Level 3 checks

R scripts to produce high-level characterization data to benchmark across DAPs and with external resources.

THIS REPOSITORY REMAINS READ-ONLY, AND IT WILL BE NO LONGER ACTIVELY MAINTAINED

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Table of Contents

1. About The Project
2. Level 3 checks
3. Getting Started
   • Prerequisites
   • Installation
   • Subpopulation analysis
   • Uploading results to the online research environment
   • Data characterization study links
   • Current version
4. License
5. Contact

About The Project

ConcePTION aims to build an ecosystem that can use Real World Data (RWD) to generate Real World Evidence (RWE) that may be used for clinical and regulatory decision making. RWE is required to address the big information gap of medication safety in pregnancy.

ConcePTION is designed to be a learning healthcare system (LHS). In the ConcePTION LHS, we have agreed upon a study-independent syntactically harmonized common data model and aim to assess the quality and fitness for purpose of data in this CDM in a study-independent way (for quality and completeness) and in study design and research question-specific ways (for fitness for purpose).

ConcePTION CDM tables:

Level 3 checks

Aims of Level 3 quality checks:
1. To perform high-level data characterization of the ConcePTION CDM instance for each DAP and benchmark across DAPs and with external resources.
a. Assessing medication use in females of childbearing age and in pregnancy.
b. Assessing vaccine exposure in females of childbearing age and in pregnancy.
c. Calculation of incidence rates of events during pregnancy and before/after.
d. Assessing severity of specific maternal conditions.
e. Assessing prenatal and antenatal outcomes in relation to drug exposure for signal generation and signal evaluation.
f. Assessing medication use in the study population.
g. Assessing vaccine exposure in the study population.

Level 3 checks will quantify population and person time in each data source for the source and study population as a whole as well as for subpopulations of interest. Examples of this type of check include: counts of codes extracted to identify each event and exposure of interest, counts of medication prescription and vaccine administrations etc.

The level 3 checks are divided in 8 major steps:

1. Source and study population.
2. Medicines.
3. Vaccines.
4. Diagnoses.
5. Pregnancy.
6. Populations of interest.
7. Health-seeking behaviour and lifestyle factors.
8. EUROCAT indicators.

Getting Started

Follow the steps below to run Level 3 checks in your data.

Prerequisites

R version 4.1.0 (2021-05-18)

Installation

1. Download the ZIP folder and extract the contents.

2. Create a main folder with the name of your project(if you already have done so for Level 1/2 checks skip this step).

3. Inside the main folder create the folder Data characterisation. Put the extracted folder inside.

4. Inside the main folder create a folder named CDMInstances, which will be used to store the .csv files representing the CDM tables(if you already have done so for Level 1/2 checks skip this step).

5. Inside the CDMInstances folder create a folder with the name of your project and inside the latter put all your .csv files(if you already have done so for Level 1/2 checks skip this step).

6. In the folder Level_3_checks_to_be_deployed_v1.0, go to the script 99_path.R and change the variable Studyname(line 6) to the name of your project. Make sure that the name of the folder you have created in the folder CDMInstances and the name of the variable match exactly. Save it.

7. Open the to_run.R script.

8. If your data source contains a birth registry, in line 13 you need to specify the meaning variable which means birth in your SURVEY_OBSERVATIONS table. The example shows birth_registry_mother. This variable will be used to select women who gave birth and classify them as having an end_of_pregnancy code. iIf you do not have a birth registry, then replace line 13 with the following: meanings_birth_registry<-c().

9. To run the Lifestyle script a few variables need to be specified. The information needed is the name of the CDM table where you have saved the information(CDM_table), the name of the CDM column where this information is stored(CDM_column), the original name of your variable(value), the CDM variable where the vocabulary (if there is a vocabulary) is saved(c.voc), the value of the vocabulary(v.voc) and the date variable that stores the date of recording(v.date). In line 30 you can find an example of how to fill out this information. The information needed refers to smoking, alcohol abuse, folic acid use, BMI and SES. Please fill out the information in line 39. If no data is available, delete all information about Lifestyle and replace that with Lifestyle<-list(). If there is missing information for a particular variable, delete the section regaring that variable and leave the others as they are.

