Time-to-event analysis mitigates the impact of symptomatic therapy on therapeutic benefit in Parkinson’s disease trials
Bibliographic record
Abstract
The use of symptomatic medications represents a challenge for clinical trials of novel medicines designed to slow Parkinson's disease progression. A time-to-event (TTE) approach using a defined motor progression milestone may mitigate the confounding effect of symptomatic therapy on the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). This analysis uses prasinezumab- and placebo-treated groups from the PASADENA study to evaluate the impact of symptomatic medications on treatment effects by comparing a TTE approach to a change-from-baseline approach with and without censoring the population upon starting symptomatic therapy. While the TTE approach yielded consistent hazard ratios between censored and non-censored analyses, the estimated difference between treatment arms using the change-from-baseline approach was lower without censoring than with censoring, suggesting a potential masking of prasinezumab treatment effects by symptomatic therapy. Thus, the TTE approach may mitigate the potential confounding effect of symptomatic therapy on MDS-UPDRS Part III.
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.
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 | 0.305 | 0.320 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".