Deaths from breast cancer: tackling multidimensionality and non-linearity by correspondence analysis

We investigated the use of non-linear, multidimensional factor analysis for the study of observational data on death from breast cancer. These data were obtained in the context of a clinical practice and not in a clinical trial. We looked into the correlations between patient characteristics and time of death and/or disease-free interval.

We first analyzed the characteristics of a population of patients that had died from breast cancer (n = 295), then of a population including patients still alive 7 years after surgery (n = 344). We used correspondence analysis (CA) which is based on chi(2)-metrics, does not assume linear relationships, and provides graphic overviews.

The CA mapped variables (clinical stage, histoprognostic grade, node status, receptor positivity) in a way that fits in well with available knowledge on their importance as prognostic factors. We observed, however, that death occurred during three main periods (1-3, 4-7, < OR = 8 years after surgery) defined by different mixes of variables as if the disease progressed by stage rather than continuously. The CA distinguished long-term survivors (>7 years) from patients who died 8-10 years after surgery. Long-term survivors tended to be node-negative; those who died at 8-10 years tended to be the youngest patients (under 40).

Because correspondence analysis combines the advantages of multidimensional and non-linear methods, it is a valuable exploratory tool for describing multiple correlations within a population before attempting to establish statistical significance of selected variables by more classic methods.