Feasibility of Simon 2-Stage Futility Trials in Early Parkinson Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Disease-modifying treatments (DMTs) are a major unmet need in Parkinson disease (PD). To date, trials investigating DMT candidates in PD most often used a randomized controlled trial (RCT) design. Unfortunately, RCTs to date have not led to a breakthrough, in part because of the large sample sizes and length of follow-up required. In the interest of testing DMT candidates in a more efficient manner, it may be worthwhile to perform futility trials, which are smaller clinical trials that have originally been developed as phase 2 trials in oncology and more recently been used in progressive multiple sclerosis. In this investigation, we used original, patient-level data from DATATOP and PRECEPT, 2 large RCTs in early PD, to explore the feasibility of using the Simon 2-Stage futility trial design in early PD. METHODS: This is a post hoc analysis of original, patient-level data from the DATATOP and PRECEPT RCTs in early PD. In our analyses, we use descriptive statistics, survival analysis, and binary logistic regression to explore thresholds of change in the Unified Parkinson Disease Rating Scale (UPDRS) motor score as the primary outcome measure, length of follow-up, inclusion and exclusion criteria, and projected sample sizes for Simon 2-Stage futility trials in early PD. We also performed bootstrapping experiments to illustrate the ability of trials using the Simon 2-Stage futility design to identify selegiline as nonfutile and tocopherol as futile. RESULTS: PRECEPT included 806 participants (mean age 59.7 years, SD 10.3, 64.4% male), and DATATOP included 800 participants (mean age 61.1 years, SD 9.5, 64.4% male). Our analyses suggest that futility trials using the Simon 2-Stage methodology are feasible in PD. We propose a 5-point worsening on the UPDRS motor score as the primary outcome measure and a length of follow-up of 12 months. Trial simulations based on these data suggest the required sample size for such clinical trials to be lower than 200 participants. DISCUSSION: Based on our analysis of DATATOP and PRECEPT, phase 2 clinical trials using the Simon 2-Stage methodology are feasible in PD and may offer an opportunity to expedite the discovery of promising treatments in early PD.
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 | 0.294 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".