Abstract:
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The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program provides a rich source of data stratified according to tumor biomarkers that play an important role in cancer surveillance research. These tumor markers, however, are often prone to missing observations. In particular, estrogen receptor (ER) status, a key biomarker in the study of breast cancer, has not historically been well-reported, with missingness rates as high as 25% in some years. Previous methods used to correct estimates of breast cancer incidence or ER-related odds ratios for unknown ER status have relied on a missing-at-random (MAR) assumption. In this paper we explore the sensitivity of these key estimates to departures from MAR. We develop a hot deck procedure that can be used to impute missing ER status under either an MAR or an MNAR assumption, and apply it to the SEER breast cancer data. The hot deck uses the predictive power of the rich set of covariates available in the SEER registry, while also allowing us to investigate the impact of departures from MAR. We find some differences in inference under the two assumptions, though the magnitude of differences tends to be small.
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