### Examples

Source: https://openpharma.github.io/simaerep/reference/sim_after_prep.html

Example of how to use the sim_after_prep function with test data.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = 0.6
  ) %>%
  # internal functions require internal column names
  dplyr::rename(
    n_ae = n_event,
    site_number = site_id,
    patnum = patient_id
  )

df_site <- site_aggr(df_visit)

df_prep <- simaerep:::prep_for_sim(df_site, df_visit)

df_sim <- simaerep:::sim_after_prep(df_prep)

df_sim
#> # A tibble: 5 × 9
#>   study_id site_number n_pat n_pat_with_med75 visit_med75 mean_ae_site_med75
#>   <chr>    <chr>       <int>            <dbl>       <int>              <dbl>
#> 1 A        S0001          20               20          15               18.8
#> 2 A        S0002          20               17          16               18  
#> 3 A        S0003          20               17          16               12  
#> 4 A        S0004          20               20          15               10.2
#> 5 A        S0005          20               16          15               11.2
#> # ℹ 3 more variables: mean_ae_study_med75 <dbl>, n_pat_with_med75_study <int>,
#> #   prob_low <dbl>
```

--------------------------------

### Example

Source: https://openpharma.github.io/simaerep/reference/get_portf_config.html

Example of how to use get_portf_config to generate portfolio configuration from simulated study data.

```R
df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 ratio_out = 0.4, factor_event_rate = - 0.6,
                                 study_id = "A")


df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 ratio_out = 0.2, factor_event_rate = - 0.1,
                                 study_id = "B")


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


get_portf_config(df_visit)
#> # A tibble: 20 × 6
#>    study_id event_per_visit_mean site_id max_visit_sd max_visit_mean n_pat
#>    <chr>                   <dbl> <chr>          <dbl>          <dbl> <int>
#>  1 0001                    0.455 0001            2.86           20.2    10
#>  2 0001                    0.455 0002            3.98           20.1    10
#>  3 0001                    0.455 0003            5.87           19.6    10
#>  4 0001                    0.455 0004            4.38           18.1    10
#>  5 0001                    0.455 0005            5.43           18.9    10
#>  6 0001                    0.455 0006            3.74           19.7    10
#>  7 0001                    0.455 0007            3.08           18.8    10
#>  8 0001                    0.455 0008            4.12           19.4    10
#>  9 0001                    0.455 0009            3.49           22.2    10
#> 10 0001                    0.455 0010            2.23           20.9    10
#> 11 0002                    0.576 0001            3.98           18.5    10
#> 12 0002                    0.576 0002            3.03           19.6    10
#> 13 0002                    0.576 0003            2.86           20.8    10
#> 14 0002                    0.576 0004            4.88           20.5    10
#> 15 0002                    0.576 0005            4.90           21.3    10
#> 16 0002                    0.576 0006            3.74           19.8    10
#> 17 0002                    0.576 0007            6.07           20      10
#> 18 0002                    0.576 0008            4.38           18.6    10
#> 19 0002                    0.576 0009            4.75           17.9    10
#> 20 0002                    0.576 0010            3.14           19.1    10

# \donttest{
```

--------------------------------

### Database Example

Source: https://openpharma.github.io/simaerep/reference/get_portf_event_rates.html

Shows how to use get_portf_event_rates with a database connection.

```R
# Database example
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = ":memory:")
dplyr::copy_to(con, df_visit, "visit")
tbl_visit <- dplyr::tbl(con, "visit")
get_portf_event_rates(tbl_visit)
#> # A tibble: 59 × 4
#>    study_id visit event_rate n_pat
#>    <chr>    <int>      <dbl> <dbl>
#>  1 0001         4     1.57     100
#>  2 0001         7     0.57     100
#>  3 0001        13     0.0526    95
#>  4 0001        21     0.114     35
#>  5 0002         2     1.76     100
#>  6 0002        22     0         26
#>  7 0002        26     0          9
#>  8 0001         5     0.95     100
#>  9 0002         8     0.41     100
#> 10 0002         9     0.3      100
#> # ℹ 49 more rows
DBI::dbDisconnect(con)
# }
```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/prep_for_sim.html

Example of how to use the prep_for_sim function with test data.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = 0.6
  ) %>%
  # internal functions require internal column names
  dplyr::rename(
    n_ae = n_event,
    site_number = site_id,
    patnum = patient_id
  )

df_site <- site_aggr(df_visit)

df_prep <- simaerep:::prep_for_sim(df_site, df_visit)
df_prep
```

