Help for package simaerep

Title:

Find Clinical Trial Sites Under-Reporting Adverse Events

Version:

0.7.0

Description:

Monitoring of Adverse Event (AE) reporting in clinical trials is important for patient safety. Sites that are under-reporting AEs can be detected using Bootstrap-based simulations that simulate overall AE reporting. Based on the simulation an AE under-reporting probability is assigned to each site in a given trial (Koneswarakantha 2021 <doi:10.1007/s40264-020-01011-5>).

URL:

https://openpharma.github.io/simaerep/, https://github.com/openpharma/simaerep/

License:

MIT + file LICENSE

Encoding:

UTF-8

Depends:

R (≥ 4.0), ggplot2

Imports:

dplyr (≥ 1.1.0), tidyr (≥ 1.1.0), magrittr, purrr, rlang, stringr, forcats, cowplot, RColorBrewer, furrr (≥ 0.2.1), progressr, knitr, tibble, dbplyr

Suggests:

testthat, devtools, pkgdown, spelling, haven, vdiffr, lintr, DBI, duckdb, ggExtra

RoxygenNote:

7.3.2

Language:

en-US

Config/testthat/edition:

NeedsCompilation:

Packaged:

2025-04-09 09:11:02 UTC; koneswab

Author:

Bjoern Koneswarakantha

[aut, cre, cph], F. Hoffmann-La Roche Ltd [cph]

Maintainer:

Bjoern Koneswarakantha <bjoern.koneswarakantha@roche.com>

Repository:

CRAN

Date/Publication:

2025-04-09 09:40:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Value

returns output of rhs function

Aggregate duplicated visits.

Description

Internal function called by check_df_visit().

Usage

aggr_duplicated_visits(df_visit, event_names = "ae")

Arguments

df_visit

dataframe with columns: study_id, site_number, patnum, visit, n_ae

event_names

vector, contains the event names, default = "ae"

Value

df_visit corrected

Integrity check for df_visit.

Description

Internal function used by all functions that accept df_visit as a parameter. Checks for NA columns, numeric visits and AEs, implicitly missing and duplicated visits.

Usage

check_df_visit(df_visit, event_names = "ae")

Arguments

df_visit

dataframe with columns: study_id, site_number, patnum, visit, n_ae

event_names

vector, contains the event names, default = "ae"

Value

corrected df_visit

Examples


df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  frac_site_with_ur = 0.4,
  ur_rate = 0.6
)

df_visit$study_id <- "A"

df_visit_filt <- df_visit %>%
  dplyr::filter(visit != 3)

df_visit_corr <- check_df_visit(df_visit_filt)
3 %in% df_visit_corr$visit
nrow(df_visit_corr) == nrow(df_visit)

df_visit_corr <- check_df_visit(dplyr::bind_rows(df_visit, df_visit))
nrow(df_visit_corr) == nrow(df_visit)

Evaluate sites.

Description

Correct under-reporting probabilities using p.adjust.

Usage

eval_sites(
  df_sim_sites,
  method = "BH",
  under_only = TRUE,
  event_names = c("ae"),
  ...
)

Arguments

df_sim_sites

dataframe generated by sim_sites

method

character, passed to stats::p.adjust(), if NULL

under_only

compute under-reporting probabilities only, default = TRUE check_df_visit(), computationally expensive on large data sets. Default: TRUE

event_names

vector, contains the event names, default = "ae" eval_sites_deprecated() is used instead, Default = "BH"

...

use to pass r_sim_sites parameter to eval_sites_deprecated()

Value

dataframe with the following columns:

study_id: study identification
site_number: site identification
visit_med75: median(max(visit)) * 0.75
mean_ae_site_med75: mean AE at visit_med75 site level
mean_ae_study_med75: mean AE at visit_med75 study level
pval: p-value as returned by poisson.test
prob_low: bootstrapped probability for having mean_ae_site_med75 or lower
pval_adj: adjusted p-values
prob_low_adj: adjusted bootstrapped probability for having mean_ae_site_med75 or lower
pval_prob_ur: probability under-reporting as 1 - pval_adj, poisson.test (use as benchmark)
prob_low_prob_ur: probability under-reporting as 1 - prob_low_adj, bootstrapped (use)

Examples

df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5,
    frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit$study_id <- "A"
df_site <- site_aggr(df_visit)

df_sim_sites <- sim_sites(df_site, df_visit, r = 100)

df_eval <- eval_sites(df_sim_sites)
df_eval

Expose implicitly missing visits.

Description

Internal function called by check_df_visit().

Usage

exp_implicit_missing_visits(df_visit, event_names = "ae")

Arguments

df_visit

dataframe with columns: study_id, site_number, patnum, visit, n_ae

event_names

vector, contains the event names, default = "ae"

Value

df_visit corrected

Get Portfolio Configuration

Description

Get Portfolio configuration from a dataframe aggregated on patient level with max_ae and max_visit. Will filter studies with only a few sites and patients and will anonymize IDs. Portfolio configuration can be used by sim_test_data_portfolio to generate data for an artificial portfolio.

Usage

get_config(
  df_site,
  min_pat_per_study = 100,
  min_sites_per_study = 10,
  anonymize = TRUE,
  pad_width = 4
)

Arguments

df_site

dataframe aggregated on patient level with max_ae and max_visit

min_pat_per_study

minimum number of patients per study, Default: 100

min_sites_per_study

minimum number of sites per study, Default: 10

anonymize

logical, Default: TRUE

pad_width

padding width for newly created IDs, Default: 4

Value

dataframe with the following columns:

study_id: study identification
ae_per_visit_mean: mean AE per visit per study
site_number: site
max_visit_sd: standard deviation of maximum patient visits per site
max_visit_mean: mean of maximum patient visits per site
n_pat: number of patients

Examples


df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit1$study_id <- "A"

df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.2, ur_rate = 0.1)

df_visit2$study_id <- "B"

df_visit <- dplyr::bind_rows(df_visit1, df_visit2)

df_site_max <- df_visit %>%
  dplyr::group_by(study_id, site_number, patnum) %>%
  dplyr::summarise(max_visit = max(visit),
            max_ae = max(n_ae),
            .groups = "drop")

df_config <- get_config(df_site_max)

df_config

df_portf <- sim_test_data_portfolio(df_config)

df_portf

df_scen <- sim_ur_scenarios(df_portf,
                            extra_ur_sites = 2,
                            ur_rate = c(0.5, 1))


df_scen

df_perf <- get_portf_perf(df_scen)

df_perf

Get empirical cumulative distribution values of pval or prob_lower

Description

Test function, test applicability of poisson test, by calculating

the bootstrapped probability of obtaining a specific p-value or lower, use in combination with sim_studies().

