Marginal inference for hierarchical generalized linear mixed models with patterned covariance matrices using the Laplace approximation.

We develop hierarchical models and methods in a fully parametric approach to generalized linear mixed models for any patterned covariance matrix. The Laplace approximation is used to marginally estimate covariance parameters by integrating over all fixed and latent random effects. The Laplace approximation relies on Newton-Raphson updates, which also leads to predictions for the latent random effects.

Skew-normal antedependence models for skewed longitudinal data.

Antedependence models, also known as transition models, have proven to be useful for longitudinal data exhibiting serial correlation, especially when the variances and/or same-lag correlations are time-varying. Statistical inference procedures associated with normal antedependence models are well-developed and have many nice properties, but they are not appropriate for longitudinal data that exhibit considerable skewness. We propose two direct extensions of normal antedependence models to skew-normal antedependence models.

Antedependence models for nonstationary categorical longitudinal data with ignorable missingness: likelihood-based inference.

Time index-ordered random variables are said to be antedependent (AD) of order (p1 ,p2 , … ,pn ) if the kth variable, conditioned on the pk immediately preceding variables, is independent of all further preceding variables. Inferential methods associated with AD models are well developed for continuous (primarily normal) longitudinal data, but not for categorical longitudinal data. In this article, we develop likelihood-based inferential procedures for unstructured AD models for categorical longitudinal data.

The effects of local street network characteristics on the positional accuracy of automated geocoding for geographic health studies.

Background: Automated geocoding of patient addresses for the purpose of conducting spatial epidemiologic studies results in positional errors. It is well documented that errors tend to be larger in rural areas than in cities, but possible effects of local characteristics of the street network, such as street intersection density and street length, on errors have not yet been documented. Our study quantifies effects of these local street network characteristics on the means and the entire probability distributions of positional errors, using regression methods and tolerance intervals/regions, for more than 6000 geocoded patient addresses from an Iowa county.

Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering.

Automated geocoding of patient addresses is an important data assimilation component of many spatial epidemiologic studies. Inevitably, the geocoding process results in positional errors. Positional errors incurred by automated geocoding tend to reduce the power of tests for disease clustering and otherwise affect spatial analytic methods.

Spatial clustering of the failure to geocode and its implications for the detection of disease clustering.

Geocoding a study population as completely as possible is an important data assimilation component of many spatial epidemiologic studies. Unfortunately, complete geocoding is rare in practice. The failure of a substantial proportion of study subjects' addresses to geocode has consequences for spatial analyses, some of which are not yet fully understood.

Geocoding accuracy and the recovery of relationships between environmental exposures and health.

Background: This research develops methods for determining the effect of geocoding quality on relationships between environmental exposures and health. The likelihood of detecting an existing relationship - statistical power - between measures of environmental exposures and health depends not only on the strength of the relationship but also on the level of positional accuracy and completeness of the geocodes from which the measures of environmental exposure are made. This paper summarizes the results of simulation studies conducted to examine the impact of inaccuracies of geocoded addresses generated by three types of geocoding processes: a) addresses located on orthophoto maps, b) addresses matched to TIGER files (U.

Estimating the intensity of a spatial point process from locations coarsened by incomplete geocoding.

The estimation of spatial intensity is an important inference problem in spatial epidemiologic studies. A standard data assimilation component of these studies is the assignment of a geocode, that is, point-level spatial coordinates, to the address of each subject in the study population. Unfortunately, when geocoding is performed by the standard automated method of street-segment matching to a georeferenced road file and subsequent interpolation, it is rarely completely successful.

Modeling the probability distribution of positional errors incurred by residential address geocoding.

Background: The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors.

Geocoding in cancer research: a review.

There is now widespread agreement that geographic identifiers (geocodes) should be assigned to cancer records, but little agreement on their form and how they should be assigned, reported, and used. This paper reviews geocoding practice in relation to major purposes and discusses methods to improve the accuracy of geocoded cancer data. Differences in geocoding methods and materials introduce errors of commission and omission into geocoded data.