Format of Participant-Level Data

There are three options for participant-level data format from PIC-SURE:

1. Dataframe or CSV

2. Time Series Data

3. PFB

1. Dataframe or CSV Format

Participant-level data brought to an analysis platform using the Dataframe or CSV format will be exported in a single table. In this table, each row represents a participant and each column represents a variable. The variables included in the table are those added as filters to the query and exported with the "Add Variable" action.

Example Table: Dataframe Format

patient_id	\Nhanes\demographics\SEX\	\Nhanes\demographics\AGE\	\Nhanes\laboratory\acrylamide\
1001	male	31	Yes
1003	male	56	No
1004	female	83	Yes
1005	female	26	Yes

• patient_id is a PIC-SURE-generated participant identifier.

• Each column is labeled with the concept path of the variable.

2. Time Series Data

3. Portable Format for Biomedical Data (PFB)

Participant-level data brought to an analysis platform using the PFB format will be handed off in a single file with two tables: the data and data dictionary tables.

The data will be labeled as pic_sure_patients_[dataset ID] and show the participant-level data from PIC-SURE. The columns of this table are the variables, which are labeled as the PIC-SURE concept paths.

The data dictionary will be labeled as "pic_sure_data_dicitonary_[dataset ID]" and will contain information about the variables that have been exported. This includes information about each variable, such as the concept path, description, and display name. The data dictionary also includes DRS URIs, or links to the original data file, which can be used to access the files for further analysis in BDC analysis platforms.

Example Table: Data Table of PFB

patient_id	\example_study\demographics\sex\	\example_study\demographics\age\	\example_study\exam1\asthma\
1001	Male	31	Never had asthma
1003	Male	56	Currently has asthma
1004	Female	83
1005	Female	26	Currently has asthma

• patient_id is a PIC-SURE-generated participant identifier.

• Each column is labeled with the variable's concept path. For many BDC studies, this is formatted as \phs (study accession number)\pht (dataset accession number)\phv (variable accession number)\variable name\. For more information, please refer to Data Organization in BDC-PIC-SURE.

A tip with Data "Missing-ness":

In PIC-SURE output, an empty cell indicates that there is no data available for that variable and participant. This is demonstrated with participant 1004 above; there is an empty cell in the asthma column. This means that there is no information available for that participant for asthma status.

This is different than cells with NA values. If a cell contains NA, this was recorded by the study submitters. Depending on the context of the NA value, this could be useful information for analysis.

Example Table: Data Dictionary Table of PFB

concept_path	dataset	description	display_name	DRS_URI
\example_study\demographics\sex\	example_study	Participant sex recorded by the study	Sex	drs://example.com/unqiueID123
\example_study\demographics\age\	example_study	Participant age recorded by the study	Age	drs://example.com/uniqueID123
\example_study\exam1\asthma\	example_study	Exam 1: What is your current asthma status?	Asthma status	drs://example.com/uniqueID456

• Each row of the data dictionary table corresponds to a column in the data table.

• DRS URIs link to the study files from which the variable originated.