OFH Simulated Data Generator

This repository introduces the ofhsim R library that creates synthetic datasets, aiming to mimic the structure of multiple linked health data sources (participant, questionnaire, clinic measures, HES-like events, primary care medicines, deaths, and geography) available in the Our Future Health cohort. It is designed so researchers can develop and test workflows before running them on the much larger real data.

This project is designed to generate synthetic data from rule-based and stochastic generators, schema dictionaries, and user-supplied code lists, rather than from participant-level OFH source records.

The output data is a general approximation of the schema intended for code development, not for statistical inference.

This is an unofficial tool and is not affiliated with Our Future Health.

What It Generates

The generator produces CSV files for a synthetic cohort, including:

• participant data
• questionnaire data
• clinic measurements
• outpatient, inpatient, emergency, and deaths HES data
• primary care medication data
• country/region data

All datasets are linked by pid.

The sample size, diagnostic codes, and BNF drug codes are determined by the user. These codes are then sampled and distributed using configurable probabilities (for cohort coverage, record counts, and field-level missingness) across the inpatient, outpatient, A&E, and primary care medicines datasets.

Requirements

Minimum:

• R 4.2+

For vignette building/preview:

• rmarkdown
• knitr

Install vignette dependencies (if needed):

install.packages(c("rmarkdown", "knitr"))

Quick Start

git clone https://github.com/hlnicholls/OFHSimulatedData

cd ./OFHSimulatedData

install.packages(".", repos = NULL, type = "source")
library(ofhsim)

sim <- OFHCohortSimulator$new(project_root = ".", seed = 123)

out <- sim$run_all(n = 1000)

Custom Code Lists (ICD-10, OPCS4, BNF)

You control which codes appear in outputs, and how frequently records are generated.

• Set code pools with icd10, opcs4, and bnf_codes (or *_file inputs).
• Set dataset-level coverage and record volume with proportions and record_multipliers.
  • E.g., setting proportions = list(nhse_outpat = 0.25) and record_multipliers = list(nhse_outpat = 1.2) for n = 1000 gives about 250 unique outpatient participants and about 1,200 outpatient rows (including repeat visits).
• Set within-dataset code proportionality (relative code frequencies) with code_config weights, for example icd10_weights, opcs4_weights, primary_icd10_weights, underlying_icd10_weights, ae_specific_weights, and read_weights.
  • E.g., setting code_config = list(nhse_outpat_data = list(icd10_weights = c(I210 = 5, I500 = 1))) gives roughly a 5:1 ratio of I210 to I500 among sampled outpatient ICD-10 codes (subject to normal random variation).

Code Lists File Formats

ICD10 code files should use this structure (CSV columns, or tab-separated TXT with same columns):

code	description
I210	STEMI of anterolateral wall
I500	Congestive heart failure
N189	Chronic kidney disease unspecified

OPCS4 code files should use this structure (CSV columns, or tab-separated TXT with same columns):

code	description
K401	Percutaneous transluminal balloon angioplasty of coronary artery
K451	Insertion of drug-eluting stent into coronary artery
K561	Repair of heart valve

BNF codes should use this structure:

BNFCode	BNFName	Formulation	Strength
ZZZ0001AA	Custom Test Drug A	tablets	10 mg
ZZZ0002BB	Custom Test Drug B	inhaler	100 mcg
ZZZ0003CC	Custom Test Drug C	capsules	50 mg

Example Data Generation with set codes

library(ofhsim)

out <- generate_ofh_cohort(
	n = 1000,
	seed = 123,
	icd10 = c(
		I210 = "STEMI of anterolateral wall",
		I500 = "Congestive heart failure"
	),
	opcs4 = c(
		K401 = "Percutaneous transluminal balloon angioplasty of coronary artery"
	),
	bnf_codes = data.frame(
		BNFCode = c("0212000B0", "0601023A0"),
		BNFName = c("Atorvastatin 20 mg tablets", "Metformin 500 mg tablets"),
		Formulation = c("tablets", "tablets"),
		Strength = c("20 mg", "500 mg"),
		stringsAsFactors = FALSE
	)
)

# or load code lists from files
# - ICD10/OPCS4: include both code and description
#   - TXT: tab-separated columns (code\tdescription)
#   - CSV: 'code,description' columns
# - BNF: use a CSV with columns BNFCode, BNFName, Formulation (optional Strength)
out <- generate_ofh_cohort(
	n = 1000,
	seed = 123,
	icd10_file = "icd10_codes.txt",
	opcs4_file = "opcs4_codes.csv",
	bnf_codes_file = "bnf_medications.csv"
)

# return objects only in the R environment (no CSV files written)
out_objects_only <- generate_ofh_cohort(
	n = 1000,
	seed = 123,
	save_csv = FALSE,
	return_objects = TRUE
)

# write CSV files only (no returned R objects)
generate_ofh_cohort(
	n = 1000,
	seed = 123,
	save_csv = TRUE,
	return_objects = FALSE,
	output_dir = "example"
)

# customize generation probabilities
out_custom_probs <- generate_ofh_cohort(
	n = 1000,
	seed = 123,
	proportions = list(
		nhse_outpat = 0.25,
		nhse_inpat = 0.20,
		nhse_ed = 0.30,
		nhse_primcare_meds = 0.75
	),
	record_multipliers = list(
		nhse_outpat = 1.2,
		nhse_inpat = 1.1,
		nhse_ed = 1.3
	),
	code_config = list(
		nhse_outpat_data = list(diag_4_02_missing_prob = 0.70),
		nhse_inpat_data = list(single_diag_prob = 0.85)
	),
	save_csv = FALSE,
	return_objects = TRUE
)