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- 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)
- Effect size (1)
- Empirical/statistical models; analytical methods; epidemiology; particulate matter; criteria pollutants (1)
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Articles 1 - 7 of 7
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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 …
Assessing The Adequacy Of Variance Function In Heteroscedastic Regression Models, Lan Wang, Xiao-Hua Andrew Zhou
Assessing The Adequacy Of Variance Function In Heteroscedastic Regression Models, Lan Wang, Xiao-Hua Andrew Zhou
UW Biostatistics Working Paper Series
Heteroscedastic data arise in many applications. In a heteroscedastic regression model, the variance is often taken as a parametric function of the covariate or the regression mean. This paper presents a kernel-smoothing based nonparametric test for checking the adequacy of such a postulated variance structure. The test does not need to specify a parametric distribution for the random errors. It has an asymptotical normal distribution under the null hypothesis and is powerful against a large class of alternatives. Numerical simulations and an illustrative example are provided.
Relative Risk Regression In Medical Research: Models, Contrasts, Estimators, And Algorithms, Thomas Lumley, Richard Kronmal, Shuangge Ma
Relative Risk Regression In Medical Research: Models, Contrasts, Estimators, And Algorithms, Thomas Lumley, Richard Kronmal, Shuangge Ma
UW Biostatistics Working Paper Series
The relative risk or prevalence ratio is a natural and familiar summary of association between a binary outcome and an exposure or intervention. For rare events, the relative risk can be approximately estimated by logistic regression. For common events estimation is more difficult. We review proposed estimation algorithms for relative risk regression. Some of these give inconsistent estimates or invalid standard errors. We show that the methods that give correct inference can be viewed as arising from a family of quasilikelihood estimating functions for the same generalized linear model, differing in their efficiency and in their robustness to outlying values …
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 …
Reliability, Effect Size, And Responsiveness And Intraclass Correlation Of Health Status Measures Used In Randomized And Cluster-Randomized Trials, Paula Diehr, Lu Chen, Donald L. Patrick, Ziding Feng, Yutaka Yasui
Reliability, Effect Size, And Responsiveness And Intraclass Correlation Of Health Status Measures Used In Randomized And Cluster-Randomized Trials, Paula Diehr, Lu Chen, Donald L. Patrick, Ziding Feng, Yutaka Yasui
UW Biostatistics Working Paper Series
Background: New health status instruments are described by psychometric properties, such as Reliability, Effect Size, and Responsiveness. For cluster-randomized trials, another important statistic is the Intraclass Correlation for the instrument within clusters. Studies using better instruments can be performed with smaller sample sizes, but better instruments may be more expensive in terms of dollars, lost opportunities, or poorer data quality due to the response burden of longer instruments. Investigators often need to estimate the psychometric properties of a new instrument, or of an established instrument in a new setting. Optimal sample sizes for estimating these properties have not been studied …
Different Public Health Interventions Have Varying Effects, Paula Diehr, Anne B. Newman, Liming Cai, Ann Derleth
Different Public Health Interventions Have Varying Effects, Paula Diehr, Anne B. Newman, Liming Cai, Ann Derleth
UW Biostatistics Working Paper Series
Objective: To compare performance of one-time health interventions to those that change the probability of transitioning from one health state to another. Study Design and Setting: We used multi-state life table methods to estimate the impact of eight types of interventions on several outcomes. Results: In a cohort beginning at age 65, curing all the sick persons at baseline would increase life expectancy by 0.23 years and increase years of healthy life by .54 years. An equal amount of improvement could be obtained with a 12% decrease in the probability of getting sick, a 16% increase in the probability of …