Usage

get_ecd_values(df_sim_studies, df_sim_sites, val_str)

Arguments

df_sim_studies

dataframe, generated by sim_studies()

df_sim_sites

dataframe, generated by sim_sites()

val_str

c("prob_low","pval")

Details

trains a ecdf function for each studies based on the results of sim_studies()

Value

dataframe with the following columns:

study_id: study identification
site_number: site identification
visit_med75: median(max(visit)) * 0.75
mean_ae_site_med75: mean AE at visit_med75 site level
mean_ae_study_med75: mean AE at visit_med75 study level
pval/prob_low: p-value as returned by poisson.test
pval/prob_low_ecd: p-value as returned by poisson.test

Examples

df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5,
    frac_site_with_ur = 0.4, ur_rate = 0.3)

df_visit$study_id <- "A"
df_site <- site_aggr(df_visit)

df_sim_sites <- sim_sites(df_site, df_visit, r = 100)

df_sim_studies <- sim_studies(
  df_site = df_site,
  df_visit = df_visit,
  r = 3,
  parallel = FALSE,
  poisson_test = TRUE,
  prob_lower = TRUE
)

get_ecd_values(df_sim_studies, df_sim_sites, "prob_low")
get_ecd_values(df_sim_studies, df_sim_sites, "pval")

replace cowplot::get_legend, to silence warning Multiple components found; returning the first one. To return all, use 'return_all = TRUE

Description

replace cowplot::get_legend, to silence warning Multiple components found; returning the first one. To return all, use 'return_all = TRUE

Usage

get_legend(p)

Configure study patient pool by site parameters.

Description

Internal Function used by sim_sites()

Usage

get_pat_pool_config(df_visit, df_site, min_n_pat_with_med75 = 1)

Arguments

df_visit

dataframe

df_site

dataframe as created by site_aggr()

min_n_pat_with_med75

minimum number of patients with visit_med_75 for simulation, Default: 1

Details

For simulating a study we need to configure the study patient pool to match the configuration of the sites

Value

dataframe

Examples

df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 5,
                                      frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit1$study_id <- "A"

df_visit2 <- sim_test_data_study(n_pat = 1000, n_sites = 3,
                                      frac_site_with_ur = 0.2, ur_rate = 0.1)

df_visit2$study_id <- "B"

df_visit <- dplyr::bind_rows(df_visit1, df_visit2)

df_site <- site_aggr(df_visit)

df_config <- get_pat_pool_config(df_visit, df_site)

df_config

Get Portfolio Performance

Description

Performance as true positive rate (tpr as tp/P) on the basis of desired false positive rates (fpr as fp/P).

Usage

get_portf_perf(df_scen, stat = "prob_low_prob_ur", fpr = c(0.001, 0.01, 0.05))

Arguments

df_scen

dataframe as returned by sim_ur_scenarios

stat

character denoting the column name of the under-reporting statistic, Default: 'prob_low_prob_ur'

fpr

numeric vector specifying false positive rates, Default: c(0.001, 0.01, 0.05)

Details

DETAILS

Value

dataframe

Examples


df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit1$study_id <- "A"

df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.2, ur_rate = 0.1)

df_visit2$study_id <- "B"

df_visit <- dplyr::bind_rows(df_visit1, df_visit2)

df_site_max <- df_visit %>%
  dplyr::group_by(study_id, site_number, patnum) %>%
  dplyr::summarise(max_visit = max(visit),
                   max_ae = max(n_ae),
                   .groups = "drop")

df_config <- get_config(df_site_max)

df_config

df_portf <- sim_test_data_portfolio(df_config)

df_portf

df_scen <- sim_ur_scenarios(df_portf,
                            extra_ur_sites = 2,
                            ur_rate = c(0.5, 1))


df_scen

df_perf <- get_portf_perf(df_scen)

df_perf

Get site mean ae development.

Description

Internal function used by site_aggr(), plot_visit_med75(), returns mean AE development from visit 0 to visit_med75.

Usage

get_site_mean_ae_dev(df_visit, df_pat, df_site, event_names = c("ae"))

Arguments

df_visit

dataframe

df_pat

dataframe as returned by pat_aggr()

df_site

dataframe as returned by site_aggr()

event_names

vector, contains the event names, default = "ae"

Value

dataframe

Get visit_med75.

Description

Internal function used by site_aggr().

Usage

get_visit_med75(df_pat, method = "med75_adj", min_pat_pool = 0.2)

Arguments

df_pat

dataframe as returned by pat_aggr()

method

character, one of c("med75", "med75_adj") defining method for defining evaluation point visit_med75 (see details), Default: "med75_adj"

min_pat_pool

double, minimum ratio of available patients available for sampling. Determines maximum visit_med75 value see Details. Default: 0.2

Value

dataframe

is orivisit class

Description

internal function

Usage

is_orivisit(x)

Arguments

x

object

Value

logical

is simaerep class

Description

internal function

Usage

is_simaerep(x)

Arguments

x

object

Value

logical

Calculate Max Rank

Description

like rank() with ties.method = "max", works on tbl objects

Usage

max_rank(df, col, col_new)

Arguments

df

dataframe

col

character column name to rank y

col_new

character column name for rankings

Details

this is needed for hochberg p value adjustment. We need to assign higher rank when multiple sites have same p value

Examples


df <- tibble::tibble(s = c(1, 2, 2, 2, 5, 10)) %>%
 dplyr::mutate(
   rank = rank(s, ties.method = "max")
 )

df %>%
 max_rank("s", "max_rank")

# Database
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = ":memory:")

dplyr::copy_to(con, df, "df")
max_rank(dplyr::tbl(con, "df"), "s", "max_rank")

DBI::dbDisconnect(con)

create orivisit object

Description

Internal S3 object, stores lazy reference to original visit data.

Usage

orivisit(df_visit, call = NULL, env = parent.frame(), event_names = c("ae"))

Arguments

df_visit

dataframe with original visit data

call

optional, provide call, Default: NULL

env

optional, provide environment of original visit data, Default: parent.frame()

event_names

vector, contains the event names, default = "ae"

Details

Saves variable name of original visit data, checks whether it can be retrieved from parent environment and stores summary. Original data can be retrieved using as.data.frame(x).

Value

orivisit object

Examples


df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  frac_site_with_ur = 0.4,
  ur_rate = 0.6
)

df_visit$study_id <- "A"

visit <- orivisit(df_visit)

object.size(df_visit)
object.size(visit)

as.data.frame(visit)

benjamini hochberg p value correction using table operations

Description

benjamini hochberg p value correction using table operations

Usage

p_adjust_bh_inframe(df_eval, cols, suffix)

Aggregate visit to patient level.

