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Survival data analysis becomes complex when the proportional hazards assumption is violated at population level, or when crude hazard rates are no longer estimators of marginal ones. We develop a Bayesian survival analysis method to deal with these situations, based on assuming that the complexities are induced by latent cohort or disease heterogeneity that is not captured by covariates, and that proportional hazards hold at the level of individuals. This leads to a description from which risk-specific marginal hazard rates and survival functions are fully accessible, 'decontaminated' of the effects of informative censoring, and which includes Cox, random effects and latent class models as special cases. Simulated data confirm that our approach can map a cohort's substructure, and remove heterogeneity-induced informative censoring effects. Application to data from the ULSAM cohort leads to plausible alternative explanations for previous counter-intuitive inferences on prostate cancer. The importance of managing cardiovascular disease as a comorbidity in women diagnosed with breast cancer is suggested on application to data from the AMORIS study.

BackgroundImpaired glucose metabolism has been linked with increased cancer risk, but the association between serum glucose and cancer risk remains unclear. We used repeated measurements of glucose and fructosamine to get more insight into the association between the glucose metabolism and risk of cancer.MethodsWe selected 11,998 persons (>20 years old) with four prospectively collected serum glucose and fructosamine measurements from the Apolipoprotein Mortality Risk (AMORIS) study. Multivariate Cox proportional hazards regression was used to assess standardized log of overall mean glucose and fructosamine in relation to cancer risk. Similar analyses were performed for tertiles of glucose and fructosamine and for different types of cancer.ResultsA positive trend was observed between standardized log overall mean glucose and overall cancer risk (HR = 1.08; 95% CI: 1.02–1.14). Including standardized log fructosamine in the model resulted in a stronger association between glucose and cancer risk and aninverse association between fructosamine and cancer risk (HR = 1.17; 95% CI: 1.08–1.26 and HR: 0.89; 95% CI: 0.82–0.96, respectively). Cancer risks were highest among those in the highest tertile of glucose and lowest tertile of fructosamine. Similar findings were observed for prostate, lung, and colorectal cancer while none observed for breast cancer.ConclusionThe contrasting effect between glucose, fructosamine, and cancer risk suggests the existence of distinct groups among those with impaired glucose metabolism, resulting in different cancer risks based on individual metabolic profiles. Further studies are needed to clarify whether glucose is a proxy of other lifestyle-related or metabolic factors.

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