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Job request: 20263

Organisation:
The London School of Hygiene & Tropical Medicine
Workspace:
openprompt-hrqol
ID:
64anfv266wns2tav
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Oliver Carlile 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

  • Action:
    twopart_models
    Status:
    Status: Succeeded
    Job identifier:
    txiztni2wbp6f7oe

Pipeline

Show project.yaml 
version: '3.0'

expectations:
 population_size: 10000

actions:

  create_dummy_data: 
    run: >
      ehrql:v0
        create-dummy-tables 
        analysis/model_questions/dataset_definition.py output/dummydata 
        -- 
        --day=0
    outputs: 
      highly_sensitive:
        openprompt_dummy: output/dummydata/open_prompt.csv

  edit_dummy_data:
    run: > 
      r:latest
        analysis/dummy_data_editing/edit_automatic_dummy_data.R
    needs: [create_dummy_data]
    outputs: 
      highly_sensitive: 
        openprompt_dummy_edited: output/dummydata/dummy_edited/open_prompt.csv

  generate_openprompt_survey1: 
    run: >
      ehrql:v0
        generate-dataset 
        analysis/model_questions/dataset_definition.py 
        --output output/openprompt_survey1.csv
        --dummy-tables output/dummydata/dummy_edited
        --
        --day=0
        --window=5
    needs: [edit_dummy_data]
    outputs:
      highly_sensitive:
        openprompt_survey1: output/openprompt_survey1.csv

  generate_openprompt_survey2: 
    run: >
      ehrql:v0
        generate-dataset 
        analysis/model_questions/dataset_definition.py 
        --output output/openprompt_survey2.csv
        --dummy-tables output/dummydata/dummy_edited
        --
        --day=30
        --window=5
    needs: [edit_dummy_data]
    outputs:
      highly_sensitive:
        openprompt_survey2: output/openprompt_survey2.csv

  generate_openprompt_survey3: 
    run: >
      ehrql:v0
        generate-dataset 
        analysis/model_questions/dataset_definition.py 
        --output output/openprompt_survey3.csv
        --dummy-tables output/dummydata/dummy_edited
        --
        --day=60
        --window=5
    needs: [edit_dummy_data]
    outputs:
      highly_sensitive:
        openprompt_survey3: output/openprompt_survey3.csv

  generate_openprompt_survey4: 
    run: >
      ehrql:v0
        generate-dataset 
        analysis/model_questions/dataset_definition.py 
        --output output/openprompt_survey4.csv
        --dummy-tables output/dummydata/dummy_edited
        --
        --day=90
        --window=5
    needs: [edit_dummy_data]
    outputs:
      highly_sensitive:
        openprompt_survey4: output/openprompt_survey4.csv

  combine_openprompt:
    run: >
      r:latest analysis/001_datacombine.R
    needs: [generate_openprompt_survey1, generate_openprompt_survey2, generate_openprompt_survey3, generate_openprompt_survey4]
    outputs: 
      highly_sensitive: 
        openprompt_combined: output/openprompt_raw.csv.gz
        openprompt_combined_stata: output/op_stata.dta
      moderately_sensitive:
        openprompt_raw_skim: output/data_properties/op_raw_skim.txt
        openprompt_raw_tab: output/data_properties/op_raw_tabulate.txt
        openprompt_mapped_skim: output/data_properties/op_mapped_skim.txt
        openprompt_mapped_tab: output/data_properties/op_mapped_tabulate.txt
        check_days_after_baseline: output/data_properties/sample_day_lags.pdf
        indexdates: output/data_properties/index_dates.pdf
        table1: output/tab1_baseline_description.html
        raw_summ_base_s: output/data_properties/op_baseline_skim.txt
        raw_summ_base_t: output/data_properties/op_baseline_tabulate.txt
        raw_summ_survey1_s: output/data_properties/op_survey1_skim.txt
        raw_summ_survey1_t: output/data_properties/op_survey1_tabulate.txt
        raw_summ_survey2_s: output/data_properties/op_survey2_skim.txt
        raw_summ_survey2_t: output/data_properties/op_survey2_tabulate.txt
        raw_summ_survey3_s: output/data_properties/op_survey3_skim.txt
        raw_summ_survey3_t: output/data_properties/op_survey3_tabulate.txt
        raw_summ_survey4_s: output/data_properties/op_survey4_skim.txt
        raw_summ_survey4_t: output/data_properties/op_survey4_tabulate.txt
        survey_date_consistency: output/data_properties/survey_date_consistency.csv
        survey_date_consistency_summary: output/data_properties/survey_date_consistency_summary.csv

