Diagnostics for the OMOP perinatal expansion (OMOP) • PETDiagnostics

The goal of PETDiagnostics is to assess feasibility of data sources to perform observational studies on pregnancy related topics using the pregnancy extension tables in OHDSI.

Installation

You can install the development version of PETDiagnostics like so:

install.packages("remotes")
remotes::install_github("oxford-pharmacoepi/PETDiagnostics")

Example

Create a reference to data in the OMOP CDM format

The PETDiagnostics package is designed to work with data in the OMOP CDM format, so our first step is to create a reference to the data using the CDMConnector package. Here we´ll generate an example reference with simulated data (to see how you would create a reference to your database please consult the CDMConnector package documentation).

library(PETDiagnostics)
library(CDMConnector)
#> Warning: package 'CDMConnector' was built under R version 4.2.2
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.2.2
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union


# We first need to create a mock database with a cdm reference
# this function creates a motherTable and a babyTable
cdm<-mockPregnancy(motherTable = NULL,
                          babyTable = NULL,
                          pregnancy_size = 100,
                          fetus_size = 110,
                          seed = 1)

# this is what the table(s) look like
# use the motherTable and/or the babyTable depending on your data
head(cdm$motherTable)
#> # Source:   SQL [6 x 27]
#> # Database: DuckDB 0.5.1 [tburkard@Windows 10 x64:R 4.2.1/:memory:]
#>   pregna…¹ perso…² pregnanc…³ pregnanc…⁴ gesta…⁵ pregn…⁶ pregn…⁷ pregn…⁸ pregn…⁹
#>      <int>   <int> <date>     <date>       <dbl>   <dbl>   <dbl>   <int>   <dbl>
#> 1        1      68 2010-12-05 2011-02-01      58 4092289 4015701 4188539 4095714
#> 2        2      39 2004-09-01 2005-02-21     173 4081422       0 4188540 4053842
#> 3        3       1 2019-10-03 2020-01-19     108 4092289       0 4188540 4242253
#> 4        4      34 2001-05-15 2002-01-08     238  443213       0 4188539 4095714
#> 5        5      87 2010-05-15 2011-01-14     244 4092289 4125611 4188540 4095714
#> 6        6      43 2018-03-21 2018-08-13     145       0       0 4188539 4338692
#> # … with 18 more variables: pregnancy_number_fetuses <int>,
#> #   pregnancy_number_liveborn <int>, prev_pregnancy_gravidity <int>,
#> #   prev_livebirth_number <int>, prev_stillbirth_number <int>,
#> #   prev_miscar_number <int>, prev_top_number <int>, prev_top12_number <int>,
#> #   prev_pregnancy_parity <dbl>, pregnancy_bmi <int>,
#> #   pregnancy_outcome_source_value <chr>,
#> #   pregnancy_mode_delivery_source_value <chr>, pregnancy_folic <int>, …
head(cdm$babyTable)
#> # Source:   SQL [6 x 8]
#> # Database: DuckDB 0.5.1 [tburkard@Windows 10 x64:R 4.2.1/:memory:]
#>   pregnancy_id fetus_id birth_outcome birth_we…¹ birth…² birth…³ birth…⁴ birth…⁵
#>   <chr>        <chr>            <dbl>      <int>   <int>   <int>   <int>   <int>
#> 1 1            1              4092289       2447 4188540 4188539 4188540       6
#> 2 2            2              4092289       1827 4188540 4188540 4188539      10
#> 3 3            3              4092289       1056 4188539 4188539 4188539       0
#> 4 4            4                   NA        786 4188539 4188539 4188539      10
#> 5 5            5              4092289       3716 4188540 4188539 4188539       8
#> 6 6            6               443213       4201 4188540 4188540 4188539       7
#> # … with abbreviated variable names ¹birth_weight, ²birth_con_malformation,
#> #   ³birth_sga, ⁴birth_fgr, ⁵birth_apgar

Execute the diagnostic checks of your table(s)

if both tables are available, all checks are possible

if only the motherTable is available, the “fetusid” and “weightDist” check is not possible, put babyTable = NULL

if only the babyTable is available, only the “overview”, “missing”, “weightDist”, and “bitSet” check is possible, put motherTable = NULL

resultList <- executeChecks (
                          motherTable = cdm$motherTable,               
                          babyTable = cdm$babyTable,                  
                          checks = c("overview","annualOverview","missing", "unknown","gestationalAge","datesAgeDist","outcomeMode",
                                     "fetusesLiveborn","fetusid","weightDist","bitSet"),                       
                          minCellCount = 5,
                          minGestAge_Days = 21,
                          verbose = FALSE)
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> Warning: attributes are not identical across measure variables; they will be
#> dropped
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables
#> Warning: attributes are not identical across measure variables; they will be
#> dropped

Exporting results

resultList is the named list with results

databaseId is the database identifier

outputFolder is the folder to write to

writeResultToDisk (resultList = resultList, databaseId = "mock", outputFolder = tempdir())