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Journal of the American Statistical Association Volume 104, 2009 - Issue 487
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Applications and Case Studies

Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data

Lan Huang Lan Huang is Mathematical Statistician, Contractor, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Ram Tiwari is Associate Director, Office of Biostatistics, CDER, FDA, Silver Spring, MD 20993 . Zhaohui Zou is Statistical Programmer, Information Management Services, Silver Spring, MD 20904 . Martin Kulldorff is Associate Professor, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215 . Eric J. Feuer is Chief, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Most of Ram C. Tiwari’s work was done while he was at the National Cancer Institute, NIH. The views expressed in this article do not necessarily represent those of the U.S. Food and Drug Administration, and of the National Cancer Institute. The authors wish to thank the editor, associate editor, and the referees for their valuable comments and suggestions. Kulldorff gratefully acknowledge the support of National Institute of Child Health and Human Development grant R01 HD048852.
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Ram C. Tiwari Lan Huang is Mathematical Statistician, Contractor, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Ram Tiwari is Associate Director, Office of Biostatistics, CDER, FDA, Silver Spring, MD 20993 . Zhaohui Zou is Statistical Programmer, Information Management Services, Silver Spring, MD 20904 . Martin Kulldorff is Associate Professor, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215 . Eric J. Feuer is Chief, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Most of Ram C. Tiwari’s work was done while he was at the National Cancer Institute, NIH. The views expressed in this article do not necessarily represent those of the U.S. Food and Drug Administration, and of the National Cancer Institute. The authors wish to thank the editor, associate editor, and the referees for their valuable comments and suggestions. Kulldorff gratefully acknowledge the support of National Institute of Child Health and Human Development grant R01 HD048852.
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Zhaohui Zou Lan Huang is Mathematical Statistician, Contractor, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Ram Tiwari is Associate Director, Office of Biostatistics, CDER, FDA, Silver Spring, MD 20993 . Zhaohui Zou is Statistical Programmer, Information Management Services, Silver Spring, MD 20904 . Martin Kulldorff is Associate Professor, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215 . Eric J. Feuer is Chief, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Most of Ram C. Tiwari’s work was done while he was at the National Cancer Institute, NIH. The views expressed in this article do not necessarily represent those of the U.S. Food and Drug Administration, and of the National Cancer Institute. The authors wish to thank the editor, associate editor, and the referees for their valuable comments and suggestions. Kulldorff gratefully acknowledge the support of National Institute of Child Health and Human Development grant R01 HD048852.
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Martin Kulldorff Lan Huang is Mathematical Statistician, Contractor, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Ram Tiwari is Associate Director, Office of Biostatistics, CDER, FDA, Silver Spring, MD 20993 . Zhaohui Zou is Statistical Programmer, Information Management Services, Silver Spring, MD 20904 . Martin Kulldorff is Associate Professor, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215 . Eric J. Feuer is Chief, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Most of Ram C. Tiwari’s work was done while he was at the National Cancer Institute, NIH. The views expressed in this article do not necessarily represent those of the U.S. Food and Drug Administration, and of the National Cancer Institute. The authors wish to thank the editor, associate editor, and the referees for their valuable comments and suggestions. Kulldorff gratefully acknowledge the support of National Institute of Child Health and Human Development grant R01 HD048852.
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Eric J. Feuer Lan Huang is Mathematical Statistician, Contractor, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Ram Tiwari is Associate Director, Office of Biostatistics, CDER, FDA, Silver Spring, MD 20993 . Zhaohui Zou is Statistical Programmer, Information Management Services, Silver Spring, MD 20904 . Martin Kulldorff is Associate Professor, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215 . Eric J. Feuer is Chief, Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852 . Most of Ram C. Tiwari’s work was done while he was at the National Cancer Institute, NIH. The views expressed in this article do not necessarily represent those of the U.S. Food and Drug Administration, and of the National Cancer Institute. The authors wish to thank the editor, associate editor, and the referees for their valuable comments and suggestions. Kulldorff gratefully acknowledge the support of National Institute of Child Health and Human Development grant R01 HD048852.
Pages 886-898 | Received 01 Nov 2007, Published online: 01 Jan 2012
 
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Abstract

In geographical spatial epidemiology and disease surveillance, all the existing spatial scan methods for cluster detection using continuous data are designed for evaluating clusters of individuals and analyzing individual-level data. Motivated by growing demands to study the spatial heterogeneity of continuous measures in population data, such as mortality rates, survival rates, average body mass indexes and pollution at state, county, and census tract levels, we propose a weighted normal scan statistic for investigating the clusters of the cells (geographic units such as counties) with unusual high/low continuous regional measures, where the weights reflect the uncertainty of the regional measures or sample size (number of observed cases) in the cells. Power, precision, the effect of the weights, and the sensitivity of the proposed test statistic to data from various distributions are investigated through intensive simulation. The method is applied to 1988–2002 stage I and II lung cancer survival data in Los Angeles County in order to search for clusters of geographic units with high/low survival rates in a short-term/long-term survival after diagnosis, and to 1999–2003 breast cancer age-adjusted mortality rate data in the U.S. collected by the Surveillance, Epidemiology and End Results (SEER) program in order to evaluate the clustering pattern of counties with high mortality rate. The proposed method is included in the latest release of the SaTScan software (www.satscan.org).

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