Estimation and extrapolation of time trends in registry data---Borrowing strength from related populations

To analyze and project age-specific mortality or morbidity rates age-period-cohort (APC) models are very popular. Bayesian approaches facilitate estimation and improve predictions by assigning smoothing priors to age, period and cohort effects. Adjustments for overdispersion are straightforward using additional random effects. When rates are further stratified, for example, by countries, multivariate APC models can be used, where differences of stratum-specific effects are interpretable as log relative risks. Here, we incorporate correlated stratum-specific smoothing priors and correlated overdispersion parameters into the multivariate APC model, and use Markov chain Monte Carlo and integrated nested Laplace approximations for inference. Compared to a model without correlation, the new approach may lead to more precise relative risk estimates, as shown in an application to chronic obstructive pulmonary disease mortality in three regions of England and Wales. Furthermore, the imputation of missing data for one particular stratum may be improved, since the new approach takes advantage of the remaining strata if the corresponding observations are available there. This is shown in an application to female mortality in Denmark, Sweden and Norway from the 20th century, where we treat for each country in turn either the first or second half of the observations as missing and then impute the omitted data. The projections are compared to those obtained from a univariate APC model and an extended Lee--Carter demographic forecasting approach using the proper Dawid--Sebastiani scoring rule.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS498 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Spatial Aggregation with Respect to a Population Distribution

Spatial aggregation with respect to a population distribution involves estimating aggregate quantities for a population based on an observation of individuals in a subpopulation. In this context, a geostatistical workflow must account for three major sources of `aggregation error': aggregation weights, fine scale variation, and finite population variation. However, common practice is to treat the unknown population distribution as a known population density and ignore empirical variability in outcomes. We improve common practice by introducing a `sampling frame model' that allows aggregation models to account for the three sources of aggregation error simply and transparently. We compare the proposed and the traditional approach using two simulation studies that mimic neonatal mortality rate (NMR) data from the 2014 Kenya Demographic and Health Survey (KDHS2014). For the traditional approach, undercoverage/overcoverage depends arbitrarily on the aggregation grid resolution, while the new approach exhibits low sensitivity. The differences between the two aggregation approaches increase as the population of an area decreases. The differences are substantial at the second administrative level and finer, but also at the first administrative level for some population quantities. We find differences between the proposed and traditional approach are consistent with those we observe in an application to NMR data from the KDHS2014.Comment: main manuscript: 33 pages, 5 figures, 5 tables; supplemental materials: 15 pages, 2 figures, 15 table

Estimating Under Five Mortality in Space and Time in a Developing World Context

Accurate estimates of the under-5 mortality rate (U5MR) in a developing world context are a key barometer of the health of a nation. This paper describes new models to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is, wishing to estimate U5MR across regions and years, and to investigate the association between the U5MR and spatially-varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980 - 2014 using data from demographic health surveys (DHS). We use a binomial likelihood with fixed effects for the urban/rural stratification to account for the complex survey design. We carry out smoothing using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in U5MR in the period 1980 - 2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. Temperature, precipitation and a measure of malaria infection prevalence were candidates for inclusion in the covariate model.Comment: 36 pages, 11 figure

meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice

This paper introduces the R package meta4diag for implementing Bayesian bivariate meta-analyses of diagnostic test studies. Our package meta4diag is a purpose-built front end of the R package INLA. While INLA offers full Bayesian inference for the large set of latent Gaussian models using integrated nested Laplace approximations, meta4diag extracts the features needed for bivariate meta-analysis and presents them in an intuitive way. It allows the user a straightforward model specification and offers user-specific prior distributions. Further, the newly proposed penalized complexity prior framework is supported, which builds on prior intuitions about the behaviors of the variance and correlation parameters. Accurate posterior marginal distributions for sensitivity and specificity as well as all hyperparameters, and covariates are directly obtained without Markov chain Monte Carlo sampling. Further, univariate estimates of interest, such as odds ratios, as well as the summary receiver operating characteristic (SROC) curve and other common graphics are directly available for interpretation. An interactive graphical user interface provides the user with the full functionality of the package without requiring any R programming. The package is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=meta4diag/ and its usage will be illustrated using three real data examples

Impact of Jittering on Raster- and Distance-based Geostatistical Analyses of DHS Data

Fine-scale covariate rasters are routinely used in geostatistical models for mapping demographic and health indicators based on household surveys from the Demographic and Health Surveys (DHS) program. However, the geostatistical analyses ignore the fact that GPS coordinates in DHS surveys are jittered for privacy purposes. We demonstrate the need to account for this jittering, and we propose a computationally efficient approach that can be routinely applied. We use the new method to analyse the prevalence of completion of secondary education for 20--49 year old women in Nigeria in 2018 based on the 2018 DHS survey. The analysis demonstrates substantial changes in the estimates of spatial range and fixed effects compared to when we ignore jittering. Through a simulation study that mimics the dataset, we demonstrate that accounting for jittering reduces attenuation in the estimated coefficients for covariates and improves predictions. The results also show that the common approach of averaging covariate values in windows around the observed locations does not lead to the same improvements as accounting for jittering