--------------------------------

### Example Usage

Source: https://openpharma.github.io/simaerep/reference/get_portf_event_rates.html

Demonstrates how to use the get_portf_event_rates function with sample dataframes.

```R
df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 ratio_out = 0.4, factor_event_rate = - 0.6,
                                 study_id = "A")


df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
                                 ratio_out = 0.2, factor_event_rate = - 0.1,
                                 study_id = "B")


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


get_portf_event_rates(df_visit)
#> # A tibble: 59 × 4
#>    study_id visit event_rate n_pat
#>    <chr>    <dbl>      <dbl> <int>
#>  1 0001         1      0       100
#>  2 0001         2      1.35    100
#>  3 0001         3      1.5     100
#>  4 0001         4      1.57    100
#>  5 0001         5      0.95    100
#>  6 0001         6      0.87    100
#>  7 0001         7      0.57    100
#>  8 0001         8      0.32    100
#>  9 0001         9      0.263    99
#> 10 0001        10      0.182    99
#> # ℹ 49 more rows
```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/eval_sites.html

An example of how to use the eval_sites function with simulated data.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = 0.6
  ) %>%
  # internal functions require internal column names
  dplyr::rename(
    n_ae = n_event,
    site_number = site_id,
    patnum = patient_id
  )

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
```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/sim_test_data_portfolio.html

Example of how to use sim_test_data_portfolio to simulate portfolio test data.

```R
# \donttest{
# df_visit1 <- sim_test_data_study(n_pat = 100, n_sites = 10,
#                                  ratio_out = 0.4, factor_event_rate = 0.6,
#                                  study_id = "A")
# 
# df_visit2 <- sim_test_data_study(n_pat = 100, n_sites = 10,
#                                  ratio_out = 0.2, factor_event_rate = 0.1,
#                                  study_id = "B")
# 
# 
# df_visit <- dplyr::bind_rows(df_visit1, df_visit2)
# 
# df_config <- get_portf_config(df_visit)
# 
# df_config
# #> # A tibble: 20 × 6
# #>    study_id event_per_visit_mean site_id max_visit_sd max_visit_mean n_pat
# #>    <chr>                   <dbl> <chr>          <dbl>          <dbl> <int>
# #>  1 0001                    0.778 0001            3.08           20.8    10
# #>  2 0001                    0.778 0002            3.39           20.8    10
# #>  3 0001                    0.778 0003            4.93           19.5    10
# #>  4 0001                    0.778 0004            3.43           19.3    10
# #>  5 0001                    0.778 0005            4.50           19.5    10
# #>  6 0001                    0.778 0006            3.92           17.3    10
# #>  7 0001                    0.778 0007            4.76           19.7    10
# #>  8 0001                    0.778 0008            4.40           19.3    10
# #>  9 0001                    0.778 0009            4.27           19.3    10
# #> 10 0001                    0.778 0010            2.99           20.4    10
# #> 11 0002                    0.632 0001            4.30           19.7    10
# #> 12 0002                    0.632 0002            2.49           19.8    10
# #> 13 0002                    0.632 0003            5.32           19.4    10
# #> 14 0002                    0.632 0004            3.77           19.3    10
# #> 15 0002                    0.632 0005            5.44           19.7    10
# #> 16 0002                    0.632 0006            4.72           19.5    10
# #> 17 0002                    0.632 0007            4.10           20.8    10
# #> 18 0002                    0.632 0008            3.88           17.8    10
# #> 19 0002                    0.632 0009            3.89           18.3    10
# #> 20 0002                    0.632 0010            3.19           17.8    10
# 
# df_portf <- sim_test_data_portfolio(df_config)
# 
# df_portf
# #> # A tibble: 3,725 × 8
# #>    study_id event_per_visit_mean site_id max_visit_sd max_visit_mean patient_id
# #>    <chr>                   <dbl> <chr>          <dbl>          <dbl> <chr>     
# #>  1 0001                    0.778 0001            3.08           20.8 0001      
# #>  2 0001                    0.778 0001            3.08           20.8 0001      
# #>  3 0001                    0.778 0001            3.08           20.8 0001      
# #>  4 0001                    0.778 0001            3.08           20.8 0001      
# #>  5 0001                    0.778 0001            3.08           20.8 0001      
# #>  6 0001                    0.778 0001            3.08           20.8 0001      
# #>  7 0001                    0.778 0001            3.08           20.8 0001      
# #>  8 0001                    0.778 0001            3.08           20.8 0001      
# #>  9 0001                    0.778 0001            3.08           20.8 0001      
# #> 10 0001                    0.778 0001            3.08           20.8 0001      
# #> # ℹ 3,715 more rows
# #> # ℹ 2 more variables: visit <int>, n_event <dbl>
# 
# # }

```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/site_aggr.html

Example of how to use the site_aggr function with test data.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = 0.6
  ) %>%
  # internal functions require internal column names
  dplyr::rename(
    n_ae = n_event,
    site_number = site_id,
    patnum = patient_id
  )

df_site <- site_aggr(df_visit)

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

--------------------------------

### Example Usage

Source: https://openpharma.github.io/simaerep/reference/plot_sim_example.html

An example of how to call the plot_sim_example function with a modified argument.

