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Job request: 9552

Organisation:
Bennett Institute
Workspace:
covid_mortality_over_time
ID:
36shurxr2q54s27k
View repo View project.yaml

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

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level the outputs were written to.

The output security levels are:

  • highly_sensitive
    • Researchers can never directly view these outputs
    • Researchers can only request code is run against them
  • moderately_sensitive
    • Can be viewed by an approved researcher by logging into a highly secure environment
    • These are the only outputs that can be requested for public release via a controlled output review service.

Jobs

Pipeline

Show project.yaml 
version: '3.0'

expectations:
  population_size: 1000

actions:

# Extract data
# When argument --index-date-range is changed, change has to be made in ./analysis/config.json too
  generate_study_population:
    run: >
      cohortextractor:latest generate_cohort 
        --study-definition study_definition 
        --skip-existing 
        --output-format=csv.gz 
        --index-date-range "2020-03-01 to 2022-02-01 by month"
    outputs:
      highly_sensitive:
        cohort: output/input_*.csv.gz

# Extract ethnicity
  generate_study_population_ethnicity:
    run: >
      cohortextractor:latest generate_cohort 
        --study-definition study_definition_ethnicity 
        --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_ethnicity.csv.gz

# Join data
  join_cohorts:
    run: >
      cohort-joiner:v0.0.7
        --lhs output/input_202*.csv.gz
        --rhs output/input_ethnicity.csv.gz
        --output-dir=output/joined
    needs: [generate_study_population, generate_study_population_ethnicity]
    outputs:
      highly_sensitive:
        cohort: output/joined/input_202*.csv.gz

# Calculate mortality rates (crude + subgroup specific)
  calculate_measures:
    run: >
      cohortextractor:latest generate_measures 
        --study-definition study_definition
        --skip-existing
        --output-dir=output/joined
    needs: [join_cohorts]
    outputs:
      moderately_sensitive:
        measure: output/joined/measure_*_mortality_rate.csv

# Calculate mortality rates ckd_rrt subgroup
  calculate_measures_ckd_rrt:
    run: r:latest analysis/measures_calc_ckd_rrt.R
    needs: [join_cohorts]
    outputs:
      moderately_sensitive:
        measure: output/joined/measure_ckd_rrt_mortality_rate.csv

# Redact rates
  redact_rates:
    run: r:latest analysis/utils/redact_rates.R
    needs: [calculate_measures, calculate_measures_ckd_rrt]
    outputs:
      moderately_sensitive:
        csvs: output/rates/redacted/*_redacted.csv       

# Standardise crude mortality rate
  standardise_crude_rates:
    run: r:latest analysis/crude_rates_standardise.R
    needs: [redact_rates]
    outputs:
      moderately_sensitive:
        csvs: output/rates/standardised/crude_*std.csv 

# Standardise subgroup specific mortality rates
  standardise_subgroup_rates:
    run: r:latest analysis/subgroups_rates_standardise.R
    needs: [redact_rates]
    outputs:
      moderately_sensitive:
        csvs: output/rates/standardised/*_std.csv

# Process subgroup specific mortality rates
  process_subgroup_rates:
    run: r:latest analysis/utils/process_rates.R
    needs: [standardise_subgroup_rates, standardise_subgroup_rates]
    outputs:
      moderately_sensitive:
        csvs: output/rates/processed/*.csv

# Calculate standardised rate ratios
  calculate_rate_ratios:
    run: r:latest analysis/subgroups_ratios.R
    needs: [standardise_subgroup_rates, process_subgroup_rates]
    outputs:
      moderately_sensitive:
        csvs: output/ratios/*.csv

# Plot and save graphs depicting the crude rates
  visualise_crude_rates:
    run: r:latest analysis/crude_rates_visualise.R
    needs: [standardise_crude_rates]
    outputs:
      moderately_sensitive:
        pngs: output/figures/rates_crude/*.png

# Plot and save graphs depicting the subgroup specific mortality rates
  visualise_subgroup_rates:
    run: r:latest analysis/subgroups_rates_visualise.R
    needs: [standardise_subgroup_rates, process_subgroup_rates]
    outputs:
      moderately_sensitive:
        pngs: output/figures/rates_subgroups/*.png

# Plot and save graphs depicting the subgroup specific mortality ratios
  visualise_subgroup_ratios:
    run: r:latest analysis/subgroups_ratios_visualise.R
    needs: [calculate_rate_ratios]
    outputs:
      moderately_sensitive:
        pngs: output/figures/ratios_subgroups/*.png