Description

Internal function used by site_aggr() and plot_visit_med75(), adds the maximum visit for each patient.

Usage

pat_aggr(df_visit)

Arguments

df_visit

dataframe

Value

dataframe

Create a study specific patient pool for sampling

Description

Internal function for sim_sites, filter all visits greater than max_visit_med75_study returns dataframe with one column for studies and one column with nested patient data.

Usage

pat_pool(df_visit, df_site)

Arguments

df_visit

dataframe, created by sim_sites

df_site

dataframe created by site_aggr

Value

dataframe with nested pat_pool column

Examples

df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  frac_site_with_ur = 0.4,
  ur_rate = 0.6
)

df_visit$study_id <- "A"

df_site <- site_aggr(df_visit)

df_pat_pool <- pat_pool(df_visit, df_site)

df_pat_pool

plot AE under-reporting simulation results

Description

generic plot function for simaerep objects

Usage

## S3 method for class 'simaerep'
plot(
  x,
  ...,
  study = NULL,
  what = "ur",
  n_sites = 16,
  df_visit = NULL,
  env = parent.frame(),
  plot_event = "ae"
)

Arguments

x

simaerep object

...

additional parameters passed to plot_study() or plot_visit_med75()

study

character specifying study to be plotted, Default: NULL

what

one of c("ur", "med75"), specifying whether to plot site AE under-reporting or visit_med75 values, Default: 'ur'

n_sites

number of sites to plot, Default: 16

df_visit

optional, pass original visit data if it cannot be retrieved from parent environment, Default: NULL

env

optional, pass environment from which to retrieve original visit data, Default: parent.frame()

plot_event

vector containing the events that should be plotted, default = "ae"

Details

see plot_study() and plot_visit_med75()

Value

ggplot object

Examples


df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  frac_site_with_ur = 0.4,
  ur_rate = 0.6
)

df_visit$study_id <- "A"

aerep <- simaerep(df_visit)

plot(aerep, what = "ur", study = "A")
plot(aerep, what = "med75", study = "A")

Plots AE per site as dots.

Description

This plot is meant to supplement the package documentation.

Usage

plot_dots(
  df,
  nrow = 10,
  ncols = 10,
  col_group = "site",
  thresh = NULL,
  color_site_a = "#BDBDBD",
  color_site_b = "#757575",
  color_site_c = "gold3",
  color_high = "#00695C",
  color_low = "#25A69A",
  size_dots = 10
)

Arguments

df

dataframe, cols = c('site', 'patients', 'n_ae')

nrow

integer, number of rows, Default: 10

ncols

integer, number of columns, Default: 10

col_group

character, grouping column, Default: 'site'

thresh

numeric, threshold to determine color of mean_ae annotation, Default: NULL

color_site_a

character, hex color value, Default: '#BDBDBD'

color_site_b

character, hex color value, Default: '#757575'

color_site_c

character, hex color value, Default: 'gold3'

color_high

character, hex color value, Default: '#00695C'

color_low

character, hex color value, Default: '#25A69A'

size_dots

integer, Default: 10

Value

ggplot object

Examples

study <- tibble::tibble(
  site = LETTERS[1:3],
  patients = c(list(seq(1, 50, 1)), list(seq(1, 40, 1)), list(seq(1, 10, 1)))
) %>%
  tidyr::unnest(patients) %>%
  dplyr::mutate(n_ae = as.integer(runif(min = 0, max = 10, n = nrow(.))))

plot_dots(study)

Plot simulation example.

Description

This plots supplements the package documentation.

Usage

plot_sim_example(
  substract_ae_per_pat = 0,
  size_dots = 10,
  size_raster_label = 12,
  color_site_a = "#BDBDBD",
  color_site_b = "#757575",
  color_site_c = "gold3",
  color_high = "#00695C",
  color_low = "#25A69A",
  title = TRUE,
  legend = TRUE,
  seed = 5
)

Arguments

substract_ae_per_pat

integer, subtract aes from patients at site C, Default: 0

size_dots

integer, Default: 10

size_raster_label

integer, Default: 12

color_site_a

character, hex color value, Default: '#BDBDBD'

color_site_b

character, hex color value, Default: '#757575'

color_site_c

character, hex color value, Default: 'gold3'

color_high

character, hex color value, Default: '#00695C'

color_low

character, hex color value, Default: '#25A69A'

title

logical, include title, Default: T

legend

logical, include legend, Default: T

seed

pass seed for simulations Default: 5

Details

uses plot_dots() and adds 2 simulation panels, uses made-up site config with three sites A,B,C simulating site C

Value

ggplot

Examples


plot_sim_example(size_dots = 5)

Plot multiple simulation examples.

Description

This plot is meant to supplement the package documentation.

Usage

plot_sim_examples(substract_ae_per_pat = c(0, 1, 3), ...)

Arguments

substract_ae_per_pat

integer, Default: c(0, 1, 3)

...

parameters passed to plot_sim_example()

Details

This function is a wrapper for plot_sim_example()

Value

ggplot

Examples


plot_sim_examples(size_dot = 3, size_raster_label = 10)
plot_sim_examples()

Plot ae development of study and sites highlighting at risk sites.

Description

Most suitable visual representation of the AE under-reporting statistics.

Usage

plot_study(
  df_visit,
  df_site,
  df_eval,
  study,
  df_al = NULL,
  n_sites = 16,
  pval = FALSE,
  prob_col = "prob_low_prob_ur",
  event_names = c("ae"),
  plot_event = "ae"
)

Arguments

df_visit

dataframe, created by sim_sites()

df_site

dataframe created by site_aggr()

df_eval

dataframe created by eval_sites()

study

study

df_al

dataframe containing study_id, site_number, alert_level_site, alert_level_study (optional), Default: NA

n_sites

integer number of most at risk sites, Default: 16

pval

logical show p-value, Default:FALSE

prob_col

character, denotes probability column, Default: "prob_low_prob_ur"

event_names

vector, contains the event names, default = "ae"

plot_event

vector containing the events that should be plotted, default = "ae"

Details

Left panel shows mean AE reporting per site (lightblue and darkblue lines) against mean AE reporting of the entire study (golden line). Single sites are plotted in descending order by AE under-reporting probability on the right panel in which grey lines denote cumulative AE count of single patients. Grey dots in the left panel plot indicate sites that were picked for single plotting. AE under-reporting probability of dark blue lines crossed threshold of 95%. Numbers in the upper left corner indicate the ratio of patients that have been used for the analysis against the total number of patients. Patients that have not been on the study long enough to reach the evaluation point (visit_med75) will be ignored.

