Estimating treatment effects in randomized clinical trials in the presence of non-compliance
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
In clinical trials where patients are randomized between two treatment arms, not all patients comply with the treatment they were randomly assigned to. The reasons for (non)compliance may be associated with the outcome variable and thereby act as confounders. The standard way of analysing such trials is by the 'intention-to-treat' principle, which allows the use of permutation tests. Conclusions drawn from such tests do not depend on untested assumptions such as absence of confounding. However, this approach may yield biased estimators for the causal effects of treatments. We consider the estimation of such effects for clinical trials where non-compliers can be considered to have switched to the other trial arm. The most important example of this is the placebo-controlled clinical trial where no substantial placebo effects are anticipated. We consider the situation where the relationship between compliance, and thus treatment received, and outcome is influenced by unobserved confounders. The residual of the regression of the actual treatment indicator variable on the randomization arm indicator variable is shown to 'intercept' the effect of such confounders. Inclusion of this residual in a multivariate analysis, in conjunction with the treatment indicator variable, should thus adjust for confounding. Examples are given. In those examples, the results are similar to those obtained by more complex methods.
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 | 0.024 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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