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Latent Class Modelling of the Association between Socioeconomic Background and Breast Cancer Survival Status at 5 Years Whilst Incorporating Stage of Disease
  1. Amy Downing,
  2. Wendy J Harrison,
  3. Robert M West,
  4. David Forman,
  5. Mark S Gilthorpe
  1. University of Leeds, United Kingdom
  1. * Corresponding author; email: a.downing{at}


Background: Stage of disease and socioeconomic background (SEB) are often used to ‘explain’ differences in breast cancer outcomes. There are challenges for all types of analysis (e.g. survival analysis, logistic regression etc.), including missing data, measurement error and the ‘reversal paradox’. This study investigates the association between SEB and survival status within 5 years of breast cancer diagnosis using: (i) logistic regression with and without adjustment for stage; and (ii) logistic latent class analysis (LCA) excluding stage as a covariate but with and without stage as a latent class predictor.

Methods: Females diagnosed with invasive breast cancer between 1998 and 2000 in one UK region were identified (n=11,781). Multilevel logistic regression was performed using standard regression and LCA. Models included SEB (2001 Townsend Index), age and stage (‘missing’ stage (8.0%) modelled as a separate category). The association of SEB with stage was also assessed.

Results: Using standard regression, there was a substantial association between SEB and death within 5 years, with and without adjustment for stage. Using LCA, patients were assigned to a large good prognosis group and a small poor prognosis group. The association between SEB and survival was substantive in both classes for the model without stage but only in the larger class for the model with stage. Increasing deprivation was associated with more advanced stage at diagnosis.

Conclusions: LCA categorises patients into prognostic groups according to patient and tumour characteristics, providing an alternative strategy to the usual statistical adjustment for stage.

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