Clustering on longitudinal quality‐of‐life measurements using growth mixture models for clinical prognosis: Implementation on <scp>CCTG</scp>/<scp>AGITG CO</scp>.20 trial
Bibliographic record
Abstract
INTRODUCTION: Analyzing longitudinal cancer quality-of-life (QoL) measurements and their impact on clinical outcomes may improve our understanding of patient trajectories during systemic therapy. We applied an unsupervised growth mixture modeling (GMM) approach to identify unobserved subpopulations ("patient clusters") in the CO.20 clinical trial longitudinal QoL data. Classes were then evaluated for differences in clinico-epidemiologic characteristics and overall survival (OS). METHODS AND MATERIALS: In CO.20, 750 chemotherapy-refractory metastatic colorectal cancer (CRC) patients were randomized to receive Brivanib+Cetuximab (n = 376, experimental arm) versus Cetuximab+Placebo (n = 374, standard arm) for 16 weeks. EORTC-QLQ-C30 QoL summary scores were calculated for each patient at seven time points, and GMM was applied to identify patient clusters (termed "classes"). Log-rank/Kaplan-Meier and multivariable Cox regression analyses were conducted to analyze the survival performance between classes. Cox analyses were used to explore the relationship between baseline QoL, individual slope, and the quadratic terms from the GMM output with OS. RESULTS: In univariable analysis, the linear mixed effect model (LMM) identified sex and ECOG Performance Status as strongly associated with the longitudinal QoL score (p < 0.01). The patients within each treatment arm were clustered into three distinct QoL-based classes by GMM, respectively. The three classes identified in the experimental (log-rank p-value = 0.00058) and in the control arms (p < 0.0001) each showed significantly different survival performance. The GMM's baseline, slope, and quadratic terms were each significantly associated with OS (p < 0.001). CONCLUSION: GMM can be used to analyze longitudinal QoL data in cancer studies, by identifying unobserved subpopulations (patient clusters). As demonstrated by CO.20 data, these classes can have important implications, including clinical prognostication.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".