The use of the propensity score for estimating treatment effects: administrative versus clinical data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is an increasing interest in using administrative data to estimate the treatment effects of interventions. While administrative data are relatively inexpensive to obtain and provide population coverage, they are frequently characterized by lack of clinical detail, often leading to problematic confounding when they are used to conduct observational research. Propensity score methods are increasingly being used to address confounding in estimating the effects of interventions in such studies. Using data on patients discharged from hospital for whom both administrative data and detailed clinical data obtained from chart reviews were available, we examined the degree to which stratifying on the quintiles of propensity scores derived from administrative data was able to balance patient characteristics measured in clinical data. We also determined the extent to which measures of treatment effect obtained using propensity score methods were similar to those obtained using traditional regression methods. As a test case, we examined the treatment effects of ASA and beta-blockers following acute myocardial infarction. We demonstrated that propensity scores developed using administrative data do not necessarily balance patient characteristics contained in clinical data. Furthermore, measures of treatment effectiveness were attenuated when obtained using clinical data compared to when administrative data were used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it