Generating realistic simulated administrative health data for population-based drug safety and effectiveness research and training
Bibliographic record
Abstract
Abstract Background: Administrative health records (AHRs), which are generated primarily for management and billing purposes, are now widely used in drug safety and comparative effectiveness studies. The development of analytic methods for multi-site studies can benefit from the availability of simulated data, which do not require ethical approvals and data access permissions. We simulated AHRs using both the Observational Medical Dataset Simulator II (OSIM2) proposed by the Observational Medical Outcomes Partnership, and a modified OSIM (ModOSIM) method developed by the Canadian Network for Observational Drug Effect Studies (CNODES). Our objective was to compare the simulated data to real-world AHR data to assess the representativeness of the simulated data.Methods: The real-world data comprised prescription drug records for all individuals with healthcare coverage at any point in a 10-year period (2008 – 2017) from the Manitoba Population Research Data Repository (MPRDR) in the province of Manitoba, Canada. OSIM2 and ModOSIM, which are empirical simulation models for longitudinal patient data, were used to simulate AHRs. The data were described using frequencies and percentages. We estimated agreement of prescription drug use measures in MPRDR, OSIM2 and ModOSIM using the concordance coefficient.Results: The MPRDR cohort included 169,586,633 drug records and 1,395 drug types for 1,604,734 individuals. Data for 50,000 individuals were simulated using OSIM2 and ModOSIM. Sex and age group distributions were similar in the real-world and simulated data. There were significant differences in the total number of drug records and number of unique drugs for OSIM2 and ModOSIM when compared with MPRDR; the median number of unique drugs in MPRDR, OSIM2 and ModOSIM was 9.0, 6.0 and 10.0, respectively. For average number of days of drug use, concordance was 16% (95% confidence interval [CI]: 12% – 19%) for MPRDR and OSIM2 and 88% (95% CI: 87%-90%) for MPRDR and ModOSIM.Conclusions: ModOSIM data were more similar to MPRDR than OSIM2 data on many measures of prescription drug use. Simulated AHRs that are consistent with those found in real-world settings can be generated using ModOSIM; these simulated data will benefit methodological studies and data analyst training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".