Exploring Alternative Designs using ‘Big’ Administrative Data
Bibliographic record
Abstract
IntroductionLarge population-based data sets present similar analytic issues across such fields as: population health, clinical epidemiology, education, justice, and children’s services. Step-wise approaches and generalized tools can bring together several pillars: big (typically administrative) data, programming, and study design/analysis. How can we improve efficiency and explore alternative designs? Objectives and ApproachLinked data sets typically contain: 1) files presenting longitudinal histories 2) substantive files noting various events (concussions, burns, loss of a loved one, public housing entry) and several possible covariates and outcomes. Step-wise approaches enable automating tasks by developing general tools (decreasing programmer input) and facilitating alternative designs. Macros improve upon the classic ‘one design, one data set’ perspective. Two case studies highlight tradeoffs in retrospective cohort studies (quasi-experiments) among sample size, length of follow-up, and the number of time periods. ResultsStudy 1: Step 1 calculated the number of mothers with a child placed in care during various index years. Taking 1 year before and after placement generated 5,991 eligible mothers; selecting 5 years before/after decreased the N to 2,281. Step 2 selected appropriate in-province residents. Step 3 handled missing covariates and outcomes, while Step 4 ran alternative designs. One example (of several) compared maternal mental health outcomes using 8 time periods (in 2 years) before/after the event with outcomes using 16 time periods (in 4 years) before/after. Besides showing increasing maternal problems, the 4-year follow-up sometimes produced different statistically significant periods than the 2-year follow-up. Study 2. Swedish/Canadian comparisons of mothers with children placed in foster care highlighted growing differences in maternal pharmaceutical use. Conclusion/ImplicationsPresenting design alternatives is straightforward and applicable across disciplines. Ongoing work is facilitating comparisons of ‘experimental’ and control groups. Literature-derived guidelines and simulation-based techniques should lead to better design decisions. Automated model assessment can help analyze robustness, statistical power, residuals, and bias, suggesting artificial intelligence approaches.
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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 | 0.159 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".