Efficient panel designs for longitudinal recurrent event studies recording panel counts
Bibliographic record
Abstract
Many clinical trials are designed to study outcome measures recorded as the number of events occurring during specific intervals, called panel data. In such data, the intervals are specified by a planned set of follow-up times. As the collection of panel data results in a partial loss of information relative to a record of the actual event times, it is important to gain a thorough understanding of the impact of panel study designs on the efficiency of the estimates of treatment effects and covariates. This understanding can then be used as a base from which to formulate appropriate designs by layering in other concerns, e.g. clinical constraints, or other practical considerations. We compare the efficiency of the analysis of panel data with respect to the analysis of data recorded precisely as times of recurrences, and articulate conditions for efficient panel designs where the focus is on estimation of a treatment effect when adjusting for other covariates. We build from the efficiency comparisons to optimize the design of panel follow-up times. We model the recurrent intensity through the common proportional intensity framework, with the treatment effect modeled flexibly as piecewise constant over panels, or groups of panels. We provide some important considerations for the design of efficient panel studies, and illustrate the methods through analysis of designs of studies of adenomas.
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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 | 0.157 | 0.275 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".