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

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Presentation transcript:

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

Outline 1.Motivation Petrochemical exposure in relation to childhood brain and leukemia cancers 2.Cumulative Geographic Residuals Unconditional Conditional 3.Application Childhood Leukemia Childhood Brain Cancer 4.Discussion Future Research

Taiwan Petrochemical Study Matched Case-Control Study 3 controls per case Matched on Age and Gender Resided in one of 26 of the overall 38 administrative districts of Kaohsiung County, Taiwan Controls selected using national identity numbers (not dependent on location).

Study Population Due to dropout approximately 50% 3 to 1 matching, 40% 2 to 1 matching, and 10% 1 to 1 matching. LeukemiaBrain Cancer Cases Controls

Map of Kaohsiung

Conditional Logistic Model Type of Matching: 1 case to M s controls Data Structure: Assume that conditional on, an unobserved stratum- specific intercept, and given the logit link, implies, The conditional likelihood, conditioning on is,

Conditional Residual Then define a residual as, where is the solution to. => Use these correlated Residuals to test for patterns based on location.

Conditional Cluster Detection Define the Cumulative Geographic Residual Moving Block Process as,

Conditional Cumulative Residual However, the distribution of, is hard to define analytically, but we have found another distribution that is asymptotically equivalent, which consists of a fixed component of data and random variables

Significance Test Testing the NULL Simulate N realizations of by repeatedly simulating, while fixing the data at their observed values. Calculate P-value

Application Study: Kaohsiung, Taiwan Matched Case-Control Study Method: Conditional Cumulative Geographic Residual Test (Normal and Mixed Discrete)

Results Odds Ratio (p-values) Marginally Significant Clustering for both outcomes without adjusting for smoking history.

Childhood Leukemia

Childhood Brain Cancer

Discussion Cumulative Geographic Residuals Unconditional and Conditional Methods for Binary Outcomes Can find multiple significant hotspots holding type I error at appropriate levels. Not computer intensive compared to other cluster detection methods Taiwan Study Found a possible relationship between Childhood Leukemia and Petrochemical Exposure, but not with the outcome Childhood Brain Cancer.

Discussion Future Research Failure Time Data Recurrent Events Relocation of Study Participants Surveillance