Value

ggplot

Examples


df_visit <- sim_test_data_study(n_pat = 1000, n_sites = 10,
    frac_site_with_ur = 0.2, ur_rate = 0.15, max_visit_sd = 8)

df_visit$study_id <- "A"
df_site <- site_aggr(df_visit)

df_sim_sites <- sim_sites(df_site, df_visit, r = 100)

df_eval <- eval_sites(df_sim_sites)

plot_study(df_visit, df_site, df_eval, study = "A")

Plot patient visits against visit_med75.

Description

Plots cumulative AEs against visits for patients at sites of given study and compares against visit_med75.

Usage

plot_visit_med75(
  df_visit,
  df_site = NULL,
  study_id_str,
  n_sites = 6,
  min_pat_pool = 0.2,
  verbose = TRUE,
  event_names = "ae",
  plot_event = "ae"
)

Arguments

df_visit

dataframe

df_site

dataframe, as returned by site_aggr()

study_id_str

character, specify study in study_id column

n_sites

integer, Default: 6

min_pat_pool

double, minimum ratio of available patients available for sampling. Determines maximum visit_med75 value see Details. Default: 0.2

verbose

logical, Default: TRUE

event_names

vector, contains the event names, default = "ae"

plot_event

vector containing the events that should be plotted, default = "ae"

Value

ggplot

Examples

df_visit <- sim_test_data_study(n_pat = 120, n_sites = 6,
    frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit$study_id <- "A"
df_site <- site_aggr(df_visit)

plot_visit_med75(df_visit, df_site, study_id_str = "A", n_site = 6)

Poisson test for vector with site AEs vs vector with study AEs.

Description

Internal function used by sim_sites().

Usage

poiss_test_site_ae_vs_study_ae(site_ae, study_ae, visit_med75)

Arguments

site_ae

vector with AE numbers

study_ae

vector with AE numbers

visit_med75

integer

Details

sets pvalue=1 if mean AE site is greater than mean AE study or ttest gives error

Value

pval

Examples

poiss_test_site_ae_vs_study_ae(
   site_ae = c(5, 3, 3, 2, 1, 6),
   study_ae = c(9, 8, 7, 9, 6, 7, 8),
   visit_med75 = 10
)

poiss_test_site_ae_vs_study_ae(
   site_ae = c(11, 9, 8, 6, 3),
   study_ae = c(9, 8, 7, 9, 6, 7, 8),
   visit_med75 = 10
)

Prepare data for simulation.

Description

Internal function called by sim_sites. Collect AEs per patient at visit_med75 for site and study as a vector of integers.

Usage

prep_for_sim(df_site, df_visit)

Arguments

df_site

dataframe created by site_aggr

df_visit

dataframe, created by sim_sites

Value

dataframe

Examples

df_visit <- sim_test_data_study(
   n_pat = 100,
   n_sites = 5,
   frac_site_with_ur = 0.4,
   ur_rate = 0.2
)

df_visit$study_id <- "A"

df_site <- site_aggr(df_visit)

df_prep <- prep_for_sim(df_site, df_visit)
df_prep

Calculate bootstrapped probability for obtaining a lower site mean AE number.

Description

Internal function used by sim_sites()

Usage

prob_lower_site_ae_vs_study_ae(
  site_ae,
  study_ae,
  r = 1000,
  parallel = FALSE,
  under_only = TRUE
)

Arguments

site_ae

vector with AE numbers

study_ae

vector with AE numbers

r

integer, denotes number of simulations, default = 1000

parallel

logical, toggles parallel processing on and of, default = F

under_only

compute under-reporting probabilities only, default = TRUE

Details

sets pvalue=1 if mean AE site is greater than mean AE study

Value

pval

Examples

prob_lower_site_ae_vs_study_ae(
  site_ae = c(5, 3, 3, 2, 1, 6),
  study_ae = c(9, 8, 7, 9, 6, 7, 8),
  parallel = FALSE
)

prune visits to visit_med75 using table operations

Description

prune visits to visit_med75 using table operations

Usage

prune_to_visit_med75_inframe(df_visit, df_site)

Arguments

df_visit

Data frame with columns: study_id, site_number, patnum, visit, n_ae.

df_site

dataframe, as returned by site_aggr()

Execute a purrr or furrr function with a progress bar.

Description

Internal utility function.

Usage

purrr_bar(
  ...,
  .purrr,
  .f,
  .f_args = list(),
  .purrr_args = list(),
  .steps,
  .slow = FALSE,
  .progress = TRUE
)

Arguments

...

iterable arguments passed to .purrr

.purrr

purrr or furrr function

.f

function to be executed over iterables

.f_args

list of arguments passed to .f, Default: list()

.purrr_args

list of arguments passed to .purrr, Default: list()

.steps

integer number of iterations

.slow

logical slows down execution, Default: FALSE

.progress

logical, show progress bar, Default: TRUE

Details

Call still needs to be wrapped in with_progress or with_progress_cnd()

Value

result of function passed to .f

Examples

# purrr::map
progressr::with_progress(
  purrr_bar(rep(0.25, 5), .purrr = purrr::map, .f = Sys.sleep, .steps = 5)
)


# purrr::walk
progressr::with_progress(
 purrr_bar(rep(0.25, 5), .purrr = purrr::walk,.f = Sys.sleep, .steps = 5)
)

# progress bar off
progressr::with_progress(
  purrr_bar(
    rep(0.25, 5), .purrr = purrr::walk,.f = Sys.sleep, .steps = 5, .progress = FALSE
  )
)

# purrr::map2
progressr::with_progress(
  purrr_bar(
    rep(1, 5), rep(2, 5),
    .purrr = purrr::map2,
    .f = `+`,
    .steps = 5,
    .slow = TRUE
 )
)

# purrr::pmap
progressr::with_progress(
  purrr_bar(
    list(rep(1, 5), rep(2, 5)),
    .purrr = purrr::pmap,
    .f = `+`,
    .steps = 5,
    .slow = TRUE
 )
)

# define function within purr_bar() call
progressr::with_progress(
  purrr_bar(
    list(rep(1, 5), rep(2, 5)),
    .purrr = purrr::pmap,
    .f = function(x, y) {
      paste0(x, y)
    },
    .steps = 5,
    .slow = TRUE
 )
)

# with mutate
progressr::with_progress(
 tibble::tibble(x = rep(0.25, 5)) %>%
  dplyr::mutate(x = purrr_bar(x, .purrr = purrr::map, .f = Sys.sleep, .steps = 5))
)

Start simulation after preparation.

