Statistical Significance: Tests for Spatial Randomness.

Slides:

Advertisements

Similar presentations

Brain Development in ADHD The NIMH Study. Processing Steps in Pictures MRIClassificationExtraction of Surfaces Cortical Thickness Blurred Thickness Map.

Advertisements

Surgery volume and operative mortality: A re-examination using fixed-effects regression Amresh Hanchate, PhD Section of General Internal Medicine Boston.

Sources and effects of bias in investigating links between adverse health outcomes and environmental hazards Frank Dunstan University of Wales College.

Survival Analysis In many medical studies, the primary endpoint is time until an event occurs (e.g. death, remission) Data are typically subject to censoring.

Significance Testing.  A statistical method that uses sample data to evaluate a hypothesis about a population  1. State a hypothesis  2. Use the hypothesis.

Hotspot/cluster detection methods(1) Spatial Scan Statistics: Hypothesis testing – Input: data – Using continuous Poisson model Null hypothesis H0: points.

Global Clustering Tests. Tests for Spatial Randomness H 0 : The risk of disease is the same everywhere after adjustment for age, gender and/or other covariates.

Inference for Regression

Spatial statistics Lecture 3.

Brain Cancer Mortality in the United States Joint work with: Zixing Fang, UCLA David Gregorio, Univ Connecticut.

Urbanisation and spatial inequalities in health in Brazil and India Tarani ChandolaUniversity of Manchester Sergio BassanesiUFRGS - Universidade Federal.

SPATIAL DATA ANALYSIS Tony E. Smith University of Pennsylvania Point Pattern Analysis Spatial Regression Analysis Continuous Pattern Analysis.

SOCIOLOGY/ANTHROPOLOGY Soc 122 Introduction to Sociology General Education Requirement Category: Social Science.

Zakaria A. Khamis GE 2110 GEOGRAPHICAL STATISTICS GE 2110.

ODAC May 3, Subgroup Analyses in Clinical Trials Stephen L George, PhD Department of Biostatistics and Bioinformatics Duke University Medical Center.

Estimation and Reporting of Heterogeneity of Treatment Effects in Observational Comparative Effectiveness Research Prepared for: Agency for Healthcare.

A Spatial Scan Statistic for Survival Data Lan Huang, Dep Statistics, Univ Connecticut Martin Kulldorff, Harvard Medical School David Gregorio, Dep Community.

GIS and Spatial Statistics: Methods and Applications in Public Health

LINEAR REGRESSION: Evaluating Regression Models. Overview Standard Error of the Estimate Goodness of Fit Coefficient of Determination Regression Coefficients.

Correlation and Autocorrelation

Detecting Spatial Clustering in Matched Case-Control Studies Andrea Cook, MS Collaboration with: Dr. Yi Li November 4, 2004.

C82MCP Diploma Statistics School of Psychology University of Nottingham 1 Designing Experiments In designing experiments we: Manipulate the independent.

CHI-SQUARE TEST OF INDEPENDENCE

Intro to Statistics for the Behavioral Sciences PSYC 1900 Lecture 17: Nonparametric Tests & Course Summary.

DEPENDENT SAMPLES t-TEST What is the Purpose?What Are the Assumptions?How Does it Work?

Cumulative Geographic Residual Test Example: Taiwan Petrochemical Study Andrea Cook.

PY 427 Statistics 1Fall 2006 Kin Ching Kong, Ph.D Lecture 6 Chicago School of Professional Psychology.

Detecting Spatial Clustering in Matched Case-Control Studies Andrea Cook, MS Collaboration with: Dr. Yi Li December 2, 2004.

Chapter 12 Inferring from the Data. Inferring from Data Estimation and Significance testing.

Sample Size Determination

Spatial Clusters and Pattern Analysis

Spatial Statistics for Cancer Surveillance Martin Kulldorff Harvard Medical School and Harvard Pilgrim Health Care.

Mapping Rates and Proportions. Incidence rates Mortality rates Birth rates Prevalence Proportions Percentages.

Geographic Information Science

Chapter 4 Measures of Variability

Survival Analysis: From Square One to Square Two

Introduction to Survival Analysis August 3 and 5, 2004.

Using ArcGIS/SaTScan to detect higher than expected breast cancer incidence Jim Files, BS Appathurai Balamurugan, MD, MPH.

HSTAT1101: 27. oktober 2004 Odd Aalen

The Spatial Scan Statistic. Null Hypothesis The risk of disease is the same in all parts of the map.

Chapter 8 Introduction to Hypothesis Testing

Multiple Choice Questions for discussion

Maria C. Mejia de Grubb, MD, MPH; Barbara Kilbourne, PhD; Courtney Kihlberg, MD, MSPH; and Robert Levine, MD. Department of Family and Community Medicine.

Journal Club Hallie Lee PharmD Candidate 2013 Mercer University COPHS PHA 618 Geriatrics-Continuous Care Multivitamins in the Prevention of Cardiovascular.

