This is a clone of the post-covid-gastrointestinal codebase being used to study variable selection and confounder selection methods in practice. This is done as part of Emma Tarmey's work with Kate Tilling, Jonathan Sterne and Rhian Daniel. The below gives the README of the initial repository.
This is the code and configuration for post-covid-gastrointestinal.
You can run this project via Gitpod in a web browser by clicking on this badge:
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.
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If you are interested in how we defined our code lists, look in the
codelists
folder. -
Analyses scripts are in the
analysis
directory:- If you are interested in how we defined our variables, we use study definition scripts to define three cohorts: pre-vaccination, vaccinated and unvaccinated. Study start dates (i.e., index) and end dates differ by cohort and are all described in the protocol. Hence, we have a study definition for each; these are written in
python
. Extracted data is then combined to create our final cohorts, in the preprocess data script. - This directory also contains all the R scripts that process, describe, and analyse the extracted data.
- If you are interested in how we defined our variables, we use study definition scripts to define three cohorts: pre-vaccination, vaccinated and unvaccinated. Study start dates (i.e., index) and end dates differ by cohort and are all described in the protocol. Hence, we have a study definition for each; these are written in
-
The
lib/
directory 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 thecreate_project.R
script which generates all the actions.
This manuscript is currently being drafted.
The project.yaml
defines project actions, run-order and dependencies for all analysis scripts. This file should not be edited directly. To make changes to the yaml, edit and run the create_project.R
script instead. Project actions are then run securely using OpenSAFELY Jobs. Details of the purpose and any published outputs from this project can be found at this link as well.
Below is a description of each action in the project.yaml
with arguments denoted by {arg} in the action name:
To be completed.
Variable | Description |
---|---|
Description | criterion applied to cohort |
N | number of people in the cohort after criterion applied time |
removed | number of people removed due to criterion being applied |
Variable | Description |
---|---|
Characteristic | patient characteristic under consideration |
Subcharacteristic | patient sub characteristic under consideration |
N (%) | number of people with characteristic, alongside % of total |
COVID-19 diagnoses | number of people with characteristic and COVID-19 |
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 | number of unexposed people with the outcome in the analysis |
exposed_person_days | number of person days after exposure in the analysis |
exposed_events | number of exposed people with the outcome in the analysis |
total_person_days | number of person days in the analysis |
total_events | number of people with the outcome in the analysis |
day0_events | number of people with the exposure and outcome on the same day |
total_exposed | number of people with the exposure in the analysis |
sample_size | number of people in the analysis |
Variable | Description |
---|---|
outcome | outcome under consideration |
only_snomed | outcome identified in primary care only |
only_hes | outcome identified in secondary care only |
only_death | outcome identified in death registry only |
snomed_hes | outcome identified in primary and secondary care |
snomed_death | outcome identified in primary care and death registry |
hes_death | outcome identified in secondary care and death registry |
snomed_hes_death | outcome identified in primary care, secondary care, and death registry |
total_snomed | total outcomes identified in primary care |
total_hes | total outcomes identified in secondary care |
total_death | total outcomes identified in death registry |
total | total outcomes identified |
cohort | cohort under consideration |
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 | total number of people in the analysis |
N_exposed | total number of people with the exposure in the analysis |
N_events | 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 |
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