```R
# \donttest{
plot_sim_example(size_dots = 5)
#> Warning: Cannot convert object of class list into a grob.

# }

```

--------------------------------

### Example

Source: https://openpharma.github.io/simaerep/reference/orivisit.html

Example of how to use the orivisit function with simulated test data.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = - 0.6
)#'

visit <- orivisit(df_visit)

object.size(df_visit)
#> 131576 bytes
object.size(visit)
#> 3544 bytes

as.data.frame(visit)
#> # A tibble: 1,932 × 9
#>    patient_id site_id is_out max_visit_mean max_visit_sd event_per_visit_mean
#>    <chr>      <chr>   <lgl>           <dbl>        <dbl>                <dbl>
#>  1 P000001    S0001   TRUE               20            4                0.141
#>  2 P000001    S0001   TRUE               20            4                0.141
#>  3 P000001    S0001   TRUE               20            4                0.141
#>  4 P000001    S0001   TRUE               20            4                0.141
#>  5 P000001    S0001   TRUE               20            4                0.141
#>  6 P000001    S0001   TRUE               20            4                0.141
#>  7 P000001    S0001   TRUE               20            4                0.141
#>  8 P000001    S0001   TRUE               20            4                0.141
#>  9 P000001    S0001   TRUE               20            4                0.141
#> 10 P000001    S0001   TRUE               20            4                0.141
#> # ℹ 1,922 more rows
#> # ℹ 3 more variables: visit <int>, n_event <dbl>, study_id <chr>
```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/pat_pool.html

Example of how to use the pat_pool function.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = 0.6
  ) %>%
  # internal functions require internal column names
  dplyr::rename(
    n_ae = n_event,
    site_number = site_id,
    patnum = patient_id
  )

df_site <- site_aggr(df_visit)

df_pat_pool <- simaerep:::pat_pool(df_visit, df_site)

df_pat_pool
#> # A tibble: 1 × 2
#>   study_id pat_pool            
#>   <chr>    <list>              
#> 1 A        <tibble [1,554 × 3]>
```

--------------------------------

### Usage

Source: https://openpharma.github.io/simaerep/reference/sim_after_prep.html

The usage of the sim_after_prep function.

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

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/plot_dots.html

An example of how to use the plot_dots function with a sample dataframe.

```R
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)
```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/sim_sites.html

An example demonstrating how to use the sim_sites function with simulated data.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = 0.6
  ) %>%
  # internal functions require internal column names
  dplyr::rename(
    n_ae = n_event,
    site_number = site_id,
    patnum = patient_id
  )

df_site <- site_aggr(df_visit)

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

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

--------------------------------

### Example

Source: https://openpharma.github.io/simaerep/reference/sim_out.html

Example of simulating under-reporting and comparing cumulated events.

```R
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 10)

df_ur <- sim_out(df_visit, "A", site_id = "S0001", factor_event = - 0.35)

# Example cumulated event for first patient with 35% under-reporting
df_ur[df_ur$site_id == "S0001" & df_ur$patient_id == "P000001",]$n_event
#>  [1] 0.0 0.0 0.0 0.5 1.5 2.5 3.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5
#> [20] 4.5 6.5 6.5 6.5 6.5

# Example cumulated event for first patient with no under-reporting
df_visit[df_visit$site_id == "S0001" & df_visit$patient_id == "P000001",]$n_event
#>  [1]  0  2  2  4  5  6  7  8  8  8  8  8  8  8  8  8  8  8  8  8 10 10 10 10
```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/sim_test_data_study.html

Example of how to use the `sim_test_data_study` function.