Description

Internal function called by sim_sites after prep_for_sim

Usage

sim_after_prep(
  df_sim_prep,
  r = 1000,
  poisson_test = FALSE,
  prob_lower = TRUE,
  progress = FALSE,
  under_only = TRUE
)

Arguments

df_sim_prep

dataframe as returned by prep_for_sim

r

integer, denotes number of simulations, default = 1000

poisson_test

logical, calculates poisson.test pvalue

prob_lower

logical, calculates probability for getting a lower value

progress

logical, display progress bar, Default = TRUE

under_only

compute under-reporting probabilities only, default = TRUE check_df_visit(), computationally expensive on large data sets. Default: TRUE

Value

dataframe

Examples

df_visit <- sim_test_data_study(
   n_pat = 100,
   n_sites = 5,
   frac_site_with_ur = 0.4,
   ur_rate = 0.2
)

df_visit$study_id <- "A"

df_site <- site_aggr(df_visit)

df_prep <- prep_for_sim(df_site, df_visit)

df_sim <- sim_after_prep(df_prep)

df_sim

Calculate prob_lower for study sites using table operations

Description

Calculate prob_lower for study sites using table operations

Usage

sim_inframe(df_visit, r = 1000, df_site = NULL, event_names = c("ae"))

Arguments

df_visit

Data frame with columns: study_id, site_number, patnum, visit, n_ae.

r

Integer or tbl_object, number of repetitions for bootstrap simulation. Pass a tbl object referring to a table with one column and as many rows as desired repetitions. Default: 1000.

df_site

dataframe as returned be site_aggr(), Will switch to visit_med75. Default: NULL

event_names

vector, contains the event names, default = "ae"

Examples

df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  frac_site_with_ur = 0.4,
  ur_rate = 0.6
)
df_visit$study_id <- "A"

df_sim <- sim_inframe(df_visit)
df_eval <- eval_sites(df_sim)
df_eval

simulate single scenario

Description

internal function called by simulate_scenarios()

Usage

sim_scenario(n_ae_site, n_ae_study, frac_pat_with_ur, ur_rate)

Arguments

n_ae_site

integer vector

n_ae_study

integer vector

frac_pat_with_ur

double

ur_rate

double

Value

list

Examples

sim_scenario(c(5,5,5,5), c(8,8,8,8), 0.2, 0.5)
sim_scenario(c(5,5,5,5), c(8,8,8,8), 0.75, 0.5)
sim_scenario(c(5,5,5,5), c(8,8,8,8), 1, 0.5)
sim_scenario(c(5,5,5,5), c(8,8,8,8), 1, 1)
sim_scenario(c(5,5,5,5), c(8,8,8,8), 0, 0.5)
sim_scenario(c(5,5,5,5), c(8,8,8,8), 2, 0.5)

Calculate prob_lower and poisson.test pvalue for study sites.

Description

Collects the number of AEs of all eligible patients that meet visit_med75 criteria of site. Then calculates poisson.test pvalue and bootstrapped probability of having a lower mean value.

Usage

sim_sites(
  df_site,
  df_visit,
  r = 1000,
  poisson_test = TRUE,
  prob_lower = TRUE,
  progress = TRUE,
  check = TRUE,
  under_only = TRUE
)

Arguments

df_site

dataframe created by site_aggr

df_visit

dataframe, created by sim_sites

r

integer, denotes number of simulations, default = 1000

poisson_test

logical, calculates poisson.test pvalue

prob_lower

logical, calculates probability for getting a lower value

progress

logical, display progress bar, Default = TRUE

check

logical, perform data check and attempt repair with

under_only

compute under-reporting probabilities only, default = TRUE check_df_visit(), computationally expensive on large data sets. Default: TRUE

Value

dataframe with the following columns:

study_id: study identification
site_number: site identification
n_pat: number of patients at site
visit_med75: median(max(visit)) * 0.75
n_pat_with_med75: number of patients at site with med75
mean_ae_site_med75: mean AE at visit_med75 site level
mean_ae_study_med75: mean AE at visit_med75 study level
n_pat_with_med75_study: number of patients at study with med75 excl. site
pval: p-value as returned by poisson.test
prob_low: bootstrapped probability for having mean_ae_site_med75 or lower

Examples

df_visit <- sim_test_data_study(
   n_pat = 100,
   n_sites = 5,
   frac_site_with_ur = 0.4,
   ur_rate = 0.2
)

df_visit$study_id <- "A"

df_site <- site_aggr(df_visit)

df_sim_sites <- sim_sites(df_site, df_visit, r = 100)

df_sim_sites %>%
 knitr::kable(digits = 2)

Simulate studies.

Description

Test function, test applicability of poisson test, by calculating a the bootstrapped probability of obtaining a specific p-value or lower, use in combination with get_ecd_values().

Usage

sim_studies(
  df_visit,
  df_site,
  r = 100,
  poisson_test = TRUE,
  prob_lower = TRUE,
  r_prob_lower = 1000,
  under_only = TRUE,
  parallel = FALSE,
  keep_ae = FALSE,
  min_n_pat_with_med75 = 1,
  studies = NULL,
  .progress = TRUE
)

Arguments

df_visit

dataframe

df_site

dataframe

r

integer, denotes number of simulations, Default: 1000

poisson_test

logical, calculates poisson.test pvalue, Default: TRUE

prob_lower

logical, calculates probability for getting a lower value, Default: FALSE

r_prob_lower

integer, denotes number of simulations for prob_lower value calculation,, Default: 1000

under_only

compute under-reporting probabilities only, default = TRUE

parallel

logical, see examples for registering parallel processing framework , Default: FALSE

keep_ae

logical, keep ae numbers in output dataframe memory increase roughly 30 percent, Default: F

min_n_pat_with_med75

integer, min number of patients with med75 at site to simulate, Default: 1

studies

vector with study names, Default: NULL

.progress

logical, show progress bar

Details

Here we simulate study replicates maintaining the same number of sites, patients and visit_med75 by bootstrap resampling, then probabilities for obtaining lower or same mean_ae count and p-values using poisson.test are calculated.

adds column with simulated probabilities for equal or lower mean_ae at visit_med75

Value

dataframe

Examples


df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 5,
                                      frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit1$study_id <- "A"

df_visit2 <- sim_test_data_study(n_pat = 1000, n_sites = 3,
                                      frac_site_with_ur = 0.2, ur_rate = 0.1)

df_visit2$study_id <- "B"

df_visit <- dplyr::bind_rows(df_visit1, df_visit2)

df_site <- site_aggr(df_visit)

sim_studies(df_visit, df_site, r = 3, keep_ae = TRUE)

## Not run: 
# parallel processing -------------------------
library(future)
future::plan(multiprocess)
sim_studies(df_visit, df_site, r = 3, keep_ae = TRUE, parallel = TRUE)
future::plan(sequential)

