PT - JOURNAL ARTICLE
AU - Iachan, Ronaldo
AU - Berman, Lewis
AU - Kyle, Tonja M
AU - Martin, Kelly J.
AU - Deng, Yangyang
AU - Moyse, Davia N
AU - Middleton, Deirdre
AU - Atienza, Audie A
TI - Weighting Non-probability and Probability Sample Surveys in Describing Cancer Catchment Areas
AID - 10.1158/1055-9965.EPI-18-0797
DP - 2019 Jan 01
TA - Cancer Epidemiology Biomarkers & Prevention
PG - cebp.0797.2018
4099 - http://cebp.aacrjournals.org/content/early/2019/01/12/1055-9965.EPI-18-0797.short
4100 - http://cebp.aacrjournals.org/content/early/2019/01/12/1055-9965.EPI-18-0797.full
AB - Background:The Population Health Assessment initiative by National Cancer Institute (NCI) sought to enhance cancer centers' capacity to acquire, aggregate and integrate data from multiple sources, as well as to plan, coordinate, and enhance catchment area analysis activities. Methods: Key objectives of this initiative are pooling data and comparing local data with national data. A novel aspect of analyzing data from this initiative is the methodology used to weight datasets from sites that collected both probability and non-probability samples. This article describes the methods developed to weight data which cancer centers collected with combinations of probability and non-probability sampling designs. Results:We compare alternative weighting methods in particular for the hybrid probability and non-probability sampling designs employed by different cancer centers. We also include comparisons of local center data with national survey data from large probability samples. Conclusions:This hybrid approach to calculating statistical weights can be implemented both within cancer centers that collect both probability and non-probability samples with common measures. Aggregation can also apply to cancer centers that share common data elements, and target similar populations, but differ in survey sampling designs. Impact:Researchers interested in local versus national comparisons for cancer surveillance and control outcomes should consider various weighting approaches, including hybrid approaches, when analyzing their data.