Patient-reported outcomes helped predict survival in multiple myeloma using partial least squares analysis

Abstract

Objective The prognostic value of Patient-Reported Outcomes (PRO) in predicting mortality during treatment of multiple myeloma (MM) patients was assessed using partial least square (PLS) regression, a statistical method that is well-adapted for highly correlated data. Study Design and Setting Four PRO measures, The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30, the EORTC QLQ-MY24, the FACIT-Fatigue scale, and the FACT/GOG-Ntx scale, were administered during a trial designed to evaluate the efficacy and safety of bortezomib (VELCADE ® 1.3 mg/m 2) in MM patients ( N = 202). Clinical and PRO data were analyzed for predictive value by univariate and multivariate logistic regression methods and then by PLS regression. Results Fifteen baseline PRO parameters were significant in predicting mortality during treatment when univariate logistic regression was used. In contrast, only two variables were retained in the multivariate analysis, as correlated variables were excluded from the model. Using PLS regression, 14 of the 21 PRO predictors were significant in predicting mortality. Clinical and PRO data used together increased the predictive power of all models compared to clinical data alone. Conclusion The prognostic value of PRO was established and was more informative using PLS regression. PLS regression may therefore be a valuable method for analyzing PRO data.