```R
set.seed(1)
# no outlier
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5)
df_visit[which(df_visit$patient_id == "P000001"),]
#> # A tibble: 17 × 9
#>    patient_id site_id is_out max_visit_mean max_visit_sd event_per_visit_mean
#>    <chr>      <chr>   <lgl>           <dbl>        <dbl>                <dbl>
#>  1 P000001    S0001   FALSE              20            4                0.353
#>  2 P000001    S0001   FALSE              20            4                0.353
#>  3 P000001    S0001   FALSE              20            4                0.353
#>  4 P000001    S0001   FALSE              20            4                0.353
#>  5 P000001    S0001   FALSE              20            4                0.353
#>  6 P000001    S0001   FALSE              20            4                0.353
#>  7 P000001    S0001   FALSE              20            4                0.353
#>  8 P000001    S0001   FALSE              20            4                0.353
#>  9 P000001    S0001   FALSE              20            4                0.353
#> 10 P000001    S0001   FALSE              20            4                0.353
#> 11 P000001    S0001   FALSE              20            4                0.353
#> 12 P000001    S0001   FALSE              20            4                0.353
#> 13 P000001    S0001   FALSE              20            4                0.353
#> 14 P000001    S0001   FALSE              20            4                0.353
#> 15 P000001    S0001   FALSE              20            4                0.353
#> 16 P000001    S0001   FALSE              20            4                0.353
#> 17 P000001    S0001   FALSE              20            4                0.353
#> # ℹ 3 more variables: visit <int>, n_event <dbl>, study_id <chr>

# under-reporting outlier
df_visit <- sim_test_data_study(n_pat = 100, n_sites = 5,
    ratio_out = 0.2, factor_event_rate = -0.5)
df_visit[which(df_visit$patient_id == "P000001"),]
#> # A tibble: 23 × 9
#>    patient_id site_id is_out max_visit_mean max_visit_sd event_per_visit_mean
#>    <chr>      <chr>   <lgl>           <dbl>        <dbl>                <dbl>
#>  1 P000001    S0001   TRUE               20            4                0.176
#>  2 P000001    S0001   TRUE               20            4                0.176
#>  3 P000001    S0001   TRUE               20            4                0.176
#>  4 P000001    S0001   TRUE               20            4                0.176
#>  5 P000001    S0001   TRUE               20            4                0.176
#>  6 P000001    S0001   TRUE               20            4                0.176
#>  7 P000001    S0001   TRUE               20            4                0.176
#>  8 P000001    S0001   TRUE               20            4                0.176
#>  9 P000001    S0001   TRUE               20            4                0.176
#> 10 P000001    S0001   TRUE               20            4                0.176
#> # ℹ 13 more rows
#> # ℹ 3 more variables: visit <int>, n_event <dbl>, study_id <chr>
```

--------------------------------

### Example 1: Basic usage

Source: https://openpharma.github.io/simaerep/reference/sim_studies.html

This example demonstrates the basic usage of the sim_studies function with sample data.

```R
# \donttest{
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)
#> # A tibble: 24 × 10
#>        r study_id site_number visit_med75 n_pat_with_med75 n_pat_study n_ae_site
#>    <dbl> <chr>    <chr>             <dbl>            <dbl>       <dbl> <chr>    
#>  1     1 A        S0001                16               18          68 11,5,7,4…
#>  2     1 A        S0002                18               17          53 11,16,8,…
#>  3     1 A        S0003                17               18          62 13,11,13…
#>  4     1 A        S0004                14               18          74 7,9,7,6,…
#>  5     1 A        S0005                15               17          71 6,9,5,4,…
#>  6     1 B        S0001                15              297         597 10,13,11…
#>  7     1 B        S0002                15              296         598 3,10,4,6…
#>  8     1 B        S0003                15              301         593 3,6,7,10…
#>  9     2 A        S0001                16               18          68 10,5,7,3…
#> 10     2 A        S0002                18               17          53 5,17,8,6…
#> # ℹ 14 more rows
#> # ℹ 3 more variables: n_ae_study <chr>, pval <dbl>, prob_low <dbl> 
# }
```

--------------------------------

### Database Calculation Example

Source: https://openpharma.github.io/simaerep/articles/intro.html

Demonstrates setting up a DuckDB database, creating tables for study data and replications, and running the simaerep function with lazy tables.

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

df_visit <- sim_test_data_study(
  n_pat = 100, # number of patients in study
  n_sites = 10, # number of sites in study
  ratio_out = 0.1, # ratio of sites with outlier
  factor_event_rate = -0.5, # rate of under-reporting
  # non-constant event rates based on gamma distribution
  event_rates = (dgamma(seq(1, 20, 0.5), shape = 5, rate = 2) * 5) + 0.1,
  max_visit = 20,
  max_visit_sd = 10,
  study_id = "A"
)

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

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

evrep_db <- simaerep(tbl_visit, r = tbl_r)
```

