1. Dept of Biostatistics, Emory University
6/19/2018
Models and Hotspots
Lance A. Waller
Dept of Biostatistics, Emory University
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2.9 Waller Models and Hotspots.ppt

2. Primary question What do you want to do? Model? Predict?
Identify hotspots?
As they happen?
Anytime?
What is a “hotspot” anyway?
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3. 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?)
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4. 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.
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5. Spatial Statistics for Cancer Surveillance
Martin Kulldorff
Harvard Medical School and
Harvard Pilgrim Health Care
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6. 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.
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7. Reasons for Geographical Cancer Surveillance
Disease Etiology
Known Etiology but Unknown Presence
Health Services
Public Education
Outbreak Detection
New Diseases
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8. 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.
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9. Cancer Prevention and Control
Are there geographical differences in the access to and/or use of early detection programs, such as mammography screening?
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10. Cancer Prevention and Control
Are there geographical differences in the access to and/or use of state-of-the-art breast cancer treatment?
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11. Different Types of Cancer Data
Count Data: Incidence, Mortality, Prevalence
Categorical Data: Stage, Histology, Treatment
Continuous Data: Survival
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12. For Incidence and Mortality
Poisson Data
Numerator: Number of Cases
Denominator: Person-years at risk
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13. 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.
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14. 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
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15. (Censored Data is Common)
For Survival
Survival Data
Length of Survival
(Censored Data is Common)
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16. Data Aggregation (spatial resolution)
Exact Location
Census Block Group
Zip Code
Census Tract
County
State
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17. 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.
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18. 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
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19. Course Outline Space-Time Cancer Surveillance
8. Space-Time Scan Statistic for the Early Detection of Disease Outbreaks
Statistical Software
9. SaTScan Demonstration
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20. Comments and Questions
6/19/2018
Comments and Questions
WELCOME AT ANY TIME
Software and Slide Presentation
AVAILABLE FROM THE WEB
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2.9 Waller Models and Hotspots.ppt