In public health research, survival data denoting different causes of death are often collected across geographical regions. The data may cause invalid inference, however, if employed in a general competing risk model, which assumes constant relationships between risk factors and competing risks across regions. In addition, some applications might require spatially varying cause-specific hazard ratios. To address these limitations, this study proposes a geographically weighted cause-specific hazard regression (GWCHR) model to estimate spatially varying coefficients with a common spatial scale across multiple covariates. In identifying spatial variations of coefficients, we assign distance-based weights for each location in likelihood construction. We choose the bandwidth in the weighting function according to suitable selection criteria. We analyze the asymptotic properties of the proposed GWCHR model in detail. Our simulation studies compare the finite sample performance of the proposed model with general competing risk models. We apply the proposed method to prostate cancer data from Korea’s National Health Information Service database to examine the spatially varying effects of environmental and social factors on second primary cancers for prostate cancer patients.

Co-authors: Mina Kim, Yeong-Hwa Kim, Molin Wang, Se Young Choi