Breast cancers are clinically heterogeneous based on tumor markers. The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program provides baseline data on these tumor markers for reporting cancer burden and trends over time in the US general population. 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 been collected since 1992 but historically was not well-reported, with missingness rates as high as 25% in early years. Previous methods used to correct estimates of breast cancer incidence or ER-related odds or prevalence 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 predictive mean matching procedure that can be used to multiply impute missing ER status under either an MAR or a missing not at random assumption and apply it to the SEER breast cancer data (1992-2012). The imputation procedure 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, although the magnitude of differences tends to be small. For the types of analyses typically of primary interest, we recommend imputing SEER breast cancer biomarkers under an MAR assumption, given the small apparent differences under MAR and missing not at random assumptions. Copyright © 2016 John Wiley & Sons, Ltd.
Keywords: cancer surveillance data; multiple imputation; nonignorable missingness; predictive mean matching.
Copyright © 2016 John Wiley & Sons, Ltd.