A spatial scan statistic for survival data.

A spatial scan statistic for survival data. Research Abstract Details 

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A spatial scan statistic for survival data. Abstract Text:

Lan Huang,Martin Kulldorff,David Gregorio,

Spatial scan statistics with Bernoulli and Poisson models are commonly used for geographical disease surveillance and cluster detection. These models, suitable for count data, were not designed for data with continuous outcomes. We propose a spatial scan statistic based on an exponential model to handle either uncensored or censored continuous survival data. The power and sensitivity of the developed model are investigated through intensive simulations. The method performs well for different survival distribution functions including the exponential, gamma, and log-normal distributions. We also present a method to adjust the analysis for covariates. The cluster detection method is illustrated using survival data for men diagnosed with prostate cancer in Connecticut from 1984 to 1995.

A spatial scan statistic for survival data. Publishing Authors By Initials

L Huang,M Kulldorff,D Gregorio,

For similar investigative techniques: epidemiologic methods: statistics as topic: survival analysis research abstracts see: investigative techniques: epidemiologic methods: statistics as topic: survival analysis research

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A spatial scan statistic for survival data. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Biometrics

VOLUME: 63

Page Numbers: 109-18

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ISSN: 0006-341X

DAY: 3

MONTH: Mar

YEAR: 2007

A spatial scan statistic for survival data. Information

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LANGUAGE: eng

NlmUniqueID: 370625

A spatial scan statistic for survival data. Keywords Mesh Terms:

KEYWORDS: Survival Analysis

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Grant and Affiliation Information for A spatial scan statistic for survival data.

AFFILIATION: Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute (Contractor), 6116 Executive Boulevard, Rockville, Maryland 20852, USA. huangla@mail.nih.gov

Country: United States

AGENCY: United States PHS

GRANT: U50/CCU300860

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MEDLINETA: Biometrics

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