Smoothing geographical data, particularly rates of disease.
This paper proposes a linear smoother for geographically-defined data that consist of standardized rates (for example, adjusted for age). The smoother is viewed as a special case of one that applies to data in the form of ratios, and situations are described under which such a smoother can and cannot be useful. Its application to mortality rates due to prostate cancer in both whites and non-whites demonstrates its potential to highlight features in the data that might otherwise remain obscure. Some open questions concerning inference of apparent trends are discussed.
Kafadar
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Department of Mathematics, University of Colorado-Denver 80217-3364, USA.
D001741:Black or African American; D017677:Age Distribution; D000704:Analysis of Variance; D015982:Bias; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D008297:Male; D017711:Nonlinear Dynamics; D011471:Prostatic Neoplasms / Q000401:mortality; D015996:Survival Rate; D014481:United States / Q000453:epidemiology; D044465:White People