Skip to content

Job request: 20626

Organisation:
King's College London
Workspace:
early-inflammatory-arthritis
ID:
inhsqezzyninpvke
View repo View project.yaml

This page shows the technical details of what happened when authorised researcher Mark Russell requested one or more actions to be run against real patient data in the project, within a secure environment.

By cross-referencing the indicated Requested Actions with the Pipeline section below, you can infer what security level various outputs were written to. Outputs marked as highly_sensitive can never be viewed directly by a researcher; 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

  • Action:
    create_cohorts
    Status:
    Status: Succeeded
    Job identifier:
    gh2lfsigufdujqhq

Pipeline

Show project.yaml 
version: '3.0'

expectations:
  population_size: 700000

actions:
             
  generate_dataset:
    run: ehrql:v0 generate-dataset analysis/dataset_definition.py --output output/dataset.csv
    outputs:
      highly_sensitive:
        cohort: output/dataset.csv 

  create_cohorts_ehrQL:
    run: stata-mp:latest analysis/000_define_covariates_ehrQL.do
    needs: [generate_dataset]
    outputs:
      highly_sensitive:
        log1: logs/cleaning_dataset_ehrQL.log 
        data1: output/data/file_eia_all_ehrQL.dta

  generate_study_population_allpts:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_allpts
    outputs:
      highly_sensitive:
        cohort: output/input_allpts.csv
     
  generate_study_population:
    run: cohortextractor:latest generate_cohort --study-definition study_definition
    outputs:
      highly_sensitive:
        cohort: output/input.csv
  
  create_cohorts_allpts:
    run: stata-mp:latest analysis/001_define_covariates_allpts.do
    needs: [generate_study_population_allpts]
    outputs:
      highly_sensitive:
        log1: logs/cleaning_dataset_allpts.log 
        data1: output/data/file_eia_allpts.dta        

  create_cohorts:
    run: stata-mp:latest analysis/000_define_covariates.do
    needs: [generate_study_population]
    outputs:
      highly_sensitive:
        log1: logs/cleaning_dataset.log 
        data1: output/data/file_eia_all.dta

  run_baseline_tables_allpts:
    run: stata-mp:latest analysis/101_baseline_characteristics_allpts.do
    needs: [create_cohorts_allpts]
    outputs:
      moderately_sensitive:
        log1: logs/descriptive_tables_allpts.log      
        doc1: output/tables/baseline_allpts.csv      

  run_baseline_tables:
    run: stata-mp:latest analysis/100_baseline_characteristics.do
    needs: [create_cohorts]
    outputs:
      moderately_sensitive:
        log1: logs/descriptive_tables.log   
        doc1: output/tables/baseline_bydiagnosis.csv   
        doc2: output/tables/baseline_byyear.csv 
        doc3: output/tables/referral_bydiag_nomiss.csv   
        doc4: output/tables/referral_byyear_nomiss.csv   
        doc5: output/tables/referral_byregion_nomiss.csv   
        doc6: output/tables/drug_bydiag_miss.csv 
        doc7: output/tables/drug_byyear_miss.csv 
        doc8: output/tables/drug_byyear_ra_miss.csv   
        doc9: output/tables/drug_byyear_psa_miss.csv
        doc10: output/tables/drug_byyear_undiff_miss.csv
        doc11: output/tables/drug_byyearanddisease.csv
        doc12: output/tables/drug_byyearandregion.csv
        doc13: output/tables/diag_count_bymonth.csv
        doc14: output/tables/diag_count_byyear.csv
        doc15: output/tables/appt_count_bymonth.csv
        doc16: output/tables/diag_count_byyear_ethn.csv
        doc17: output/tables/diag_count_byyear_imd.csv
        doc18: output/tables/diag_count_bymonth_female.csv
        doc19: output/tables/diag_count_bymonth_male.csv
        doc20: output/tables/diag_count_byyear_female.csv
        doc21: output/tables/diag_count_byyear_male.csv
        figure1: output/figures/incidence_twoway_rounded.svg
        figure2: output/figures/incidence_twoway_appt.svg
        figure3: output/figures/incidence_twoway_rounded_female.svg
        figure4: output/figures/incidence_twoway_rounded_male.svg
  
  run_itsa_models:
    run: stata-mp:latest analysis/200_itsa_models.do
    needs: [create_cohorts]
    outputs:
      moderately_sensitive:
        log1: logs/itsa_models.log   
        figure1:  output/figures/ITSA_diagnostic_delay_newey.svg
        figure2:  output/figures/ITSA_diagnostic_delay_prais.svg
        figure3:  output/figures/ITSA_diagnostic_delay_GP_newey.svg
        figure4:  output/figures/ITSA_diagnostic_delay_GP_prais.svg
        doc1: output/tables/gp_to_appt_ITSA_table.csv
  
