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Job request: 19165

Organisation:
Bennett Institute
Workspace:
opioids-covid-research
ID:
bes5m65dxsltigg2
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Andrea Schaffer 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

Pipeline

Show project.yaml 
######################################
# This script defines the project pipeline - it specifies the execution orders for all the code in this
# repo using a series of actions.
######################################

version: '3.0'

expectations:
  population_size: 10000

actions:

  generate_dataset_apr22:
    run: ehrql:v0 generate-dataset analysis/define_dataset_apr22.py --output output/dataset_apr22.csv.gz
    outputs:
      highly_sensitive:
        cohort: output/dataset_apr22.csv.gz  
  
  generate_dataset_may22:
    run: ehrql:v0 generate-dataset analysis/define_dataset_may22.py --output output/dataset_may22.csv.gz
    outputs:
      highly_sensitive:
        cohort: output/dataset_may22.csv.gz

  generate_dataset_jun22:
    run: ehrql:v0 generate-dataset analysis/define_dataset_jun22.py --output output/dataset_jun22.csv.gz
    outputs:
      highly_sensitive:
        cohort: output/dataset_jun22.csv.gz

  measures_overall:
    run: ehrql:v0 generate-measures analysis/measures_overall.py --output output/measures_overall.csv
    outputs:
      moderately_sensitive:
        measure_csv: output/measures_overall.csv
  
  measures_overall_nocancer:
    run: ehrql:v0 generate-measures analysis/measures_overall_nocancer.py --output output/measures_overall_nocancer.csv
    outputs:
      moderately_sensitive:
        measure_csv: output/measures_overall_nocancer.csv
    
  measures_demo:
    run: ehrql:v0 generate-measures analysis/measures_demo.py --output output/measures_demo.csv
    outputs:
      moderately_sensitive:
        measure_csv: output/measures_demo.csv

  measures_demo_nocancer:
    run: ehrql:v0 generate-measures analysis/measures_demo_nocancer.py --output output/measures_demo_nocancer.csv
    outputs:
      moderately_sensitive:
        measure_csv: output/measures_demo_nocancer.csv

  measures_type:
    run: ehrql:v0 generate-measures analysis/measures_type.py --output output/measures_type.csv
    outputs:
      moderately_sensitive:
        measure_csv: output/measures_type.csv

  measures_type_nocancer:
    run: ehrql:v0 generate-measures analysis/measures_type_nocancer.py --output output/measures_type_nocancer.csv
    outputs:
      moderately_sensitive:
        measure_csv: output/measures_type_nocancer.csv

  measures_sens:
    run: ehrql:v0 generate-measures analysis/measures_sens.py --output output/measures_sens.csv
    outputs:
      moderately_sensitive:
        measure_csv: output/measures_sens.csv
        
  
  # ## Cohort data
  # generate_study_population_1:
  #   run: cohortextractor:latest generate_cohort
  #     --study-definition study_definition
  #     --index-date-range "2018-01-01 to 2018-12-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/input_*.csv

  # generate_study_population_2:
  #   run: cohortextractor:latest generate_cohort 
  #     --study-definition study_definition
  #     --index-date-range "2019-01-01 to 2019-12-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/input*.csv

  # generate_study_population_3:
  #   run: cohortextractor:latest generate_cohort 
  #     --study-definition study_definition
  #     --index-date-range "2020-01-01 to 2020-12-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/inpu*.csv

  # generate_study_population_4:
  #   run: cohortextractor:latest generate_cohort 
  #     --study-definition study_definition
  #     --index-date-range "2021-01-01 to 2021-12-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/inp*.csv
  
  # generate_study_population_5:
  #   run: cohortextractor:latest generate_cohort 
  #     --study-definition study_definition
  #     --index-date-range "2022-01-01 to 2022-03-01 by month" 
  #     --output-dir=output 
  #     --output-format=csv
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/in*.csv

  # ## Ethnicity      
  # generate_ethnicity_cohort:
  #   run: >
  #     cohortextractor:latest generate_cohort
  #       --study-definition study_definition_ethnicity
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/input_ethnicity.csv


  # # Data processing ----
  
  # ## Add ethnicity
  # join_cohorts:
  #   run: >
  #     cohort-joiner:v0.0.48
  #       --lhs output/input_*.csv
  #       --rhs output/input_ethnicity.csv
  #       --output-dir output/data
  #   needs: [generate_study_population_1,  generate_study_population_2, 
  #     generate_study_population_5, generate_study_population_3, 
  #     generate_study_population_4, generate_ethnicity_cohort]
  #   outputs:
  #     highly_sensitive:
  #       cohort: output/data/input_*.csv 


  # ## Generate measures - full population
  # generate_measures:
  #   run: >
  #     cohortextractor:latest generate_measures 
  #       --study-definition study_definition
  #       --output-dir output/data
  #   needs: [join_cohorts]
  #   outputs:
  #     moderately_sensitive:
  #       measure_csv: output/data/measure_*.csv


  ## Process data - time series
  process_data_ts:
   run: r:latest analysis/process/process_data_ts.R
   needs: [measures_overall, measures_overall_nocancer, measures_demo, measures_demo_nocancer, measures_type, measures_type_nocancer, measures_sens]
   outputs:
      moderately_sensitive:
        ts_csv: output/processed/final_*.csv

  ## Process data - table
  process_data_table:
   run: r:latest analysis/process/process_data_table.R
   needs: [generate_dataset_apr22, generate_dataset_may22, generate_dataset_jun22]
   outputs:
      highly_sensitive:
        table_csv: output/processed/final*.csv


  # # Results ---

  # ## Time series
  # timeseries:
  #   run: r:latest analysis/descriptive/time_series_stand.R
  #   needs: [process_data_ts]
  #   outputs:
  #     moderately_sensitive:
  #       table: output/time series/ts_*.csv  
  
  # ## Time series graphs
  # # graphs:
  # #   run: r:latest analysis/descriptive/graphs.R
  # #   needs: [timeseries]
  # #   outputs:
  # #     moderately_sensitive:
  # #       plot: output/time series/graphs/graph*.png

  # ## Table 
  table:
    run: r:latest analysis/descriptive/table_stand.R
    needs: [process_data_table]
    outputs:
      moderately_sensitive:
        table: output/tables/table_*.csv

Timeline

  • Created:

  • Started:

  • Runtime: 00:28:15

These timestamps are generated and stored using the UTC timezone on the TPP backend.

Job information

Status
Failed
Backend
TPP
Workspace
opioids-covid-research
Requested by
Andrea Schaffer
Branch
main
Force run dependencies
Yes
Git commit hash
71403f9
Requested actions
  • run_all

Code comparison

Compare the code used in this Job Request