Latent class modelling of the association between socioeconomic background and breast cancer survival status at 5 years incorporating stage of disease.
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 (eg, survival analysis, logistic regression), 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 (1) logistic regression with and without adjustment for stage and (2) logistic latent class analysis (LCA) excluding stage as a covariate but with and without stage as a latent class predictor.


METHODS
Women 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.
Downing
Amy
A
Cancer Epidemiology Group, Centre for Epidemiology and Biostatistics, University of Leeds, Leeds, UK.
Harrison
Wendy J
WJ
West
Robert M
RM
Forman
David
D
Gilthorpe
Mark S
MS
D000328:Adult; D001943:Breast Neoplasms / Q000191:economics* / Q000401:mortality* / Q000473:pathology; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D015233:Models, Statistical; D055361:Multilevel Analysis; D009367:Neoplasm Staging; D016017:Odds Ratio; D011203:Poverty; D012959:Socioeconomic Factors; D015996:Survival Rate; D013997:Time Factors; D016896:Treatment Outcome; D006113:United Kingdom / Q000453:epidemiology