  run_itsa_models_drugs:
    run: stata-mp:latest analysis/201_itsa_models_drugs.do
    needs: [create_cohorts]
    outputs:
      moderately_sensitive:
        log1: logs/itsa_models_drugs.log   
        figure1:  output/figures/ITSA_csDMARD_delay_newey.svg
        figure2:  output/figures/ITSA_csDMARD_delay_prais.svg
        figure3: output/figures/ITSA_csDMARD_delay_newey_sensitivity.svg
        figure4: output/figures/ITSA_csDMARD_delay_prais_sensitivity.svg
        doc1: output/tables/appt_to_csdmard_ITSA_table.csv

  run_box_plots:
    run: stata-mp:latest analysis/300_box_plots.do
    needs: [create_cohorts]
    outputs:
      moderately_sensitive:
        log1: logs/box_plots.log
        figure 1: output/figures/regional_qs1_bar_overall.svg
        figure 2: output/figures/regional_qs1_bar_2019.svg 
        figure 3: output/figures/regional_qs1_bar_2020.svg       
        figure 4: output/figures/regional_qs2_bar_overall.svg  
        figure 5: output/figures/regional_qs2_bar_2019.svg 
        figure 6: output/figures/regional_qs2_bar_2020.svg    
        figure 7: output/figures/regional_qs2_bar_GP_overall.svg  
        figure 8: output/figures/regional_qs2_bar_GP_2019.svg     
        figure 9: output/figures/regional_qs2_bar_GP_2020.svg 
        figure 10: output/figures/regional_qs2_bar_GP_merged.svg
        figure 11: output/figures/regional_qs2_bar_GP_ethnicity.svg
        figure 12: output/figures/regional_qs2_bar_GP_imd.svg
        figure 13: output/figures/regional_csdmard_bar_overall.svg  
        figure 14: output/figures/regional_csdmard_bar_2019.svg     
        figure 15: output/figures/regional_csdmard_bar_2020.svg 
        figure 16: output/figures/regional_csdmard_bar_merged.svg
        figure 17: output/figures/regional_csdmard_bar_ethnicity.svg
        figure 18: output/figures/regional_csdmard_bar_imd.svg

  run_redacted_tables:
    run: stata-mp:latest analysis/400_redacted_tables.do
    needs: [create_cohorts]
    outputs:
      moderately_sensitive:
        log1: logs/redacted_tables.log   
        doc1: output/tables/table_1_rounded_bydiag.csv   
        doc2: output/tables/table_mean_bydiag_rounded.csv 
        doc3: output/tables/table_median_bydiag_rounded.csv   
        doc4: output/tables/table_median_bydiag_rounded_to21.csv   
        doc5: output/tables/ITSA_tables_appt_delay_rounded.csv   
        doc6: output/tables/ITSA_tables_csdmard_delay_rounded.csv 
        doc7: output/tables/drug_byyearanddisease_rounded.csv 
        doc8: output/tables/first_csdmard_rounded.csv   
        doc9: output/tables/drug_byyearandregion_rounded.csv
        doc10: output/tables/referral_byregion_rounded.csv
        doc11: output/tables/consultation_medium_rounded.csv
        doc12: output/tables/table_median_bydiag_rounded_to21_report.csv 
        doc13: output/tables/first_csdmard_rounded_report.csv  

  run_redacted_tables_allpts:
    run: stata-mp:latest analysis/401_redacted_tables_allpts.do
    needs: [create_cohorts_allpts]
    outputs:
      moderately_sensitive:
        log1: logs/redacted_tables_allpts.log   
        doc1: output/tables/table_1_rounded_allpts.csv  

  convert_image_formats:
    run: python:latest python analysis/convert_images.py --input_dir output/figures --output_dir output/figures
    needs: [run_baseline_tables, run_itsa_models, run_itsa_models_drugs, run_box_plots, run_redacted_tables]
    outputs:
      moderately_sensitive:
        figures: output/figures/*.png           

  generate_notebook:
    run: jupyter:latest jupyter nbconvert /workspace/analysis/report.ipynb --execute --to html --template basic --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
    needs: [convert_image_formats,run_baseline_tables, run_itsa_models, run_itsa_models_drugs, run_box_plots, run_redacted_tables]
    outputs:
      moderately_sensitive:
        notebook: output/report.html

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:02:43

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

Job information

Status
Succeeded
Backend
TPP
Workspace
early-inflammatory-arthritis
Requested by
Mark Russell
Branch
main
Force run dependencies
No
Git commit hash
e2fffd8
Requested actions
  • create_cohorts

Code comparison

Compare the code used in this Job Request