.
`footnotes`	A character vector of table footnotes.
`title`	Term "analysis", "observation"and "population") for collecting title from metadata or a character vector of table titles.
`path_outdata`	A character string of the outdata path.
`path_outtable`	A character string of the outtable path.

Value

RTF file and the source dataset for AE specific table.

Examples

meta <- meta_ae_example()

meta |>
  prepare_ae_specific(
    population = "apat",
    observation = "wk12",
    parameter = "rel"
  ) |>
  format_ae_specific() |>
  tlf_ae_specific(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    meddra_version = "24.0",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )
meta <- meta_ae_example()

meta |>
  prepare_ae_specific(
    population = "apat",
    observation = "wk12",
    parameter = "rel"
  ) |>
  format_ae_specific() |>
  tlf_ae_specific(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    meddra_version = "24.0",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )

Specific adverse events table for subgroup analysis

Description

Specific adverse events table for subgroup analysis

Usage

tlf_ae_specific_subgroup(
  outdata,
  meddra_version,
  source,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "landscape",
  footnotes = NULL,
  title = NULL,
  path_outdata = NULL,
  path_outtable = NULL
)
tlf_ae_specific_subgroup(
  outdata,
  meddra_version,
  source,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "landscape",
  footnotes = NULL,
  title = NULL,
  path_outdata = NULL,
  path_outtable = NULL
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`meddra_version`	A character value of the MedDRA version for this dataset.
`source`	A character value of the data source.
`col_rel_width`	Column relative width in a vector e.g. c(2,1,1) refers to 2:1:1. Default is NULL for equal column width.
`text_font_size`	Text font size. To vary text font size by column, use numeric vector with length of vector equal to number of columns displayed e.g. c(9,20,40).
`orientation`	Orientation in 'portrait' or 'landscape'.
`footnotes`	A character vector of table footnotes.
`title`	Term "analysis", "observation"and "population") for collecting title from metadata or a character vector of table titles.
`path_outdata`	A character string of the outdata path.
`path_outtable`	A character string of the outtable path.

Value

RTF file and the source dataset for AE specific subgroup analysis table.

Examples

meta <- meta_ae_example()
prepare_ae_specific_subgroup(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel",
  subgroup_var = "SEX",
  display_subgroup_total = TRUE
) |>
  format_ae_specific_subgroup() |>
  tlf_ae_specific_subgroup(
    meddra_version = "24.0",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outtable = tempfile(fileext = ".rtf")
  )
meta <- meta_ae_example()
prepare_ae_specific_subgroup(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel",
  subgroup_var = "SEX",
  display_subgroup_total = TRUE
) |>
  format_ae_specific_subgroup() |>
  tlf_ae_specific_subgroup(
    meddra_version = "24.0",
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outtable = tempfile(fileext = ".rtf")
  )

AE summary table

Description

AE summary table

Usage

tlf_ae_summary(
  outdata,
  source,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "portrait",
  title = c("analysis", "observation", "population"),
  footnotes = NULL,
  path_outdata = NULL,
  path_outtable = NULL
)
tlf_ae_summary(
  outdata,
  source,
  col_rel_width = NULL,
  text_font_size = 9,
  orientation = "portrait",
  title = c("analysis", "observation", "population"),
  footnotes = NULL,
  path_outdata = NULL,
  path_outtable = NULL
)

Arguments

`outdata`	An `outdata` object created by `prepare_ae_specific()`.
`source`	A character value of the data source.
`col_rel_width`	Column relative width in a vector e.g. c(2,1,1) refers to 2:1:1. Default is NULL for equal column width.
`text_font_size`	Text font size. To vary text font size by column, use numeric vector with length of vector equal to number of columns displayed e.g. c(9,20,40).
`orientation`	Orientation in 'portrait' or 'landscape'.
`title`	Term "analysis", "observation"and "population") for collecting title from metadata or a character vector of table titles.
`footnotes`	A character vector of table footnotes.
`path_outdata`	A character string of the outdata path.
`path_outtable`	A character string of the outtable path.

Value

RTF file and the source dataset for AE summary table.

Examples

meta <- meta_ae_example()
outdata <- prepare_ae_summary(meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)
outdata |>
  format_ae_summary() |>
  tlf_ae_summary(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )
meta <- meta_ae_example()
outdata <- prepare_ae_summary(meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)
outdata |>
  format_ae_summary() |>
  tlf_ae_summary(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    path_outdata = tempfile(fileext = ".Rdata"),
    path_outtable = tempfile(fileext = ".rtf")
  )

Package 'metalite.ae'

Help Index

Add average duration information for AE specific analysis

Description

Usage

Arguments

Value

Examples

Add average number of events information for AE specific analysis

Description

Usage

Arguments

Value

Examples

Add inference information for AE specific analysis

Description

Usage

Arguments

Value

Examples

Add subgroup analysis in AE specific analysis

Description

Usage

Arguments

Value

Examples

Add exposure-adjusted rate information for AE summary analysis

Description

Usage

Arguments

Value

Examples

Format confidence interval

Description

Usage

Arguments

Value

Examples

Format model estimator

Description

Usage

Arguments

Details

Value

Specification

Examples

Format percentage

Description

Usage

Arguments

Value

Examples

Format p-value

Description

Usage

Arguments

Value

Examples

Format exposure-adjusted AE summary

Description

Usage

Arguments

Value

Examples

Format AE specific analysis

Description

Usage

Arguments

Value

Examples

Format AE specific subgroup analysis

Description

Usage

Arguments

Value

Examples

Format AE summary analysis

Description

Usage

Arguments

Create an example `meta_adam` object