## Introduction

You imported your database, but now you need to check it for errors and inconsistencies.

There are a lot of ways to do so, so EDCimport provides functions for a few concepts.

As in previous vignettes, we will be using `edc_example()`, but in the real world you should use EDC reading functions. See `vignette("reading")` to see how.

    library(EDCimport)
    library(dplyr)
    db = edc_example(N=200) %>%
      edc_unify_subjid() %>%
      edc_clean_names()
    db
    #> ── EDCimport database ──────────────────────────────────────────────────────────
    load_database(db)

## Data warning

### Data errors

The primary and most valuable data-checking tool in EDCimport is `edc_data_warn()`.

Simply use `dplyr::filter()` to identify problematic or inconsistent rows and pipe them into the function.

For example, let’s say that in our study:

- Patients should be older than 25

- Adverse event grades should be between 1 and 5.

- Patients in the treatment arm should not have `data1$date1` before 2010-04-10

Here’s how you check these conditions:

    enrol %>%
      filter(age<25) %>%
      edc_data_warn("Patients should be >25yo", issue_n=1)
    #> Warning: Issue #01: Patients should be >25yo (2 patients: #18 and #59)

    ae %>%
      filter(aegr<1 | aegr>5) %>%
      edc_data_warn("Incorrect adverse event grade", issue_n=2)

    data1 %>%
      edc_left_join(enrol) %>%
      filter(arm=="Trt") %>%
      filter(date1<"2010-04-10") %>%
      edc_data_warn("Treated patients should have been seen later", issue_n=3)
    #> Warning: Issue #03: Treated patients should have been seen later (9 patients: #1, #45,
    #> #64, #69, #82, …)

You can implement these checks according to your Data Validation Plan, ensuring they run after every export. Any failed check will produce a warning in your R console. Once the database is corrected, the warnings will no longer appear (e.g. `issue_n=2`).

After running all your checks, you can use `edc_data_warnings()` to get a summary of all detected issues.

    edc_data_warnings()
    #> # A tibble: 3 × 4
    #>   issue_n message                                      subjid    data
    #>   <chr>   <chr>                                        <list>    <list>
    #> 1 01      Patients should be >25yo                     <chr [2]> <tibble>
    #> 2 02      Incorrect adverse event grade                <NULL>    <tibble>
    #> 3 03      Treated patients should have been seen later <chr [9]> <tibble>

### Fatal error

If one check is so mandatory that you cannot work in a database where it fails, use `edc_data_stop()` instead.

For example, you can use it to check that some variable construction didn’t go wrong:

    df = mtcars %>%
      mutate(
        type = case_when(
          cyl==4 ~ "4 cylinders",
          cyl==6 ~ "6 cylinders",
          cyl==8 ~ "8 cylinders",
          .default="ERROR"
        ),
      )

    df %>%
      filter(type=="ERROR") %>%
      edc_data_stop("Error on type construction")

## Duplicate-free dataset assertion

If you work with multiple datasets, your code probably include a lot of joins. As you may have painfully discovered, joining data carries a high risk of altering the data layout and resulting in multiple rows per patient.

This is why you should always include `assert_no_duplicate()` in your pipeline if you expect only one row per patient.

    enrol %>%
      assert_no_duplicate() %>%
      count(arm)
    #> # A tibble: 2 × 2
    #>   arm       n
    #>   <chr> <int>
    #> 1 Ctl      99
    #> 2 Trt     101

    enrol %>%
      edc_left_join(data1) %>% #oopsie
      assert_no_duplicate() %>%
      count(arm)
    #> Error in `assert_no_duplicate()`:
    #> ! Duplicate on column "subjid" for values 1, 2, 3, 4, 5, 6, 7, 8, 9, and
    #>   10.

> Tip

> This is the Fail Fast principle: you’d better have an error in your R script than in your analysis report.

## Last-news table

If your analysis involves a survival endpoint, you likely have a follow-up dataset that includes the vital status as of the last visit date.

However, in real-world scenarios, this dataset might not be accurately filled, and some patient can have other data after the date of last visit.

The `lastnews_table()` function calculates the actual date of the last recorded information for each patient (`SUBJID`), based on all Date/Datetime columns across all datasets.

Currently, `edc_example()` does not include an explicit table for this scenario, so let’s consider the following example:

- `data3$date10` is your last visit date in your followup dataset. It is the origin you should `prefer` if there is a tie and the reference to identify any inconsistency.

- `data1` is a dataset containing scheduled protocol dates, such as planned medical visits. You should ignore all columns in this dataset, as they do not pertain to the patient’s last known

- `date8` and `date9` are dates when treatments were administered. They imply that the patient was alive at that time. If a date is after `date10`, it means that the survival time is underestimated.

Here is how to parameterize `lastnews_table()` to fit this scenario:

    lastnews_table(prefer="date10", except="data1", show_delta=TRUE) %>%
      mutate(delta=round(delta)) %>%
      arrange(desc(delta))
    #> # A tibble: 200 × 8
    #>    subjid last_date  origin_data origin_col origin_label    preferred_last_date
    #>     <dbl> <date>     <chr>       <chr>      <chr>           <date>
    #>  1      9 2010-08-01 data3       date9      Date at visit 9 2010-07-14
    #>  2     41 2010-07-28 data3       date9      Date at visit 9 2010-07-09
    #>  3     71 2010-07-16 data3       date8      Date at visit 8 2010-06-28
    #>  4    186 2010-07-12 data3       date9      Date at visit 9 2010-06-23
    #>  5     14 2010-07-30 data3       date9      Date at visit 9 2010-07-13
    #>  6     61 2010-07-30 data3       date9      Date at visit 9 2010-07-12
    #>  7     68 2010-08-06 data3       date8      Date at visit 8 2010-07-20
    #>  8    120 2010-07-27 data3       date9      Date at visit 9 2010-07-09
    #>  9     58 2010-07-31 data3       date9      Date at visit 9 2010-07-15
    #> 10     93 2010-07-30 data3       date9      Date at visit 9 2010-07-13
    #> # ℹ 190 more rows
    #> # ℹ 2 more variables: preferred_origin <chr>, delta <drtn>

> Warning

> This table is useful for Overall Survival and data checking. However, you should be very careful when using it for Event-Free Survival: a patient can be alive at that point without having experienced an event.