## End(Not run)

simulate test data events

Description

generates multi-event data using sim_test_data_study()

Usage

sim_test_data_events(
  n_pat = 100,
  n_sites = 5,
  ae_per_visit_mean = 0.5,
  ae_rates = c(NULL),
  event_names = list("ae")
)

Arguments

n_pat

integer, number of patients, Default: 100

n_sites

integer, number of sites, Default: 5

ae_per_visit_mean

mean event per visit per patient, Default: 0.5

ae_rates

vector with visit-specific event rates, Default: Null

event_names

vector, contains the event names, default = "ae"

Value

tibble with columns site_number, patnum, is_ur, max_visit_mean, max_visit_sd, visit, and event data (events_per_visit_mean and n_events)

simulate patient ae reporting test data

Description

helper function for sim_test_data_study()

Usage

sim_test_data_patient(
  .f_sample_max_visit = function() rnorm(1, mean = 20, sd = 4),
  .f_sample_ae_per_visit = function(max_visit) rpois(max_visit, 0.5)
)

Arguments

.f_sample_max_visit

function used to sample the maximum number of aes, Default: function() rnorm(1, mean = 20, sd = 4)

.f_sample_ae_per_visit

function used to sample the aes for each visit, Default: function(x) rpois(x, 0.5)

Details

Value

vector containing cumulative aes

Examples

replicate(5, sim_test_data_patient())
replicate(5, sim_test_data_patient(
    .f_sample_ae_per_visit = function(x) rpois(x, 1.2))
  )
replicate(5, sim_test_data_patient(
    .f_sample_max_visit = function() rnorm(1, mean = 5, sd = 5))
  )

Simulate Portfolio Test Data

Description

Simulate visit level data from a portfolio configuration.

Usage

sim_test_data_portfolio(
  df_config,
  df_ae_rates = NULL,
  parallel = FALSE,
  progress = TRUE
)

Arguments

df_config

dataframe as returned by get_config

df_ae_rates

dataframe with ae rates. Default: NULL

parallel

logical activate parallel processing, see details, Default: FALSE

progress

logical, Default: TRUE

Details

uses sim_test_data_study. We use the furrr package to implement parallel processing as these simulations can take a long time to run. For this to work we need to specify the plan for how the code should run, e.g. 'plan(multisession, workers = 3)

Value

dataframe with the following columns:

study_id: study identification
ae_per_visit_mean: mean AE per visit per study
site_number: site
max_visit_sd: standard deviation of maximum patient visits per site
max_visit_mean: mean of maximum patient visits per site
patnum: number of patients
visit: visit number
n_ae: cumulative sum of AEs

Examples


df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit1$study_id <- "A"

df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.2, ur_rate = 0.1)

df_visit2$study_id <- "B"

df_visit <- dplyr::bind_rows(df_visit1, df_visit2)

df_site_max <- df_visit %>%
  dplyr::group_by(study_id, site_number, patnum) %>%
  dplyr::summarise(max_visit = max(visit),
            max_ae = max(n_ae),
            .groups = "drop")

df_config <- get_config(df_site_max)

df_config

df_portf <- sim_test_data_portfolio(df_config)

df_portf

df_scen <- sim_ur_scenarios(df_portf,
                            extra_ur_sites = 2,
                            ur_rate = c(0.5, 1))


df_scen

df_perf <- get_portf_perf(df_scen)

df_perf

simulate study test data

Description

evenly distributes a number of given patients across a number of given sites. Then simulates ae development of each patient reducing the number of reported AEs for patients distributed to AE-under-reporting sites.

Usage

sim_test_data_study(
  n_pat = 1000,
  n_sites = 20,
  frac_site_with_ur = 0,
  ur_rate = 0,
  max_visit_mean = 20,
  max_visit_sd = 4,
  ae_per_visit_mean = c(0.5),
  ae_rates = c(NULL),
  event_names = list("ae")
)

Arguments

n_pat

integer, number of patients, Default: 1000

n_sites

integer, number of sites, Default: 20

frac_site_with_ur

fraction of AE under-reporting sites, Default: 0

ur_rate

AE under-reporting rate, will lower mean ae per visit used to simulate patients at sites flagged as AE-under-reporting. Negative Values will simulate over-reporting., Default: 0

max_visit_mean

mean of the maximum number of visits of each patient, Default: 20

max_visit_sd

standard deviation of maximum number of visits of each patient, Default: 4

ae_per_visit_mean

mean event per visit per patient, Default: 0.5

ae_rates

vector with visit-specific event rates, Default: Null

event_names

vector, contains the event names, default = "ae"

Details

maximum visit number will be sampled from normal distribution with characteristics derived from max_visit_mean and max_visit_sd, while the ae per visit will be sampled from a poisson distribution described by ae_per_visit_mean.

Value

tibble with columns site_number, patnum, is_ur, max_visit_mean, max_visit_sd, ae_per_visit_mean, visit, n_ae

Examples

set.seed(1)
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5)
df_visit[which(df_visit$patnum == "P000001"),]
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5,
    frac_site_with_ur = 0.2, ur_rate = 0.5)
df_visit[which(df_visit$patnum == "P000001"),]
ae_rates <- c(0.7, rep(0.5, 8), rep(0.3, 5))
sim_test_data_study(n_pat = 100, n_sites = 5, ae_rates = ae_rates)

simulate under-reporting

Description

we remove a fraction of AEs from a specific site

Usage

sim_ur(df_visit, study_id, site_number, ur_rate)

Arguments

df_visit

dataframe

study_id

character

site_number

character

ur_rate

double

Details

we determine the absolute number of AEs per patient for removal. Then them remove them at the first visit. We intentionally allow fractions

Examples

df_visit <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit$study_id <- "A"

df_ur <- sim_ur(df_visit, "A", site_number = "S0001", ur_rate = 0.35)

# Example cumulated AE for first patient with 35% under-reporting
df_ur[df_ur$site_number == "S0001" & df_ur$patnum == "P000001",]$n_ae

# Example cumulated AE for first patient with no under-reporting
df_visit[df_visit$site_number == "S0001" & df_visit$patnum == "P000001",]$n_ae

Simulate Under-Reporting Scenarios

Description

Use with simulated portfolio data to generate under-reporting stats for specified scenarios.

