Ram C. Tiwari

Cancer Statistics, 2005

Each year, the American Cancer Society estimates the number of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute and mortality data from the National Center for Health Statistics. Incidence and death rates are age‐standardized to the 2000 US standard million population. A total of 1,372,910 new cancer cases and 570,280 deaths are expected in the United States in 2005. When deaths are aggregated by age, cancer has surpassed heart disease as the leading cause of death for persons younger than 85 since 1999. When adjusted to delayed reporting, cancer incidence rates stabilized in men from 1995 through 2001 but continued to increase by 0.3% per year from 1987 through 2001 in women. The death rate from all cancers combined has decreased by 1.5% per year since 1993 among men and by 0.8% per year since 1992 among women. The mortality rate has also continued to decrease from the three most common cancer sites in men (lung and bronchus, colon and rectum, and prostate) and from breast and colorectal cancers in women. Lung cancer mortality among women has leveled off after increasing for many decades. In analyses by race and ethnicity, African American men and women have 40% and 20% higher death rates from all cancers combined than White men and women, respectively. Cancer incidence and death rates are lower in other racial and ethnic groups than in Whites and African Americans for all sites combined and for the four major cancer sites. However, these groups generally have higher rates for stomach, liver, and cervical cancers than Whites. Furthermore, minority populations are more likely to be diagnosed with advanced stage disease than are Whites. Progress in reducing the burden of suffering and death from cancer can be accelerated by applying existing cancer control knowledge across all segments of the population.

Cancer Statistics, 2004†

Each year, the American Cancer Society estimates the number of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival rates based on incidence data from the National Cancer Institute and mortality data from the National Center for Health Statistics. Incidence and mortality rates are age standardized to the 2000 US standard million population. A total of 1,368,030 new cancer cases and 563,700 deaths are expected in the United States in 2004. Incidence rates stabilized among men from 1995 through 2000 but continued to increase among females by 0.4% per year from 1987 through 2000. Mortality rates have decreased by 1.5% per year since 1992 among men, but have stabilized from 1998 through 2000 among women. Cancer death rates continued to decrease from the three major cancer sites in men (lung and bronchus, colon and rectum, and prostate) and from female breast and colorectal cancers in women. In analyses by race and ethnicity, African‐American men and women have 40% and 20% higher death rates from all cancers combined compared with White men and women, respectively. Cancer incidence and mortality rates are lower in other racial and ethnic groups than in Whites and African Americans for all sites combined and for the four major cancer sites. However, these groups generally have higher rates for stomach, liver, and cervical cancers than do Whites. Furthermore, minority populations are more likely to be diagnosed with advanced stage disease than are Whites. Progress in reducing the burden from cancer can be accelerated by applying existing cancer control knowledge into practice among all segments of the population.

Efficient interval estimation for age-adjusted cancer rates

The age-adjusted cancer rates are defined as the weighted average of the age-specific cancer rates, where the weights are positive, known, and normalized so that their sum is 1. Fay and Feuer developed a confidence interval for a single age-adjusted rate based on the gamma approximation. Fay used the gamma approximations to construct an F interval for the ratio of two age-adjusted rates. Modifications of the gamma and F intervals are proposed and a simulation study is carried out to show that these modified gamma and modified F intervals are more efficient than the gamma and F intervals, respectively, in the sense that the proposed intervals have empirical coverage probabilities less than or equal to their counterparts, and that they also retain the nominal level. The normal and beta confidence intervals for a single age-adjusted rate are also provided, but they are shown to be slightly liberal. Finally, for comparing two correlated age-adjusted rates, the confidence intervals for the difference and for the ratio of the two age-adjusted rates are derived incorporating the correlation between the two rates. The proposed gamma and F intervals and the normal intervals for the correlated age-adjusted rates are recommended to be implemented in the Surveillance, Epidemiology and End Results Program of the National Cancer Institute.

