Job request: 5682
- Organisation:
- The London School of Hygiene & Tropical Medicine
- Workspace:
- vaccine-neuro-cohort
- ID:
- sey26kf5g7wegcxf
This page shows the technical details of what happened when the authorised researcher Anna Schultze requested one or more actions to be run against real patient data in the project, within a secure environment.
By cross-referencing the list of jobs with the
pipeline section below, you can infer what
security level
various outputs were written to. Researchers can never directly
view outputs marked as
highly_sensitive
;
they can only request that code runs against them. Outputs
marked as
moderately_sensitive
can be viewed by an approved researcher by logging into a highly
secure environment. Only outputs marked as
moderately_sensitive
can be requested for release to the public, via a controlled
output review service.
Jobs
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- Job identifier:
-
3gmjytxzxtl6kq2v
-
- Job identifier:
-
n6foekunf3dga2h5
-
- Job identifier:
-
mpc4u4gu3unq6f36
-
- Job identifier:
-
kgxtqlhejm66ye3a
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- Job identifier:
-
oy23oepcowla6fex
Pipeline
Show project.yaml
version: '3.0'
expectations:
population_size: 1000
actions:
# EXTRACT DATA FOR EXPOSED AND POTENTIAL CONTROLS
## Extract data on vaccinated people
## NB; This study definition only contains the data needed for matching
generate_exposed_cohort:
run: cohortextractor:latest generate_cohort --study-definition study_definition_exposed
outputs:
highly_sensitive:
cohort: output/input_exposed.csv
## Extract data on potential concurrent controls
generate_concurrent_controls:
run: cohortextractor:latest generate_cohort --study-definition study_definition_concurrent_controls
outputs:
highly_sensitive:
cohort: output/input_concurrent_controls.csv
## Extract data on potential historical controls
# MATCH EXPOSED TO CONTROLS
# Note that exposed and resulting matches need to_c be extracted into separate csvs to allow appropriate covariate information to be extracted
## Match vaccinated people to concurrent controls
match_concurrent:
run: python:latest python analysis/matching.py
needs: [generate_exposed_cohort, generate_concurrent_controls]
outputs:
moderately_sensitive:
matching_report: output/matching_report_concurrent.txt
highly_sensitive:
matched_cases: output/matched_cases_concurrent.csv
matched_matches: output/matched_matches_concurrent.csv
matched_all: output/matched_combined_concurrent.csv
## Match vaccinated people to historical controls
## Extract time-dependent data for matched concurrent controls
add_covariates_concurrent_controls:
run: cohortextractor:latest generate_cohort --study-definition study_definition_complete_concurrent
needs: [match_concurrent]
outputs:
highly_sensitive:
cohort: output/input_complete_concurrent.csv
## Extract time-dependent data for matched historical controls
# CREATE THE REQUIRED POPULATIONS
## ie, merge case and match data, apply remaining inclusions and exclusions
apply_exclusion_criteria:
run: stata-mp:latest analysis/01_apply_exclusion_criteria.do
needs: [add_covariates_concurrent_controls, match_concurrent]
outputs:
moderately_sensitive:
log: output/logs/01_apply_exclusion_criteria.log
highly_sensitive:
interim_data: output/concurrent_cohort.dta
Timeline
-
Created:
-
Started:
-
Finished:
-
Runtime: 62:55:05
These timestamps are generated and stored using the UTC timezone on the TPP backend.
Job information
- Status
-
Failed
- Backend
- TPP
- Workspace
- vaccine-neuro-cohort
- Requested by
- Anna Schultze
- Branch
- main
- Force run dependencies
- Yes
- Git commit hash
- 86b5cf0
- Requested actions
-
-
run_all
-
Code comparison
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