Job request: 13520
- Organisation:
- King's College London
- Workspace:
- early-inflammatory-arthritis
- ID:
- hz44jbvduav4da5n
This page shows the technical details of what happened when the 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 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:
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fotd5w3gbt2haveq
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- Job identifier:
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azv4twhmy2bcp7va
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- Job identifier:
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twriu7dgshtsbe6g
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- Job identifier:
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737awbwkhabuqqgf
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- Job identifier:
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hysywskwlw7bxne3
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- Job identifier:
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yktvc4aclbbmqit7
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- Job identifier:
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hjqnxs2v3izwxaab
Pipeline
Show project.yaml
version: '3.0'
expectations:
population_size: 400000
actions:
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
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Created:
-
Started:
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Finished:
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Runtime: 00:06:35
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
- 4111ef7
- Requested actions
-
-
run_baseline_tables
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run_itsa_models
-
run_itsa_models_drugs
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run_box_plots
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run_redacted_tables
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convert_image_formats
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generate_notebook
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Code comparison
Compare the code used in this Job Request