Terminating observation within matched pairs of subjects in a matched cohort analysis: a Monte Carlo simulation study
Bibliographic record
Abstract
Matched cohort analyses are becoming increasingly popular for estimating treatment effects in observational studies. However, in the applied biomedical literature, analysts and authors are inconsistent regarding whether to terminate follow-up among members of a matched set once one member is no longer under observation. This paper focused on time-to-event outcomes and used Monte Carlo simulation methods to determine the optimal approach. We found that the bias of the estimated treatment effect estimate was negligible under both approaches and that the percentage of censoring had no discernible effect on the magnitude of bias. The mean model-based standard error of the treatment estimate was consistently higher when we terminated observation within matched pairs. Furthermore, the type 1 error rate was consistently lower when we did not terminate follow-up within matched pairs. In conclusion, when the focus was on time-to-event outcomes, we demonstrated that there was no advantage to terminating follow-up within matched pairs. Continuing follow-up on each subject until their observation was naturally complete was superior compared with terminating a subject's observation time once its matched pair had ceased to be under observation. Given the frequency with which these analyses are conducted in the applied literature, our results provide important guidance to analysts and applied researchers as to the preferred analytic approach.
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How this classification was reachedexpand
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".