Detection of spatial variations in temporal trends with a quadratic function

Paula Moraga, Martin Kulldorff, Andrew B. Lawson, Duncan Lee, Ying MacNab

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


Methods for the assessment of spatial variations in temporal trends (SVTT) are important tools for disease surveillance, which can help governments to formulate programs to prevent diseases, and measure the progress, impact, and efficacy of preventive efforts already in operation. The linear SVTT method is designed to detect areas with unusual different disease linear trends. In some situations, however, its estimation trend procedure can lead to wrong conclusions. In this article, the quadratic SVTT method is proposed as alternative of the linear SVTT method. The quadratic method provides better estimates of the real trends, and increases the power of detection in situations where the linear SVTT method fails. A performance comparison between the linear and quadratic methods is provided to help illustrate their respective properties. The quadratic method is applied to detect unusual different cervical cancer trends in white women in the United States, over the period 1969 to 1995.
Original languageEnglish (US)
Title of host publicationStatistical Methods in Medical Research
PublisherSAGE Publications
Number of pages16
StatePublished - Aug 1 2016
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2021-03-16


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