Gaithersburg, MD, United States
Gaithersburg, MD, United States

Time filter

Source Type

Zhu L.,U.S. National Institutes of Health | Pickle L.W.,StatNet Consulting LLC | Ghosh K.,University of Nevada, Las Vegas | Naishadham D.,Surveillance Research | And 10 more authors.
Cancer | Year: 2012

BACKGROUND. The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases. METHODS. Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age-specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay-adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted. RESULTS. The differences between the spatiotemporal model-estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method. CONCLUSIONS. The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts. © 2012 American Cancer Society.


Huang L.,Office of Biostatistics | Tiwari R.C.,Office of Biostatistics | Pickle L.W.,StatNet Consulting LLC | Zou Z.,Management Information Services Inc.
Statistics in Medicine | Year: 2010

In the field of cluster detection, a weighted normal model-based scan statistic was recently developed to analyze regional continuous data and to evaluate the clustering pattern of pre-defined cells (such as state, county, tract, school, hospital) that include many individuals. The continuous measures of interest are, for example, the survival rate, mortality rate, length of physical activity, or the obesity measure, namely, body mass index, at the cell level with an uncertainty measure for each cell. In this paper, we extend the method to search for clusters of the cells after adjusting for single/multiple categorical/continuous covariates. We apply the proposed method to 1999-2003 obesity data in the United States (US) collected by CDC's Behavioral Risk Factor Surveillance System with adjustment for age and race, and to 1999-2003 lung cancer age-adjusted mortality data by gender in the United States from the Surveillance Epidemiology and End Results (SEER Program) with adjustment for smoking and income. © 2010 John Wiley & Sons, Ltd.


Pickle L.W.,StatNet Consulting LLC | Pearson J.B.,StatNet Consulting LLC | Carr D.B.,George Mason University
Journal of Statistical Software | Year: 2015

The linked micromap graphical design uses color to link each geographic unit's name with its statistical graphic elements and map location across columns in a single row. Perceptual grouping of these rows into smaller chunks of data facilitates local focus and visual queries. Sorting the geographic units (the rows) in different ways can reveal patterns in the statistics, in the maps, and in the association between them. This design supports both exploration and communication in a multivariate geospatial context. This paper describes micromapST, an R package that implements the linked micromap graphical design specifically formatted for US state data, a common geographic unit used to display geographic patterns of health and other factors within the US. This package creates a graphic for the 51 geographic units (50 states plus DC) that fits on a single page, with states comprising the rows and state names, graphs and maps the columns. The graphical element for each state/column combination may represent a single statistical value, e.g., by a dot or horizontal bar, with or without an uncertainty measure. The distribution of values within each state, e.g., for counties, may be displayed by a boxplot. Two values per state may be represented by an arrow indicating the change in values, e.g., between two time points, or a scatter plot of the paired data. Categorical counts may be displayed as horizontal stacked bars, with optional standardization to percents or centering of the bars. Layout options include specification of the sort order for the rows, the graph/map linking colors, a vertical reference line and others. Output may be directed to the screen but is best displayed on a printer (or as a print image saved to any file format supported by R). The availability of a pre-defined linked micromap layout specifically for the 51 US states with graphical displays of single values, data distributions, change between two values, scatter plots of paired values, time series data and categorical data, facilitates quick exploration and communication of US state data for most common data types. ©2015, Journal of Statistical Software All rights received.


Chen H.-S.,U.S. National Institutes of Health | Portier K.,American Cancer Society | Ghosh K.,University of Nevada, Las Vegas | Naishadham D.,American Cancer Society | And 7 more authors.
Cancer | Year: 2012

Background: A study was undertaken to evaluate the temporal projection methods that are applied by the American Cancer Society to predict 4-year-ahead projections. Methods: Cancer mortality data recorded in each year from 1969 through 2007 for the United States overall and for each state from the National Center for Health Statistics was obtained. Based on the mortality data through 2000, 2001, 2002, and 2003, Projections were made 4 years ahead to estimate the expected number of cancer deaths in 2004, 2005, 2006, 2007, respectively, in the United States and in each state, using 5 projection methods. These predictive estimates were compared to the observed number of deaths that occurred for all cancers combined and 47 cancer sites at the national level, and 21 cancer sites at the state level. Results: Among the models that were compared, the joinpoint regression model with modified Bayesian information criterion selection produced estimates that are closest to the actual number of deaths. Overall, results show the 4-year-ahead projection has larger error than 3-year-ahead projection of death counts when the same method is used. However, 4-year-ahead projection from the new method performed better than the 3-year-ahead projection from the current state-space method. Conclusions: The Joinpoint method with modified Bayesian information criterion model has the smallest error of all the models considered for 4-year-ahead projection of cancer deaths to the current year for the United States overall and for each state. This method will be used by the American Cancer Society to project the number of cancer deaths starting in 2012. © 2012 American Cancer Society.


PubMed | StatNet Consulting LLC and U.S. National Institutes of Health
Type: Journal Article | Journal: International journal of health geographics | Year: 2016

Ratios of age-adjusted rates between a set of geographic units and the overall area are of interest to the general public and to policy stakeholders. These ratios are correlated due to two reasons-the first being that each region is a component of the overall area and hence there is an overlap between them; and the second is that there is spatial autocorrelation between the regions. Existing methods in calculating the confidence intervals of rate ratios take into account the first source of correlation. This paper incorporates spatial autocorrelation, along with the correlation due to area overlap, into the rate ratio variance and confidence interval calculations.The proposed method divides the rate ratio variances into three components, representing no correlation, overlap correlation, and spatial autocorrelation, respectively. Results applied to simulated and real cancer mortality and incidence data show that with increasing strength and scales in spatial autocorrelation, the proposed method leads to substantial improvements over the existing method. If the data do not show spatial autocorrelation, the proposed method performs as well as the existing method.The calculations are relatively easy to implement, and we recommend using this new method to calculate rate ratio confidence intervals in all cases.


PubMed | StatNet Consulting LLC
Type: Journal Article | Journal: Spatial and spatio-temporal epidemiology | Year: 2012

This article presents a brief history of U.S. small area mortality atlases published since 1975, focusing on their content, cartographic style and findings resulting from the maps. The atlas designs are evaluated on the basis of map design recommendations from cartographers and from a series of cognitive experiments on information extraction from rate maps. Despite some design limitations, the atlases adequately described patterns of U.S. mortality data, resulting in important etiologic findings and action to reduce cancer rates and health disparities.

Loading StatNet Consulting LLC collaborators
Loading StatNet Consulting LLC collaborators