Joint modeling of progression-free survival and patient-reported outcomes to evaluate the association between disease progression and symptoms among patients with relapsed/refractory multiple myeloma
Bibliographic record
Abstract
BACKGROUND: Here we aim to evaluate the relationship between progression-free survival (PFS) and patient-reported symptoms (measured by health-related quality of life scores) among patients with relapsed/refractory multiple myeloma (RRMM). METHODOLOGY: Pain and fatigue were identified as the most common patient-relevant symptoms within RRMM based on a predefined literature review of patient preference/qualitative studies (confirmed by clinical experts). Consequently, the European Organisation for Research and Treatment of Cancer QLQ-C30 pain, QLQ-MY20 disease symptoms (pain in different locations), and QLQ-C30 fatigue domains were selected. Change from baseline scores per symptom domain was jointly modeled with PFS assuming a current slope association structure. For each symptom, we evaluated trial-specific joint models based on individual patient data from 7 RRMM clinical trials. The association between symptoms and PFS was summarized via association-effect hazard ratios (HRs) from the joint models, where a HR > 1 indicates that symptom worsening was associated with an increased hazard of a progression/death event. Meta-analyses were performed to synthesize the joint model HRs from all trials into one summary statistic (meta-HR) per symptom domain. RESULTS: Across trials, worsening in pain and fatigue was associated with an increased hazard of progression events (disease progression/death) based on joint-model-association-effect HRs. Specifically, meta-HRs (95% CI) were 1.10 (1.02, 1.19) for QLQ-C30 pain, 1.10 (1.01, 1.20) for QLQ-MY20 disease symptoms, and 1.11 (1.05, 1.17) for QLQ-C30 fatigue. CONCLUSIONS: This study demonstrated that worsening in pain and fatigue was consistently associated with an increased hazard of disease progression or death events across numerous RRMM clinical trials with varying disease severity. This suggests risk of PFS events may align with patient experience in terms of worsening symptom burden.
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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 | 0.056 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.033 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".