  generate_openprompt_dataset:
    run: >
      stata-mp:latest analysis/op_combined.do
    needs: [combine_openprompt]
    outputs:
      highly_sensitive:
        data: output/openprompt_dataset.dta
        log: output/open-prompt-combine.log

  extract_linked_tpp_info: 
    run: >
      ehrql:v0
        generate-dataset 
        analysis/add_tpp_data.py 
        --output output/openprompt_linked_tpp.csv.gz
    outputs:
      highly_sensitive:
        openprompt_survey1: output/openprompt_linked_tpp.csv.gz

  import_linked_tpp: 
    run:   
      r:latest analysis/002_import_linked_tpp.R
    needs: [extract_linked_tpp_info]
    outputs: 
      moderately_sensitive:
        openprompt_tpp_skim: output/data_properties/op_tpp_skim.txt
        openprompt_tpp_tab: output/data_properties/op_tpp_tabulate.txt

  combine_linked_tpp:
    run: > 
      stata-mp:latest analysis/op_tpp_linked.do
    needs: [generate_openprompt_dataset, extract_linked_tpp_info]
    outputs: 
      highly_sensitive:
        data: output/op_tpp_linked.dta
        logs: output/linked-tpp.log

  gen_baseline_tables:
    run: >
      stata-mp:latest analysis/op_table1.do
    needs: [combine_linked_tpp]
    outputs:
      moderately_sensitive:
        demographic_data: output/tables/table1_demographic.csv
        questionnaire_data: output/tables/table1_questions.csv
        long_covid_dx: output/tables/long-covid-dx.csv
        utility_score: output/figures/baseline_EQ5D_utility.svg
        disutility_score: output/figures/baseline_EQ5D_disutility.svg
        question_responses: output/figures/baseline_EQ5D_responses.svg
        question_percents: output/figures/baseline_EQ5D_percentage.svg
        vas_ncovids: output/figures/VAS_by_covids.svg
        vas_nvaccines: output/figures/VAS_by_vaccines.svg
        facit_fscore: output/figures/facit_baseline.svg
        log_tables: output/op-baseline-table1.log

  twopart_models:
    run: >
      stata-mp:latest analysis/op_modelling.do
    needs: [combine_linked_tpp]
    outputs: 
      moderately_sensitive:
        EQ5D_by_longcovd: output/figures/EQ5D_longcovid.svg
        demographic_indicators: output/figures/mixed_odds_ratio.svg
        demographic_coefficients: output/figures/mixed_coefs.svg
        socioeconomic_indicators: output/figures/socio_odds.svg
        socioeconomic_coefficients: output/figures/socio_coefs.svg
        baseline_regression: output/tables/twopart-model.csv
        longitudinal_models: output/tables/longit-model.csv
        log_regression: output/models.log

  mixed_models:
    run: >
      stata-mp:latest analysis/op_mixed_models.do
    needs: [combine_linked_tpp]
    outputs:
      moderately_sensitive:
        mixed_glm: output/tables/mixed-models.csv
        # proms_mixed: output/tables/mixed-proms.csv
        glm_log: output/mixed-glm.log

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 02:10:12

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

Job information

Status
Succeeded
Backend
TPP
Workspace
openprompt-hrqol
Requested by
Oliver Carlile
Branch
main
Force run dependencies
No
Git commit hash
b5af565
Requested actions
  • twopart_models

Code comparison

Compare the code used in this Job Request