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Clustering on longitudinal quality-of-life measurements using growth mixture models for clinical prognosis: Implementation on CCTG/AGITG CO.20 trial.

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.