Journal of Data Science

Abstract: Li and Tiwari (2008) recently developed a corrected Z-test statistic for comparing the trends in cancer age-adjusted mortality and incidence rates across overlapping geographic regions, by properly adjusting for the correlation between the slopes of the fitted simple linear regression equations. One of their key assumptions is that the error variances have unknown but common variance. However, since the age-adjusted rates are linear combinations of mortality or incidence counts, arising naturally from an underlying Poisson process, this constant variance assumption may be violated. This paper develops a weighted-least-squares based test that incorporates heteroscedastic error variances, and thus significantly extends the work of Li and Tiwari. The proposed test generally outperforms the aforementioned test through simulations and through application to the age-adjusted mortality data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute.

Abstract: Providing reliable estimates of the ratios of cancer incidence and mortality rates across geographic regions has been important for the National cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program as it profiles cancer risk factors as well decides cancer control planning. A fundamental difficulty, however, arises when such ratios have to be computed to compare the rate of a subregion (e.g., California) with that of a parent region (e.g., the US). Such a comparison is often made for policy-making purposes. Based on F-approximations as well as normal approximations, this paper provides new confidence intervals (CIs) for such rate ratios. Intensive simulations, which capture the real issues with the observed mortality data, reveal that these two CIs perform well. In general, for rare cancer sites, the F-intervals are often more conservative, and for moderate and common cancers, all intervals perform similarly.

Abstract: The likelihood of developing cancer during one’s lifetime is approximately one in two for men and one in three for women in the United States. Cancer is the second-leading cause of death and accounts for one in every four deaths. Evidence-based policy planning and decision making by cancer researchers and public health administrators are best accomplished with up-to-date age-adjusted site-specific cancer death rates. Because of the 3-year lag in reporting, forecasting methodology is employed here to estimate the current year’s rates based on complete observed death data up through three years prior to the current year. The authors expand the State Space Model (SSM) statistical methodology currently in use by the American Cancer Society (ACS) to predict age-adjusted cancer death rates for the current year. These predictions are compared with those from the previous Proc Forecast ACS method and results suggest the expanded SSM performs well.

Abstract: The interest in estimating the probability of cure has been increas ing in cancer survival analysis as the cure of some cancer sites is becoming a reality. Mixture cure models have been used to model the failure time data with the existence of long-term survivors. The mixture cure model assumes that a fraction of the survivors are cured from the disease of interest. The failure time distribution for the uncured individuals (latency) can be mod eled by either parametric models or a semi-parametric proportional hazards model. In the model, the probability of cure and the latency distribution are both related to the prognostic factors and patients’ characteristics. The maximum likelihood estimates (MLEs) of these parameters can be obtained using the Newton-Raphson algorithm. The EM algorithm has been proposed as a simple alternative by Larson and Dinse (1985) and Taylor (1995). in various setting for the cause-specific survival analysis. This approach is ex tended here to the grouped relative survival data. The methods are applied to analyze the colorectal cancer relative survival data from the Surveillance, Epidemiology, and End Results (SEER) program.