Comparison performance of the Bayesian Approach with the Weibull and Birnbaum-Saunders distributions in imputation of time-to-event censors.

Almost all survival data is censored, and censor imputation is necessary. This study aimed to investigate the performance of the Bayesian Approach (BA) in the imputation of censored records in simulated and Breast Cancer (BC) data. Due to the difference in the distribution of time to event in survival analysis, two well-known the Weibull and Birnbaum-Saunders (BS) distributions have been used to test the performance of the BA. For each of the censored, 10,000 times were simulated using the BA in R and BUGS software, and their median or mean was imputed instead of each censor. The eligibility of both imputation methods was investigated using different curves, different censoring percentages, and sample sizes, as well as the Deviance Information Criteria (DIC), Effective Sample Size, and the Geweke diagnostic in simulated and especially real BC data. The BC data, which contains 220 patients who were identified and followed up between 2015 and 2023, was made accessible on February 1, 2023. The Kaplan-Meier, the BA, and other survival curves were drawn for the observed times. Findings indicated that the performance of the BA under the Weibull and BS distributions in simulated data is similar. The DIC index in the BC data under the BS distribution (1510) is less than the Weibull distribution (1698). Therefore, the BS distribution is preferred over the Weibull for imputation of censoring times in real BC data.

Spatio-temporal analysis of misaligned burden of disease data using a geo-statistical approach.

Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo-statistical model to combine aggregated sparse, noisy BOD data from different sources with misaligned areal units. Our model incorporates the correlation of space, time, and age to estimate health indicators for areas with no data or a small number of observations. The model also considers the heterogeneity of data sources and the measurement errors of input data in the final estimates and uncertainty intervals. We applied the model to combine data from nine different sources of body mass index in a national and sub-national BOD study. The cross-validation results confirmed a high out-of-sample predictive ability in sparse and noisy data. The proposed model can be used by other BOD studies especially at the sub-national level when the areal units are subject to misalignment.

Application of spatio-temporal model to estimate burden of diseases, injuries and risk factors in Iran 1990 - 2013.

BACKGROUND: Identifying the burden of disease and its inequality between geographical regions is an important issue to study health priorities. Estimating burden of diseases using statistical models is inevitable especially in the context of rare data availability. To this purpose, the spatio-temporal model can provide a statistically sound approach for explaining the response variable observed over a region and various times. However, there are some methodological challenges in analysis of these complex data. Our primary objective is to provide some remedies to overcome these challenges. METHOD: Data from nationally representative surveys and systematic reviews have been gathered across contiguous areal units over a period of more than 20 years (1990 - 2013). Generally, observations of areal units are spatially and temporally correlated in such a way that observations closer in space and time tend to be more correlated than observations farther away. It is critical to determine the correlation structure in space-time process which has been observed over a set of irregular regions. Moreover, these data sets are subject to high percentage of missing, including misaligned areal units, areas with small sample size, and may have nonlinear trends over space and time. Furthermore, the Gaussian assumption might be overly restrictive to represent the data. In this setting, the traditional statistical techniques are not appropriate and more flexible and comprehensive methodology is required. Particularly, we focus on approaches that allow extending spatio-temporal models proposed previously in the literature.Since statistical models include both continuous and categorical outcomes, we assume a latent variable framework for describing the underlying structure in mixed outcomes and use a conditionally autoregressive (CAR) prior for the random effects.  In addition, we will employ misalignment modeling to combine incompatible areal units between data sources and/or over the years to obtain a unified clear picture of population health status over this period.  In order to take parameter uncertainties into account, we pursue a Bayesian sampling-based inference. Hence, a hierarchical Bayes approach is constructed to model the data. The hierarchical structure enables us to "borrow information" from neighboring areal units to improve estimates for areas with missing values and small number of observations. For their general applicability and ease of implementation, the MCMC methods are the most adapted tool to perform Bayesian inference. CONCLUSION: This study aims to combine different available data sources and produce precise and reliable evidences for Iranian burden of diseases and risk factors and their disparities among geographical regions over time. Providing appropriate statistical methods and models for analyzing the data is undoubtedly crucial to circumvent the problems and obtain satisfactory estimates of model parameters and reach accurate assessment.