Using causal models to show the effect of untestable assumptions on effect estimates in randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: The methods by which randomized controlled trials (RCTs) are analyzed rest on several assumptions, most of which are untestable. PURPOSE: To show how estimates of the net effect of treatment on survival can be obtained requiring only the assumption that randomization produced equivalent groups. METHODS: The assumptions underlying ratio measures of effect, based on disease occurrence times (DOT) obtained from survival curves, are identified and cumulatively removed. RESULTS: The four assumptions usually made are that (1) the ratio of disease incidence rates under treatment and under reference exposure is constant over time, (2) the groups being compared are exchangeable (equivalent), (3) a subject's DOT under treatment is independent of what his DOT would have been under the reference exposure, and (4) the treatment effect, if any, is in the same direction in all subjects. Removing Assumption 4 leads to an estimator of effect resembling the etiologic fraction, but able to accommodate both causative and preventive effects. Removing all assumptions but that of exchangeability still permits the estimation, directly from the survival curves, of a range of effect magnitudes, causative or preventive, compatible with the observed DOTs. The exchangeability assumption is the easiest to meet, by randomizing enough subjects. LIMITATIONS: The statistical uncertainty that affects the estimates of survival probabilities has been ignored. Taking uncertainty into account further widens the range of effects compatible with the observations. CONCLUSIONS: Retaining only the exchangeability assumption allows for a range of possible treatment effects to be estimated, although it may be wide. Readers of RCT reports should understand that the determination of a point estimate of effect within this range is entirely a function of unverifiable analytic assumptions.
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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 | 0.535 | 0.786 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".