--------------------------------

### Example 2: Parallel processing

Source: https://openpharma.github.io/simaerep/reference/sim_studies.html

This example shows how to use parallel processing with the sim_studies function.

```R
# parallel processing -------------------------
library(future)
future::plan(multiprocess)
sim_studies(df_visit, df_site, r = 3, keep_ae = TRUE, parallel = TRUE)
future::plan(sequential)

```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/check_df_visit.html

Example of how to use the check_df_visit function with test data.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = 0.6
  ) %>%
  # internal functions require internal column names
  dplyr::rename(
    site_number = site_id,
    patnum = patient_id
  )

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

df_visit_corr <- check_df_visit(df_visit_filt)
#> Warning: implicitly missing visit numbers detected and corrected
3 %in% df_visit_corr$visit
#> [1] TRUE
nrow(df_visit_corr) == nrow(df_visit)
#> [1] TRUE

df_visit_corr <- check_df_visit(dplyr::bind_rows(df_visit, df_visit))
#> Warning: Duplicated visit entries for some patients detected and corrected.
nrow(df_visit_corr) == nrow(df_visit)
#> [1] TRUE
```

--------------------------------

### Usage

Source: https://openpharma.github.io/simaerep/reference/get_config.html

Function signature for get_config.

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

--------------------------------

### Usage

Source: https://openpharma.github.io/simaerep/reference/get_portf_config.html

The function signature for get_portf_config.

```R
get_portf_config(
  df_visit,
  check = TRUE,
  min_pat_per_study = 100,
  min_sites_per_study = 10,
  anonymize = TRUE,
  pad_width = 4
)
```

--------------------------------

### Install simaerep Development Version from GitHub

Source: https://openpharma.github.io/simaerep/index.html

Install the development version of the simaerep package from GitHub using the devtools package.

```r
# install.packages("devtools")
devtools::install_github("openpharma/simaerep")
```

--------------------------------

### purrr::walk example

Source: https://openpharma.github.io/simaerep/reference/purrr_bar.html

Example of using purrr_bar with purrr::walk and Sys.sleep.

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

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/sim_test_data_patient.html

Examples demonstrating the output of sim_test_data_patient with default parameters and with custom event per visit sampling.

```R
replicate(5, sim_test_data_patient())
#> [[1]]
#>  [1]  0  2  2  4  5  6  6  7 10 10 10 11 12 13 13 13 14 15 15 15
#> 
#> [[2]]
#>  [1] 0 0 0 0 0 0 0 0 0 1 3 4 4 4 4 4 4 5
#> 
#> [[3]]
#>  [1]  0  2  3  4  5  6  7  7  7  7  9 11 11 11 12 12 12 13 14
#> 
#> [[4]]
#>  [1] 0 1 1 1 1 1 2 2 2 2 2 2 3 5 6 6
#> 
#> [[5]]
#>  [1]  1  1  1  3  3  5  6  6  7  7  7  7  7  7  7 10 12 12 12 12
#> 
replicate(5, sim_test_data_patient(
    .f_sample_event_per_visit = function(x) rpois(x, 1.2))
  )
#> [[1]]
#>  [1]  1  2  2  3  3  4  5  7  7  7 10 10 12 15 15 15 15 16 19 19 19 20 22 24
#> 
#> [[2]]
#>  [1]  2  4  5  6  6  6  6  6  8 10 12 13 14 15 17 19 20 23 24 25 26 26 28
#> 
#> [[3]]
#>  [1]  0  1  1  1  1  3  8  9  9 12 13 13 13 14 14 16 17 20 23 25 26 28 30 31 31
#> 
#> [[4]]
#>  [1]  2  2  2  3  4  7  9 10 10 11 12 13 16 16 17 17 17 18 18
#> 
#> [[5]]
#>  [1]  2  3  4  4  5  7 10 12 12 14 16 18 18 20 21 21 21 21 22 25
#> 
replicate(5, sim_test_data_patient(
    .f_sample_max_visit = function() rnorm(1, mean = 5, sd = 5))
  )
#> [[1]]
#> [1] 1 2 2
#> 
#> [[2]]
#> [1] 1
#> 
#> [[3]]
#> [1] 0 1
#> 
#> [[4]]
#> [1] 0 0 2 4 5 6 6 6
#> 
#> [[5]]
#> [1] 0 0 1 1 1 1 1
#> 

```

--------------------------------

### Example Usage 1

Source: https://openpharma.github.io/simaerep/reference/poiss_test_site_ae_vs_study_ae.html

An example demonstrating the usage of `poiss_test_site_ae_vs_study_ae` with sample AE data and a visit median.