# SECOND PART OF STUDY
  generate_study_population_wave1:
    run: >
      cohortextractor:latest generate_cohort 
        --study-definition study_definition_wave1 
        --skip-existing 
        --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_wave1.csv.gz

  generate_study_population_wave2:
    run: >
      cohortextractor:latest generate_cohort 
        --study-definition study_definition_wave2
        --skip-existing 
        --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_wave2.csv.gz

  generate_study_population_wave3:
    run: >
      cohortextractor:latest generate_cohort 
        --study-definition study_definition_wave3
        --skip-existing 
        --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_wave3.csv.gz

# Join data
  join_cohorts_waves:
    run: >
      cohort-joiner:v0.0.7
        --lhs output/input_wave*.csv.gz
        --rhs output/input_ethnicity.csv.gz
        --output-dir=output/joined
    needs: [generate_study_population_wave1, generate_study_population_wave2, generate_study_population_wave3, generate_study_population_ethnicity]
    outputs:
      highly_sensitive:
        cohort: output/joined/input_wave*.csv.gz

# Process data
  process_data:
    run: r:latest analysis/data_process.R
    needs: [join_cohorts_waves]
    outputs:
      highly_sensitive: 
        rds: output/processed/input_wave*.rds

# Skim data
  skim_data_wave1:
    run: r:latest analysis/data_skim.R output/processed/input_wave1.rds output/data_properties
    needs: [process_data]
    outputs: 
      moderately_sensitive:
        txt1: output/data_properties/input_wave1_skim.txt
        txt2: output/data_properties/input_wave1_coltypes.txt
        txt3: output/data_properties/input_wave1_tabulate.txt

  skim_data_wave2:
    run: r:latest analysis/data_skim.R output/processed/input_wave2.rds output/data_properties
    needs: [process_data]
    outputs: 
      moderately_sensitive:
        txt1: output/data_properties/input_wave2_skim.txt
        txt2: output/data_properties/input_wave2_coltypes.txt
        txt3: output/data_properties/input_wave2_tabulate.txt

  skim_data_wave3:
    run: r:latest analysis/data_skim.R output/processed/input_wave3.rds output/data_properties
    needs: [process_data]
    outputs: 
      moderately_sensitive:
        txt1: output/data_properties/input_wave3_skim.txt
        txt2: output/data_properties/input_wave3_coltypes.txt
        txt3: output/data_properties/input_wave3_tabulate.txt

# Create table one
  create_table_one:
    run: r:latest analysis/table_one.R
    needs: [process_data]
    outputs:
      moderately_sensitive: 
        html: output/tables/table1.html

# Vaccine coverage
  calc_vax_cov:
    run: r:latest analysis/vaccine_coverage_calc.R
    needs: [process_data]
    outputs: 
      moderately_sensitive:
        csvs: output/tables/wave*_vax_coverage.csv

# Incidence rates (crude)
  calc_irs:
    run: r:latest analysis/waves_irs.R
    needs: [process_data]
    outputs: 
      moderately_sensitive:
        csvs: output/tables/wave*_ir.csv
        csv: output/tables/ir_crude.csv

# Incidence rates (crude)
  calc_irs_std:
    run: r:latest analysis/waves_std_irs.R
    needs: [process_data]
    outputs: 
      moderately_sensitive:
        csvs: output/tables/wave*_ir_std.csv

# Kaplan-Meier
  create_kaplan_meier:
    run: r:latest analysis/waves_kaplan_meier.R
    needs: [process_data]
    outputs:
      moderately_sensitive:
        pngs: output/figures/kaplan_meier/wave*_*.png

# COX ph models
  model_cox_ph:
    run: r:latest analysis/waves_model_survival.R
    needs: [process_data]
    outputs: 
      moderately_sensitive:
        csvs1: output/tables/wave*_effect_estimates.csv
        csvs2: output/tables/wave*_ph_tests.csv
        csvs3: output/tables/wave*_log_file.csv

# Create table two
  create_table_two:
    run: r:latest analysis/table_two.R
    needs: [model_cox_ph]
    outputs:
      moderately_sensitive: 
        html: output/tables/table2.html

# Tidy absrisks (IRs) for viz
  tidy_absrisks_for_viz:
    run: r:latest analysis/absrisks_tidy_for_viz.R
    needs: [calc_irs, calc_irs_std, calc_vax_cov]
    outputs:
      moderately_sensitive: 
        csv: output/tables/absrisks_for_viz_tidied.csv

# Tidy relrisks (HRs) for viz
  tidy_relrisks_for_viz:
    run: r:latest analysis/relrisks_tidy_for_viz.R
    needs: [model_cox_ph, calc_vax_cov]
    outputs:
      moderately_sensitive: 
        csv: output/tables/relrisks_for_viz_tidied.csv

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:06:44

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

Job request

Status
Succeeded
Backend
TPP
Workspace
covid_mortality_over_time
Requested by
Linda Nab
Branch
main
Force run dependencies
No
Git commit hash
cdd706b
Requested actions
  • tidy_absrisks_for_viz

Code comparison

Compare the code used in this job request