Exploring series of multivariate censored temporal data through fuzzy coding and correspondence analysis

Abstract

We present an exploratory analysis methodology for a multiple series of multivariate temporal data subject to censoring, and thus requiring the introduction of a coding technique: fuzzy coding preserving a large amount of the distributional information is fully adapted. Correspondence analysis is performed on the array produced by fuzzy coding. Projections of mean paths on factorial mappings, according to subgroup characteristics, highlight the behaviour of the underlying process. This approach is illustrated with an application to a randomized controlled clinical trial designed for comparing non-diabetic chronic renal failure treatments. Our methodology has resulted in the identification of a difference between the treatments with an interpretation of the effects in subgroups of patients not obtainable with traditional survival methodology; it also provides some valuable insights for designing further studies on treatment of renal impairment.