```R
simaerep:::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
)
#> [1] 0.0009782833
```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/with_progress_cnd.html

Examples demonstrating the use of `with_progress_cnd` for controlling progress bars, including nested progress bars.

```R
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)
 )
)
}
```

--------------------------------

### Example Usage 2

Source: https://openpharma.github.io/simaerep/reference/poiss_test_site_ae_vs_study_ae.html

A second example of `poiss_test_site_ae_vs_study_ae` with different AE data.

```R
simaerep:::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
)
#> [1] 0.9154536
```

--------------------------------

### Examples

Source: https://openpharma.github.io/simaerep/reference/plot.simaerep.html

Demonstrates how to use the plot function with simulated data.

```R
# \donttest{
# df_visit <- sim_test_data_study(
#   n_pat = 100,
#   n_sites = 5,
#   ratio_out = 0.4,
#   factor_event_rate = - 0.6
# )
# 
# evrep <- simaerep(df_visit)
# 
# plot(evrep, what = "prob", study = "A")
# 
# plot(evrep, what = "med75", study = "A")
# #> purple line:          mean site event of patients with visit_med75
# #> grey line:            patient included
# #> black dashed line:    patient excluded
# #> dotted vertical line: visit_med75, 0.75 x median of maximum patient visits of site 
# #> solid vertical line:  visit_med75 adjusted, increased to minimum maximum patient visit of included patients
# #> dashed vertical line: maximum value for visit_med75 adjusted, 80% quantile of maximum patient visits of study
# #> 
# 
# # }

```

--------------------------------

### Example Usage

Source: https://openpharma.github.io/simaerep/reference/sim_inframe.html

An example demonstrating how to use the sim_inframe function with simulated test data.

```R
df_visit <- sim_test_data_study(
  n_pat = 100,
  n_sites = 5,
  ratio_out = 0.4,
  factor_event_rate = - 0.6
) %>%
  dplyr::rename(
    site_number = site_id,
    patnum = patient_id,
    n_ae = n_event
  )

df_sim <- simaerep:::sim_inframe(df_visit)
```

--------------------------------

### Install {gsm} Package

Source: https://openpharma.github.io/simaerep/articles/funnel_perf.html

Installs the {gsm} R package from a specific GitHub repository and branch.

```r
devtools::install_github("Gilead-BioStats/gsm@v1.9.2", ref = "main")
```

--------------------------------

### Simulate portfolio performance

Source: https://openpharma.github.io/simaerep/reference/get_config.html

This code snippet simulates portfolio data based on the generated configuration and then simulates under-reporting scenarios.

```r
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
```

--------------------------------

### Simulate study data and generate configuration

Source: https://openpharma.github.io/simaerep/reference/get_config.html

This code snippet simulates data for two studies, combines them, calculates site-level maximums, and then uses `get_config` to generate a configuration object.

```r
# Simulate data for two studies
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"

# Combine the dataframes
df_visit <- dplyr::bind_rows(df_visit1, df_visit2)

# Calculate site-level maximums
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")

# Generate the configuration object
df_config <- get_config(df_site_max)

df_config
```

--------------------------------

### Example Usage

Source: https://openpharma.github.io/simaerep/reference/prob_lower_site_ae_vs_study_ae.html

An example demonstrating how to use the prob_lower_site_ae_vs_study_ae function with sample site and study AE data, and the expected output.

```R
simaerep:::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)
)
#> [1] 0.011
```