Usage

sim_ur_scenarios(
  df_portf,
  extra_ur_sites = 3,
  ur_rate = c(0.25, 0.5),
  r = 1000,
  poisson_test = FALSE,
  prob_lower = TRUE,
  parallel = FALSE,
  progress = TRUE,
  site_aggr_args = list(),
  eval_sites_args = list(),
  check = TRUE
)

Arguments

df_portf

dataframe as returned by sim_test_data_portfolio

extra_ur_sites

numeric, set maximum number of additional under-reporting sites, see details Default: 3

ur_rate

numeric vector, set under-reporting rates for scenarios Default: c(0.25, 0.5)

r

integer, denotes number of simulations, default = 1000

poisson_test

logical, calculates poisson.test pvalue

prob_lower

logical, calculates probability for getting a lower value

parallel

logical, use parallel processing see details, Default: FALSE

progress

logical, show progress bar, Default: TRUE

site_aggr_args

named list of parameters passed to site_aggr, Default: list()

eval_sites_args

named list of parameters passed to eval_sites, Default: list()

check

logical, perform data check and attempt repair with

Details

The function will apply under-reporting scenarios to each site. Reducing the number of AEs by a given under-reporting (ur_rate) for all patients at the site and add the corresponding under-reporting statistics. Since the under-reporting probability is also affected by the number of other sites that are under-reporting we additionally calculate under-reporting statistics in a scenario where additional under reporting sites are present. For this we use the median number of patients per site at the study to calculate the final number of patients for which we lower the AEs in a given under-reporting scenario. We use the furrr package to implement parallel processing as these simulations can take a long time to run. For this to work we need to specify the plan for how the code should run, e.g. plan(multisession, workers = 18)

Value

dataframe with the following columns:

study_id: study identification
site_number: site identification
n_pat: number of patients at site
n_pat_with_med75: number of patients at site with visit_med75
visit_med75: median(max(visit)) * 0.75
mean_ae_site_med75: mean AE at visit_med75 site level
mean_ae_study_med75: mean AE at visit_med75 study level
n_pat_with_med75_study: number of patients at site with visit_med75 at study excl site
extra_ur_sites: additional sites with under-reporting patients
frac_pat_with_ur: ratio of patients in study that are under-reporting
ur_rate: under-reporting rate
pval: p-value as returned by poisson.test
prob_low: bootstrapped probability for having mean_ae_site_med75 or lower
pval_adj: adjusted p-values
prob_low_adj: adjusted bootstrapped probability for having mean_ae_site_med75 or lower
pval_prob_ur: probability under-reporting as 1 - pval_adj, poisson.test (use as benchmark)
prob_low_prob_ur: probability under-reporting as 1 - prob_low_adj, bootstrapped (use)

Examples


df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.4, ur_rate = 0.6)

df_visit1$study_id <- "A"

df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 frac_site_with_ur = 0.2, ur_rate = 0.1)

df_visit2$study_id <- "B"

df_visit <- dplyr::bind_rows(df_visit1, df_visit2)

df_site_max <- df_visit %>%
  dplyr::group_by(study_id, site_number, patnum) %>%
  dplyr::summarise(max_visit = max(visit),
            max_ae = max(n_ae),
            .groups = "drop")

df_config <- get_config(df_site_max)

df_config

df_portf <- sim_test_data_portfolio(df_config)

df_portf

df_scen <- sim_ur_scenarios(df_portf,
                            extra_ur_sites = 2,
                            ur_rate = c(0.5, 1))


df_scen

df_perf <- get_portf_perf(df_scen)

df_perf

Create simaerep object

Description

Simulate AE under-reporting probabilities.

Usage

simaerep(
  df_visit,
  r = 1000,
  check = TRUE,
  under_only = TRUE,
  visit_med75 = TRUE,
  inframe = FALSE,
  progress = TRUE,
  mult_corr = TRUE,
  param_site_aggr = list(method = "med75_adj", min_pat_pool = 0.2),
  param_sim_sites = list(r = 1000, poisson_test = FALSE, prob_lower = TRUE),
  param_eval_sites = list(method = "BH"),
  env = parent.frame(),
  event_names = c("ae")
)

Arguments

df_visit

Data frame with columns: study_id, site_number, patnum, visit, n_ae.

r

Integer or tbl_object, number of repetitions for bootstrap simulation. Pass a tbl object referring to a table with one column and as many rows as desired repetitions. Default: 1000.

check

Logical, perform data check and attempt repair with check_df_visit(). Computationally expensive on large data sets. Default: TRUE.

under_only

Logical, compute under-reporting probabilities only. Supersedes under_only parameter passed to eval_sites() and sim_sites(). Default: TRUE.

visit_med75

Logical, should evaluation point visit_med75 be used. Default: TRUE.

inframe

Logical, only table operations to be used; does not require visit_med75. Compatible with dbplyr supported database backends.

progress

Logical, display progress bar. Default: TRUE.

mult_corr

Logical, multiplicity correction, Default: TRUE

param_site_aggr

List of parameters passed to site_aggr(). Default: list(method = "med75_adj", min_pat_pool = 0.2).

param_sim_sites

List of parameters passed to sim_sites(). Default: list(r = 1000, poisson_test = FALSE, prob_lower = TRUE).

param_eval_sites

List of parameters passed to eval_sites(). Default: list(method = "BH").

env

Optional, provide environment of original visit data. Default: parent.frame().

event_names

vector, contains the event names, default = "ae"

Details

Executes site_aggr(), sim_sites(), and eval_sites() on original visit data and stores all intermediate results. Stores lazy reference to original visit data for facilitated plotting using generic plot(x).

Value

A simaerep object.

Examples

df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  frac_site_with_ur = 0.4,
  ur_rate = 0.6
)
df_visit$study_id <- "A"
aerep <- simaerep(df_visit)
aerep
str(aerep)


df_visit_events_test <- sim_test_data_events(n_pat = 100, n_sites = 5,
                                 ae_per_visit_mean = c(0.4, 0.5), event_names = c("ae", "pd"))
aerep_events <- simaerep(df_visit_events_test, inframe = TRUE,event_names = c("ae", "pd"))
aerep_events

  # In-frame table operations
  simaerep(df_visit, inframe = TRUE, visit_med75 = FALSE, under_only = FALSE)$df_eval
  simaerep(df_visit, inframe = TRUE, visit_med75 = TRUE, under_only = FALSE)$df_eval
  # Database example
  con <- DBI::dbConnect(duckdb::duckdb(), dbdir = ":memory:")
  df_r <- tibble::tibble(rep = seq(1, 1000))
  dplyr::copy_to(con, df_visit, "visit")
  dplyr::copy_to(con, df_r, "r")
  tbl_visit <- dplyr::tbl(con, "visit")
  tbl_r <- dplyr::tbl(con, "r")
  simaerep(tbl_visit, r = tbl_r, inframe = TRUE, visit_med75 = FALSE, under_only = FALSE)$df_eval
  simaerep(tbl_visit, r = tbl_r, inframe = TRUE, visit_med75 = TRUE, under_only = FALSE)$df_eval
  DBI::dbDisconnect(con)

simulate in dataframe

Description

simulate in dataframe

Usage

simaerep_inframe(
  df_visit,
  r = 1000,
  under_only = FALSE,
  visit_med75 = FALSE,
  check = TRUE,
  param_site_aggr = list(method = "med75_adj", min_pat_pool = 0.2),
  param_eval_sites = list(method = "BH"),
  env = parent.frame(),
  event_names = c("ae")
)