10. After everything is complete, select all by using ctrl+A(Windows) or cmmd+A(Mac) and run.

Folder structure

Main folder

• CDMInstances

  • Project_name
    • files.csv

• Data characterisation

  • Level_1_checks_to_be_deployed_v5.2
  • Level_2_checks_to_be_deployed2.0
  • Level_3_to_be_deployed1.0

Subpopulation analysis

A subpopulation analysis can be performed if your data has different provenance(i.e. different levels of the healthcare system such as hospital data and general practitioner data etc). This analysis helps to identify errors for each specific data sub sample. If you already know that your data quality is similar you can skip this analysis.

To run the level 3 checks with subpopulation analysis follow the next steps:

1. Complete the METADATA table accordingly. In type_of_metadata = subpopulations in the column values, add all your subpopulations of interest separated by space. Leave tablename, columnname and other columns empty. Example if you have hospital(HOSP) data and primary care(PC) data you will add HOSP PC to the values column.
2. If you want to analyse the overlap between different subpopulations, add first_subpopulation-sencond_subpopulation in type_of_metadata = subpopulations in the column values Example if you look at the overlap between hospital data and primary care data add HOSP-PC to the values column.
3. In type_of_metadata = op_meaning_sets in the column values specify each meaning set referring to a subpopulation. Separate meaning sets by space In the column other add the name of the subpopulation. Leave tablename and columnname empty. Example if for the primary care data you will add the meaning sets meaningsPC and meaningsPHARMA you will add in the other column, PC and in the values column, meaningsPC meaningsPHARMA.
4. In type_of_metadata = op_meanings_list_per_set in the values column add all the meanings that should be part of a meaning set and in the other column add the name of the meaning set. Leave the tablename and columnname empty. Example if the meaning set meaningsPC contains the meanings primary_care, primary_care_2, and primary_care_3 you will add to the values column primary_care primary_care_2 primary_care_3 and in the other column meaningsPC. Separate values by space.
5. If you want to exclude a specific meaning of a CDM table from a subpopulation, add in type_of_metadata = exclude_meaning in the column tablename the name of the CDM table, in the column other the name of the subpopulation and in the column values the meanings to be excluded. Separate meanings by space. Leave the columnname column empty. Example of you want to exclude the meaning pc_exclude part of the EVENTS table from the subpopulation primary care than you will add EVENTS to the column tablename, PC to the other column and pc_exclude to the values column.
6. You are now ready top run a subpopulation analysis.

Uploading results to the online research environment

Uploading to anDREa

1. In a web browser, Go To: mydre.org.
2. Click on 'Click here to login'. Pick an account and enter password.
3. Click on Workspaces in upper left and then double click on the project workspace.
4. Click on Files tab at top.
5. Double click on 'inbox' folder.
6. Click on 'Level3'.
7. Create a folder by clicking on the folder icon with + on it.
8. Name the folder with the name of the data source, quality check level number and the date of running/uploading. Example if the data source ARS is uploading the level 3 checks output on the 28 September 2021, the folder should be named: ARS_level3_2021_09_28.
9. Click on the folder you created.
10. Click on cloud icon to upload files.
11. Click on select and upload.
12. Open the ForDashboard folder which is located inside Level_3_to_be_deployed1.0/g_output/. Hold down control and select all files within your prepared folder (can only do one folder at a time).
13. Click on open.
14. When it asks to confirm: "Would like to upload the inbox?" select 'OK'.
15. Note: It may take many minutes for your upload to complete. You should receive an email once they are uploaded.
16. If you find that your files are not in the corresponding level directory, check if the files are in the inbox and move them to the corresponding level directory.

Data characterization study links

Level 1 checks: Checking the integrity of the ETL procedure.
Level 2 checks: Checking the logical relationship of the CDM tables.
Level 3 checks: Benchamrking across DAPs and external sources.

Current version

The current version of the script is 1.0.

License

Distributed under the BSD 2-Clause License License. See LICENSE for more information.

Contact

Vjola Hoxhaj - v.hoxhaj@umcutrecht.nl
Roel Elbers - R.J.H.Elbers@umcutrecht.nl
Ema Alsina - palsinaaer@gmail.com

Project Link: https://github.com/IMI-ConcePTION/Level-3-checks