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- Aggregate data design; auxiliary variables; ecological bias; efficiency; two-phase sampling; within-area confounding (1)
- Auxillary variables; Biased sampling schemes; Ecological fallacy; Hiearchical models (1)
- Bayesian Methods; Ecological Bias; Ecological Correlation Studies; Hierarchical Models; Prior Distributions; Spatial Epidemiology; Standardization. (1)
- Ecological bias; Efficiency; Outcome-dependent sampling; Two-phase sampling; Within-area confounding (1)
- Empirical/statistical models; analytical methods; epidemiology; particulate matter; criteria pollutants (1)
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Statistical Analysis Of Air Pollution Panel Studies: An Illustration, Holly Janes, Lianne Sheppard, Kristen Shepherd
Statistical Analysis Of Air Pollution Panel Studies: An Illustration, Holly Janes, Lianne Sheppard, Kristen Shepherd
UW Biostatistics Working Paper Series
The panel study design is commonly used to evaluate the short-term health effects of air pollution. Standard statistical methods for analyzing longitudinal data are available, but the literature reveals that the techniques are not well understood by practitioners. We illustrate these methods using data from the 1999 to 2002 Seattle panel study. Marginal, conditional, and transitional approaches for modeling longitudinal data are reviewed and contrasted with respect to their parameter interpretation and methods for accounting for correlation and dealing with missing data. We also discuss and illustrate techniques for controlling for time-dependent and time-independent confounding, and for exploring and summarizing …
The Combination Of Ecological And Case-Control Data, Sebastien Haneuse, Jon Wakefield
The Combination Of Ecological And Case-Control Data, Sebastien Haneuse, Jon Wakefield
UW Biostatistics Working Paper Series
Ecological studies, in which data are available at the level of the group, rather than at the level of the individual, are susceptible to a range of biases due to their inability to characterize within-group variability in exposures and confounders. In order to overcome these biases, we propose a hybrid design in which ecological data are supplemented with a sample of individual-level case-control data. We develop the likelihood for this design and illustrate its benefits via simulation, both in bias reduction when compared to an ecological study, and in efficiency gains relative to a conventional case-control study. An interesting special …
The Combination Of Ecological And Case-Control Data, Sebastien Haneuse, Jon Wakefield
The Combination Of Ecological And Case-Control Data, Sebastien Haneuse, Jon Wakefield
UW Biostatistics Working Paper Series
Ecological studies, in which data are available at the level of the group, rather than at the level of the individual, are susceptible to a range of biases due to their inability to characterize within-group variability in exposures and confounders. In order to overcome these biases, we propose a hybrid design in which ecological data are supplemented with a sample of individual-level case-control data. We develop the likelihood for this design and illustrate its benefits via simulation, both in bias reduction when compared to an ecological study, and in efficiency gains relative to a conventional case-control study. An interesting special …
Hierarchical Models For Combining Ecological And Case-Control Data, Sebastien Haneuse, Jon Wakefield
Hierarchical Models For Combining Ecological And Case-Control Data, Sebastien Haneuse, Jon Wakefield
UW Biostatistics Working Paper Series
The ecological study design suffers from a broad range of biases that result from the loss of information regarding the joint distribution of individual-level outcomes, exposures and confounders. The consequent non-identifiability of individual-level models cannot be overcome without additional information; we combine ecological data with a sample of individual-level case-control data. The focus of this paper is hierarchical models to account for between-group heterogeneity. Estimation and inference pose serious compu- tational challenges. We present a Bayesian implementation, based on a data augmentation scheme where the unobserved data are treated as auxiliary variables. The methods are illustrated with a dataset of …
Disease Mapping And Spatial Regression With Count Data, Jon Wakefield
Disease Mapping And Spatial Regression With Count Data, Jon Wakefield
UW Biostatistics Working Paper Series
In this paper we provide critical reviews of methods suggested for the analysis of aggregate count data in the context of disease mapping and spatial regression. We introduce a new method for picking prior distributions, and propose a number of refinements of previously-used models. We also consider ecological bias, mutual standardization, and choice of both spatial model and prior specification. We analyze male lip cancer incidence data collected in Scotland over the period 1975–1980, and outline a number of problems with previous analyses of these data. A number of recommendations are provided. In disease mapping studies, hierarchical models can provide …