Arguments

df_visit

Data frame with columns: study_id, site_number, patnum, visit, n_ae.

r

Integer or tbl_object, number of repetitions for bootstrap simulation. Pass a tbl object referring to a table with one column and as many rows as desired repetitions. Default: 1000.

under_only

Logical, compute under-reporting probabilities only. Supersedes under_only parameter passed to eval_sites() and sim_sites(). Default: TRUE.

visit_med75

Logical, should evaluation point visit_med75 be used. Default: TRUE.

check

Logical, perform data check and attempt repair with check_df_visit(). Computationally expensive on large data sets. Default: TRUE.

param_site_aggr

List of parameters passed to site_aggr(). Default: list(method = "med75_adj", min_pat_pool = 0.2).

param_eval_sites

List of parameters passed to eval_sites(). Default: list(method = "BH").

env

Optional, provide environment of original visit data. Default: parent.frame().

event_names

vector, contains the event names, default = "ae"

Examples

df_visit <- sim_test_data_study(
 n_pat = 100,
 n_sites = 5,
 frac_site_with_ur = 0.4,
 ur_rate = 0.6
)
df_visit$study_id <- "A"

simaerep_inframe(df_visit)
simaerep_inframe(df_visit, visit_med75 = TRUE)$df_eval

# Database
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = ":memory:")
df_r <- tibble::tibble(rep = seq(1, 1000))

dplyr::copy_to(con, df_visit, "visit")
dplyr::copy_to(con, df_r, "r")

tbl_visit <- dplyr::tbl(con, "visit")
tbl_r <- dplyr::tbl(con, "r")

simaerep_inframe(tbl_visit, r = tbl_r)$df_eval
simaerep_inframe(tbl_visit, r = tbl_r, visit_med75 = TRUE)$df_eval

DBI::dbDisconnect(con)

Aggregate from visit to site level.

Description

Calculates visit_med75, n_pat_with_med75 and mean_ae_site_med75

Usage

site_aggr(
  df_visit,
  method = "med75_adj",
  min_pat_pool = 0.2,
  check = TRUE,
  event_names = c("ae")
)

Arguments

df_visit

dataframe with columns: study_id, site_number, patnum, visit, n_ae

method

character, one of c("med75", "med75_adj") defining method for defining evaluation point visit_med75 (see details), Default: "med75_adj"

min_pat_pool

double, minimum ratio of available patients available for sampling. Determines maximum visit_med75 value see Details. Default: 0.2

check

logical, perform data check and attempt repair with check_df_visit(), computationally expensive on large data sets. Default: TRUE

event_names

vector, contains the event names, default = "ae"

Details

For determining the visit number at which we are going to evaluate AE reporting we take the maximum visit of each patient at the site and take the median. Then we multiply with 0.75 which will give us a cut-off point determining which patient will be evaluated. Of those patients we will evaluate we take the minimum of all maximum visits hence ensuring that we take the highest visit number possible without excluding more patients from the analysis. In order to ensure that the sampling pool for that visit is large enough we limit the visit number by the 80% quantile of maximum visits of all patients in the study.

Value

dataframe with the following columns:

study_id: study identification
site_number: site identification
n_pat: number of patients, site level
visit_med75: adjusted median(max(visit)) * 0.75 see Details
n_pat_with_med75: number of patients that meet visit_med75 criterion, site level
mean_ae_site_med75: mean AE at visit_med75, site level

Examples

df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  frac_site_with_ur = 0.4,
  ur_rate = 0.6
)

df_visit$study_id <- "A"

df_site <- site_aggr(df_visit)

df_site %>%
  knitr::kable(digits = 2)

Conditional `with_progress`.

Description

Internal function. Use instead of with_progress within custom functions with progress bars.

Usage

with_progress_cnd(ex, progress = TRUE)

Arguments

ex

expression

progress

logical, Default: TRUE

Details

This wrapper adds a progress parameter to with_progress so that we can control the progress bar in the user facing functions. The progressbar only shows in interactive mode.

Value

No return value, called for side effects

Examples

if (interactive()) {

 with_progress_cnd(
   purrr_bar(rep(0.25, 5), .purrr = purrr::map, .f = Sys.sleep, .steps = 5),
   progress = TRUE
 )

 with_progress_cnd(
   purrr_bar(rep(0.25, 5), .purrr = purrr::map, .f = Sys.sleep, .steps = 5),
   progress = FALSE
 )

# wrap a function with progress bar with another call with progress bar

f1 <- function(x, progress = TRUE) {
  with_progress_cnd(
    purrr_bar(x, .purrr = purrr::walk, .f = Sys.sleep, .steps = length(x), .progress = progress),
    progress = progress
  )
}

# inner progress bar blocks outer progress bar
progressr::with_progress(
  purrr_bar(
    rep(rep(1, 3),3), .purrr = purrr::walk, .f = f1, .steps = 3,
    .f_args = list(progress = TRUE)
  )
)

# inner progress bar turned off
progressr::with_progress(
  purrr_bar(
    rep(list(rep(0.25, 3)), 5), .purrr = purrr::walk, .f = f1, .steps = 5,
    .f_args = list(progress = FALSE)
  )
)
}

Pipe operator

Description

Usage

Value

Aggregate duplicated visits.

Description

Usage

Arguments

Value

Integrity check for df_visit.

Description

Usage

Arguments

Value

Examples

Evaluate sites.

Description

Usage

Arguments

Value

See Also

Examples

Expose implicitly missing visits.

Description

Usage

Arguments

Value

Get Portfolio Configuration

Description

Usage

Arguments

Value

See Also

Examples

Get empirical cumulative distribution values of pval or prob_lower

Description

Usage

Arguments

Details

Value

Examples

replace cowplot::get_legend, to silence warning Multiple components found; returning the first one. To return all, use 'return_all = TRUE

Description

Usage

Configure study patient pool by site parameters.

Description

Usage

Arguments

Details

Value

Examples

Get Portfolio Performance

Description

Usage

Arguments

Details

Value

See Also

Examples

Get site mean ae development.

Description

Usage

Arguments

Value

Get visit_med75.

Description

Usage

Arguments

Value

is orivisit class

Description

Usage

Arguments

Value

is simaerep class

Description

Usage

Arguments

Value

Calculate Max Rank