Dept of Biostatistics, Emory University 6/19/2018 Models and Hotspots Lance A. Waller Dept of Biostatistics, Emory University lwaller@sph.emory.edu 2.9. 2.9 Waller Models and Hotspots.ppt

Primary question What do you want to do? Model? Predict? Identify hotspots? As they happen? Anytime? What is a “hotspot” anyway? 2.9.

Models Statistical models for spatio-temporal data Spatially adjusted time series (time-rich, space-poor data), vs. Temporally evolving spatial models (space-rich, time-poor data) What do you want to model? Mean (trend) Correlation What about fit? Global vs. local (hotspots?) 2.9.

Hotspots What makes a hotspot “interesting”? Typically, want to find anomalies Something that is different from what you expect. Use model to define what you expect. Use hotspot detection to identify “surprises”. IMPORTANT: What kind of anomaly you find depends on what method you use. 2.9.

Spatial Statistics for Cancer Surveillance Martin Kulldorff Harvard Medical School and Harvard Pilgrim Health Care 2.9.

Two Applications of Spatial Data and GIS in Cancer Research Studies of Specific Hypotheses: Evaluate the relationship between cancer and geographical variables of interest such as radon, pesticide use or income levels, adjusting for geographical variation. Surveillance: Evaluate the geographical variation of cancer, adjusting for known or suspected variables such as age, gender or income. 2.9.

Reasons for Geographical Cancer Surveillance Disease Etiology Known Etiology but Unknown Presence Health Services Public Education Outbreak Detection New Diseases 2.9.

Cancer Prevention and Control Are people in some geographical area at higher risk of brain cancer? This could be due to environmental, socio-economical, behavioral or genetic risk factors. 2.9.

Cancer Prevention and Control Are there geographical differences in the access to and/or use of early detection programs, such as mammography screening? 2.9.

Cancer Prevention and Control Are there geographical differences in the access to and/or use of state-of-the-art breast cancer treatment? 2.9.

Different Types of Cancer Data Count Data: Incidence, Mortality, Prevalence Categorical Data: Stage, Histology, Treatment Continuous Data: Survival 2.9.

For Incidence and Mortality Poisson Data Numerator: Number of Cases Denominator: Person-years at risk 2.9.

Bernoulli Data (0/1 Data) For Prevalence Bernoulli Data (0/1 Data) Numerator: People with Thyroid Cancer Denominator: Those without Thyroid Cancer Note: When prevalence is low, a Poisson model is a very good approximation for Bernoulli data. 2.9.

For Stage, Histology and Treatment Bernoulli Data (0/1 Data) Numerator: Cases of a specific type, e.g. late stage. Denominator: All cases. Ordinal Data For example: Stage 1, 2, 3, 4 2.9.

(Censored Data is Common) For Survival Survival Data Length of Survival (Censored Data is Common) 2.9.

Data Aggregation (spatial resolution) Exact Location Census Block Group Zip Code Census Tract County State 2.9.

Data Aggregation Same level of aggregation usually needed due to data availability. Less aggregation is typically better as more information is retained. Many statistical methods can be used irrespectively of aggregation level. 2.9.

Course Outline Geographical Cancer Surveillance 1. Mapping Rates and Proportions 2. Smoothed Maps 3. Tests for Spatial Randomness 4. Spatial Scan Statistic 5. Global Clustering Tests 6. Brain Cancer Mortality 7. Survival Data 2.9.

Course Outline Space-Time Cancer Surveillance 8. Space-Time Scan Statistic for the Early Detection of Disease Outbreaks Statistical Software 9. SaTScan Demonstration 2.9.

Comments and Questions 6/19/2018 Comments and Questions WELCOME AT ANY TIME Software and Slide Presentation AVAILABLE FROM THE WEB 2.9. 2.9 Waller Models and Hotspots.ppt