Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone
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 featuring recurrent clinical events, the definition and estimation of treatment \neffects involves a number of interesting issues, especially when loss to follow-up may be eventrelated \nand when terminal events such as death preclude the occurrence of further events. This \npaper discusses a clinical trial of breast cancer patients with bone metastases where the recurrent \nevents are skeletal complications, and where patients may die during the trial. We argue that treatment \neffects should be based on marginal rate and mean functions. When recurrent event data are \nsubject to event-dependent censoring, however, ordinary marginal methods may yield inconsistent \nestimates. Incorporating correctly specified inverse probability of censoring weights into analyses \ncan protect against dependent censoring and yield consistent estimates of marginal features. An \nalternative approach is to obtain estimates of rate and mean functions from models that involve \nsome conditioning to render censoring conditionally independent. We consider three methods of \nestimating mean functions of recurrent event processes and examine the bias and efficiency of \nunweighted and inverse probability weighted versions of the methods with and without a terminating \nevent. We compare the methods via simulation and use them to analyse the data from the \nbreast cancer trial.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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