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Accurate estimation of cancer mortality rates and the comparison across cancer sites, populations or time periods is crucial to public health, as identification of vulnerable groups who suffer the most from these diseases may lead to efficient cancer care and control with timely treatment. Because cancer mortality rate varies with age, comparisons require age-standardization using a reference population. The current method of using the Year 2000 Population Standard is standard practice, but serious concerns have been raised about its lack of justification. We have found that using the US Year 2000 Population Standard as reference overestimates prostate cancer mortality rates by 12-91% during the period 1970-2009 across all six sampled U.S. states, and also underestimates case fatality rates by 9-78% across six cancer sites, including female breast, cervix, prostate, lung, leukemia and colon-rectum. We develop a mean reference population method to minimize the bias using mathematical optimization theory and statistical modeling. The method corrects the bias to the largest extent in terms of squared loss and can be applied broadly to studies of many diseases.
Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdow
If Electronic Health Records contain a large amount of information about the patients condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its e
This paper proposes a two-fold factor model for high-dimensional functional time series (HDFTS), which enables the modeling and forecasting of multi-population mortality under the functional data framework. The proposed model first decomposes the HDF
This paper extends Bayesian mortality projection models for multiple populations considering the stochastic structure and the effect of spatial autocorrelation among the observations. We explain high levels of overdispersion according to adjacent loc
Many existing mortality models follow the framework of classical factor models, such as the Lee-Carter model and its variants. Latent common factors in factor models are defined as time-related mortality indices (such as $kappa_t$ in the Lee-Carter m