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Job request: 22355

Organisation:
The London School of Hygiene & Tropical Medicine
Workspace:
openprompt-hrqol
ID:
glb3qpoyey3qhcv4
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:
    imputed_models
    Status:
    Status: Succeeded
    Job identifier:
    fun6sp6snrhzjjqt

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
        rounded_demograpics: output/tables/table1_demographic_rounded.csv
        questionnaire_data: output/tables/table1_questions.csv
        rounded_questions: output/tables/table1_questions_rounded.csv
        long_covid_dx: output/tables/long-covid-dx.csv
        rounded_dx: output/tables/long-covid-dx-rounded.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:
        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
        baseline_regression_missing: output/tables/twopart-model-missing.csv
        longitudinal_models: output/tables/longit-model.csv
        attrition_models: output/tables/selective_attrition.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_linear: output/tables/mixed-models.csv
        proms_mixed: output/tables/mixed-proms.csv
        proms_odds: output/figures/proms_odds.svg
        proms_demographic: output/figures/demo_odds.svg
        demographics_adjust_coefs: output/figures/demo_proms_coefs.svg
        proms_adjust_ceofs: output/figures/proms_coefs.svg
        glm_log: output/mixed-glm.log

  estimate_qalys:
    run: >
      stata-mp:latest analysis/op_qalys.do
    needs: [combine_linked_tpp]
    outputs:
      moderately_sensitive:
        utility_scores: output/tables/utility-scores.csv
        EQ5D_by_longcovd: output/figures/EQ5D_longcovid.svg
        EQ5D_by_surveys: output/figures/EQ5D_surveys.svg
        utility_by_survey: output/figures/utility_survey_response.svg
        selective_attrition: output/figures/EQ5D_surveys_att.svg
        QALY_by_age: output/figures/QALM_losses_age.svg
        qaly_log: output/qaly-estimates.log

  imputed_models:
    run: >
      stata-mp:latest analysis/op_imputed.do
    needs: [combine_linked_tpp]
    outputs:
      moderately_sensitive:
        mi_log: output/mi_models.log
        mi_socio_odds: output/figures/socio_miodds.svg
        mi_melogit: output/figures/mi_melogit.svg
        mixed_mi_demographic: output/figures/mixed_micoefs.svg
        mixed_mi_socio: output/figures/socio_micoefs.svg
        mi_models: output/tables/mi-model.csv
        mi_prom_odds: output/figures/demo_miodds.svg
        mi_prom_coefs: output/figures/proms_micoefs.svg
        mi_prom_models: output/tables/mi-proms.csv

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 02:23:24

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
49b8e3a
Requested actions
  • imputed_models

Code comparison

Compare the code used in this Job Request