post-covid-neurodegenerative

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Details of the purpose and any published outputs from this project can be found at the link above.

The contents of this repository MUST NOT be considered an accurate or valid representation of the study or its purpose. This repository may reflect an incomplete or incorrect analysis with no further ongoing work. The content has ONLY been made public to support the OpenSAFELY open science and transparency principles and to support the sharing of re-usable code for other subsequent users. No clinical, policy or safety conclusions must be drawn from the contents of this repository.

Repository navigation

• Detailed protocols are in the protocol folder.

  • post-covid-events-neurodegenerative contains the outcome-specific elements necessary to implement post-covid-events-ehrql.

• If you are interested in how we defined our code lists, look in the codelists folder.

• Analyses scripts are in the analysis directory:

  • Dataset definition scripts are in the dataset_definition directory:

    • If you are interested in how we defined our variables, we use the variable script variable_helper_fuctions to define functions that generate variables. We then apply these functions in variables_cohorts to create a dictionary of variables for cohort definitions, and in variables_dates to create a dictionary of variables for calculating study start dates and end dates.
    • If you are interested in how we defined study dates (e.g., index and end dates), these vary by cohort and are described in the protocol. We use the script dataset_definition_dates to generate a dataset with all required dates for each cohort. This script imported all variables generated from variables_dates.
    • If you are interested in how we defined our cohorts, we use the dataset definition script dataset_definition_cohorts to define a function that generates cohorts. This script imports all variables generated from variables_cohorts using the patient's index date, the cohort start date and the cohort end date. This approach is used to generate three cohorts: pre-vaccination, vaccinated, and unvaccinated—found in dataset_definition_prevax, dataset_definition_vax, and dataset_definition_unvax, respectively.

  • Dataset cleaning scripts are in the dataset_clean directory:

    • This directory also contains all the R scripts that process, describe, and analyse the extracted data.
    • dataset_clean is the core script which executes all the other scripts in this folder
    • fn-preprocess is the function carrying out initial preprocessing, formatting columns correctly
    • fn-modify_dummy is called from within fn-preprocess.R, and alters the proportions of dummy variables to better suit analyses
    • fn-inex is the inclusion/exclusion function
    • fn-qa is the quality assurance function
    • fn-ref is the function that sets the reference levels for factors

  • Table 1 scripts are in the table1 directory:

    • This directory contains a single script: table1.R. This script works with the output of dataset_clean to describe the patient characteristics.

  • Modelling scripts are in the model directory:

    • make_model_input.R works with the output of dataset_clean to prepare suitable data subsets for Cox analysis. Combines each outcome and subgroup in one formatted .rds file.
    • fn-prepare_model_input.R is a companion function to make_model_input.R which handles the interaction with active_analyses.rds.
    • cox-ipw is a reusable action which uses the output of make_model_input.R to fit a Cox model to the data. (NB: It is not a file in the server)

  • Output scripts are in the make_output directory:

    • make_model_output.R combines all the Cox results in one formatted .csv file per subgroup.
    • make_other_output.R combines cohort-specific outputs (e.g. the table1 outputs) into 1 .csv file.
    • make_aer_input.R generates summary statistics by age and sex required for AER (Absolute Excess Risk) estimation for each outcome (using the model input files for the main analysis generated from make_model_input).

• The active_analyses contains a list of active analyses.

• The project.yaml defines run-order and dependencies for all the analysis scripts. This file should not be edited directly. To make changes to the yaml, edit and run the create_project_actions.R script which generates all the actions.

• Descriptive and Model outputs, including figures and tables are in the released_outputs directory.

Outputs

Outputs follow OpenSAFELY naming conventions related to suppression rules by adding the suffix "_midpoint6". The suffix "_midpoint6_derived" means that the value(s) are derived from the midpoint6 values. Detailed information regarding naming conventions can be found here.

output/table1/table1_*.csv

Variable	Description
Characteristic	patient characteristic under consideration
Subcharacteristic	patient sub characteristic under consideration
N [midpoint6 derived]	number of people with characteristic
(%) [midpoint6 derived]	% of total people with characteristic
COVID-19 [diagnoses midpoint6]	number of people with characteristic and COVID-19

output/table2/table2_*.csv

Variable	Description
name	unique identifier for analysis
cohort	cohort used for the analysis
exposure	exposure used for the analysis
outcome	outcome used for the analysis
analysis	string to identify whether this is the ‘main’ analysis or a subgroup
unexposed_person_days	number of person days before or without exposure in the analysis
unexposed_events_midpoint6	number of unexposed people with the outcome in the analysis
exposed_person_days	number of person days after exposure in the analysis
exposed_events_midpoint6	number of exposed people with the outcome in the analysis
total_person_days	number of person days in the analysis
total_events_midpoint6_derived	number of people with the outcome in the analysis
day0_events_midpoint6	number of people with the exposure and outcome on the same day
total_exposed_midpoint6	number of people with the exposure in the analysis
sample_size_midpoint6	number of people in the analysis

output/make_output/model_output.csv

Variable	Description
name	unique identifier for analysis
cohort	cohort used for the analysis
outcome	outcome used for the analysis
analysis	string to identify whether this is the ‘main’ analysis or a subgroup
error	captured error message if analysis did not run
model	string to identify whether the model adjustment
term	string to identify the term in the analysis
lnhr	log hazard ratio for the analysis
se_lnhr	standard error for the log hazard ratio for the analysis
hr	hazard ratio for the analysis
conf_low	lower confidence limit for the analysis
conf_high	higher confidence limit for the analysis
N_total_midpoint6	total number of people in the analysis
N_exposed_midpoint6	total number of people with the exposure in the analysis
N_events_midpoint6	total number of people with the outcome following exposure in the analysis
person_time_total	total person time included in the analysis
outcome_time_median	median time to outcome following exposure
strata_warning	string to identify strata variables that may cause model faults
surv_formula	survival formula for the analysis
source	language used for cox calculation

output/make_output/table1|table2|venn*_output_midpoint6.csv

These outputs will have similar outputs to the table1|table2|venn outputs, but combined across cohorts. They may contain additional columns indicating the cohort and subgroup of the analysis.

aer_input-*.csv

Variable	Description
aer_sex	sex subgroup under consideration
aer_age	age subgroup under consideration
analysis	string to identify whether this is the ‘main’ analysis or a subgroup
cohort	cohort used for the analysis
outcome	outcome used for the analysis
unexposed_person_days	unexposed person days in the age/sex grouping
unexposed_events	number of events in unexposed people in the age/sex grouping
total_exposed	total number of people with the exposure in the age/sex grouping
sample_size	total number of people in the age/sex grouping

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Licences

As standard, research projects have a MIT license.