GM Monawar Hosain State Cancer Epidemiologist Bureau of Public Health Statistics and Informatics NH Division of Pubic Health Services.

Temporal, Geographic and Demographic Trends of Early Onset Breast Cancer (EOBC) in Hampden County, MA Holly S. Mason MD FACS Jane Garb MS.

Data Sources-Cancer Betsy A. Kohler, MPH, CTR Director, Cancer Epidemiology Services New Jersey Department of Health and Senior Services.

Spatial Analysis & Vulnerability Studies START 2004 Advanced Institute IIASA, Laxenburg, Austria Colin Polsky May 12, 2004 Graduate School of Geography.

1 THE ROLE OF COVARIATES IN CLINICAL TRIALS ANALYSES Ralph B. D’Agostino, Sr., PhD Boston University FDA ODAC March 13, 2006.

Taking ‘Geography’ Seriously: Disaggregating the Study of Civil Wars. John O’Loughlin and Frank Witmer Institute of Behavioral Science University of Colorado.

Experimental Research Methods in Language Learning Chapter 10 Inferential Statistics.

Lecture: Forensic Evidence and Probability Characteristics of evidence Class characteristics Individual characteristics  features that place the item.

Chapter 8 Hypothesis Testing I. Significant Differences  Hypothesis testing is designed to detect significant differences: differences that did not occur.

Unit 1 Sections 1-1 & : Introduction What is Statistics?  Statistics – the science of conducting studies to collect, organize, summarize, analyze,

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and Methods and Applications CHAPTER 15 ANOVA : Testing for Differences among Many Samples, and Much.

Analyzing the Geospatial Imbalance of the Primary Care Physician Labor Supply in the Contiguous United States By Russ Frith University of W. Florida Capstone.

Hypothesis Testing and the T Test. First: Lets Remember Z Scores So: you received a 75 on a test. How did you do? If I said the mean was 72 what do you.

1 Using dynamic path analysis to estimate direct and indirect effects of treatment and other fixed covariates in the presence of an internal time-dependent.

Instrument design Essential concept behind the design Bandit Thinkhamrop, Ph.D.(Statistics) Department of Biostatistics and Demography Faculty of Public.

Gary L. Kamer Statistician OSB/DBS. 2 Statistical Issues at Time of PMA Review Clinical Study Design Excess All-cause Late Mortality (31 to 365 days)

GEOGRAPHIC CLUSTERS OF HEAD & NECK CANCER IN FLORIDA Recinda Sherman, MPH, CTR Florida Cancer Data Systems NAACCR Detroit, June 7, 2007.

Increasing Power in Association Studies by using Linkage Disequilibrium Structure and Molecular Function as Prior Information Eleazar Eskin UCLA.

Task 2. Average Nearest Neighborhood

Dept of Biostatistics, Emory University

The Role of Poverty in Prostate Cancer in African-Americans

12 Inferential Analysis.

Spatial statistics Topic 4 2/2/2007.

12 Inferential Analysis.

Introduction to Hypothesis Testing

Sampling and Power Slides by Jishnu Das.

Presentation transcript:

Statistical Significance: Tests for Spatial Randomness

Whether or not there are true geographical differences in risk, there will always be some geographical patterns apparent to the naked eye. As in all medical research, it is important to evaluate whether observed patterns/results are likely to be due to chance or not.

Breast Cancer Incidence, Relative Risks Age-Adjusted, Indirect Standardization

Brain Cancer Mortality, Children

Brain Cancer Mortality, Adults

Tests for Spatial Randomness Null Hypothesis: The risk of disease is the same in all parts of the map.

Covariate Adjustments For incidence and mortality analyses, it is important to adjust for age, and sometimes for other variables as well. This is done using indirect standardization, so that a covariate-adjusted expected number of cases are obtained for each census area. Can be used with any test for spatial randomness.

Tests for Spatial Randomness Global Clustering Tests Cluster Detection Tests Focused Tests Three Different Types: Complementary. Used for different purposes.

Global Clustering Tests Evaluates whether clustering exist as a global phenomena throughout the map, without pinpointing the location of specific clusters.

Global Clustering Tests Moran’s I, 1950 Mantel-Bailar’s Test, 1970 Cuzick-Edwards k-NN Test, 1990 Tango’s Maximized Excess Events Test, 2000 etc.

Cluster Detection Tests Determine the location and statistical significance of clusters without prior assumptions about their locations.

Cluster Detection Tests Turnbull’s CEPP, 1990 Spatial Scan Statistic, 1995 etc.

Focused Tests Determine whether there is a cluster around a pre-specified point or linear feature.

Focused Tests Fixed Cut-Off, Lyon et al Isotonic Regression, Stone 1988 Lawson-Waller’s Score Test, 1992,93 Bithell’s Linear Rank Score Test, 1995 etc.