Joint analysis of longitudinal and survival AIDS data with a spatial fraction of long-term survivors: A Bayesian approach.

A typical survival analysis with time-dependent covariates usually does not take into account the possible random fluctuations or the contamination by measurement errors of the variables. Ignoring these sources of randomness may cause bias in the estimates of the model parameters. One possible way for overcoming that limitation is to consider a longitudinal model for the time-varying covariates jointly with a survival model for the time to the event of interest, thereby taking advantage of the complementary information flowing between these two-model outcomes. We employ here a Bayesian hierarchical approach to jointly model spatial-clustered survival data with a fraction of long-term survivors along with the repeated measurements of CD4+ T lymphocyte counts for a random sample of 500 HIV/AIDS individuals collected in all the 27 states of Brazil during the period 2002-2006. The proposed Bayesian joint model comprises two parts: on the one hand, a flexible model using Penalized Splines to better capture the nonlinear behavior of the different CD4 profiles over time; on the other hand, a spatial cure model to cope with the set of long-term survivor individuals. Our findings show that joint models considering this set of patients were the ones with the best performance comparatively to the more traditional survival approach. Moreover, the use of spatial frailties allowed us to map the heterogeneity in the disease risk among the Brazilian states.

Bayesian joint modeling of longitudinal and spatial survival AIDS data.

Joint analysis of longitudinal and survival data has received increasing attention in the recent years, especially for analyzing cancer and AIDS data. As both repeated measurements (longitudinal) and time-to-event (survival) outcomes are observed in an individual, a joint modeling is more appropriate because it takes into account the dependence between the two types of responses, which are often analyzed separately. We propose a Bayesian hierarchical model for jointly modeling longitudinal and survival data considering functional time and spatial frailty effects, respectively. That is, the proposed model deals with non-linear longitudinal effects and spatial survival effects accounting for the unobserved heterogeneity among individuals living in the same region. This joint approach is applied to a cohort study of patients with HIV/AIDS in Brazil during the years 2002-2006. Our Bayesian joint model presents considerable improvements in the estimation of survival times of the Brazilian HIV/AIDS patients when compared with those obtained through a separate survival model and shows that the spatial risk of death is the same across the different Brazilian states. Copyright © 2016 John Wiley & Sons, Ltd.

Extended excess hazard models for spatially dependent survival data.

Relative survival represents the preferred framework for the analysis of population cancer survival data. The aim is to model the survival probability associated with cancer in the absence of information about the cause of death. Recent data linkage developments have allowed for incorporating the place of residence into the population cancer databases; however, modeling this spatial information has received little attention in the relative survival setting. We propose a flexible parametric class of spatial excess hazard models (along with inference tools), named "Relative Survival Spatial General Hazard," that allows for the inclusion of fixed and spatial effects in both time-level and hazard-level components. We illustrate the performance of the proposed model using an extensive simulation study, and provide guidelines about the interplay of sample size, censoring, and model misspecification. We present a case study using real data from colon cancer patients in England. This case study illustrates how a spatial model can be used to identify geographical areas with low cancer survival, as well as how to summarize such a model through marginal survival quantities and spatial effects.

A Bayesian Model for Spatial Partly Interval-Censored Data.

Partly interval-censored data often occur in cancer clinical trials and have been analyzed as right-censored data. Patients' geographic information sometimes is also available and can be useful in testing treatment effects and predicting survivorship. We propose a Bayesian semiparametric method for analyzing partly interval-censored data with areal spatial information under the proportional hazards model. A simulation study is conducted to compare the performance of the proposed method with the main method currently available in the literature and the traditional Cox proportional hazards model for right-censored data. The method is illustrated through a leukemia survival data set and a dental health data set. The proposed method will be especially useful for analyzing progression-free survival in multi-regional cancer clinical trials.

Assessment of spatial variation in breast cancer-specific mortality using Louisiana SEER data.

BACKGROUND: Previous studies suggest spatial differences in mortality for many types of cancer, including breast cancer. Identifying explanations for these spatial differences results in a better understanding of what leads to longer survival time. METHODS: We used a Bayesian accelerated failure time model with spatial frailty terms to investigate potential spatial differences in breast cancer mortality following breast cancer diagnosis using 2000-2013 Louisiana SEER data. RESULTS: There are meaningful spatial differences in breast cancer mortality across the parishes of Louisiana, even after adjusting for known demographic and clinical risk factors. For example, the average survival time of a woman diagnosed in Orleans parish was 1.51 times longer than that of a woman diagnosed in Terrebonne parish. Additionally, there is evidence to suggest shorter survival times in lower income parishes along the Red and Mississippi Rivers, as well as parishes with lower socioeconomic status, less access to care and fresh food, worse quality of care, and more workers in certain industries. CONCLUSION: The addition of spatial frailties to account for an individual's geographic location is useful when analyzing breast cancer mortality data. Our findings suggest that survival following breast cancer diagnosis could potentially be improved if socioeconomic status differences were addressed, healthcare improved in quality and became more accessible, and certain industrial situations were improved for individuals diagnosed in parishes identified as having shorter average survival times.