Estimating average annual per cent change in trend analysis

Trends in incidence or mortality rates over a specified time interval are usually described by the conventional annual per cent change (cAPC), under the assumption of a constant rate of change. When this assumption does not hold over the entire time interval, the trend may be characterized using the annual per cent changes from segmented analysis (sAPCs). This approach assumes that the change in rates is constant over each time partition defined by the transition points, but varies among different time partitions. Different groups (e.g. racial subgroups), however, may have different transition points and thus different time partitions over which they have constant rates of change, making comparison of sAPCs problematic across groups over a common time interval of interest (e.g. the past 10 years). We propose a new measure, the average annual per cent change (AAPC), which uses sAPCs to summarize and compare trends for a specific time period. The advantage of the proposed AAPC is that it takes into account the trend transitions, whereas cAPC does not and can lead to erroneous conclusions. In addition, when the trend is constant over the entire time interval of interest, the AAPC has the advantage of reducing to both cAPC and sAPC. Moreover, because the estimated AAPC is based on the segmented analysis over the entire data series, any selected subinterval within a single time partition will yield the same AAPC estimate—that is it will be equal to the estimated sAPC for that time partition. The cAPC, however, is re-estimated using data only from that selected subinterval; thus, its estimate may be sensitive to the subinterval selected. The AAPC estimation has been incorporated into the segmented regression (free) software Joinpoint, which is used by many registries throughout the world for characterizing trends in cancer rates. Copyright

A New Method of Estimating United States and State‐level Cancer Incidence Counts for the Current Calendar Year

The American Cancer Society (ACS) has published the estimated number of new cancer cases and deaths in the current year for the United States that are commonly used by cancer control planners and the media. The methods used to produce these estimates have changed over the years as data (incidence) and statistical models improved. In this paper we present a new method that uses statistical models of cancer incidence that incorporate potential predictors of spatial and temporal variation of cancer occurrence and that account for delay in case reporting and then projects these estimated numbers of cases ahead 4 years using a piecewise linear (joinpoint) regression method. Based on evidence presented here that the new method produces more accurate estimates of the number of new cancer cases for years and areas for which data are available for comparison, the ACS has elected to use it to estimate the number of new cancer cases in Cancer Facts & Figures 2007 and in Cancer Statistics, 2007.

Quantile comparison functions in two-sample problems, with application to comparisons of diagnostic markers

Abstract In this article a control percentile test, a chi-squared test, and a Kolmogorov-type test are proposed for comparing two distributions from incomplete survival data. These tests are obtained by examining a vertical shift comparison function at a single point, a finite number of points, and an entire set of points on an interval. The proposed methods also have applications in receiver operating characteristic (ROC) analysis, which has been widely used in such diverse fields as signal detection theory, psychology, epidemiology, and medicine. The results are derived under very general conditions that hold for the well-known random censorship and random truncation models. The performances of the proposed procedures are studied using Monte Carlo simulation. The methods are applied to analyze Mayo Clinic ovarian carcinoma data.

A New Method of Predicting US and State‐Level Cancer Mortality Counts for the Current Calendar Year

Every January for more than 40 years, the American Cancer Society (ACS) has estimated the total number of cancer deaths that are expected to occur in the United States and individual states in the upcoming year. In a collaborative effort to improve the accuracy of the predictions, investigators from the National Cancer Institute and the ACS have developed and tested a new prediction method. The new method was used to create the mortality predictions for the first time in Cancer Statistics, 2004 and Cancer Facts & Figures 2004. The authors present a conceptual overview of the previous ACS method and the new state‐space method (SSM), and they review the results of rigorous testing to determine which method provides more accurate predictions of the observed number of cancer deaths from the years 1997 to 1999. The accuracy of the methods was compared using squared deviations (the square of the predicted minus observed values) for each of the cancer sites for which predictions are published as well as for all cancer sites combined. At the national level, the squared deviations were not consistently lower for every cancer site for either method, but the average squared deviations (averaged across cancer sites, years, and sex) was substantially lower for the SSM than for the ACS method. During the period 1997 to 1999, the ACS estimates of deaths were usually greater than the observed numbers for all cancer sites combined and for several major individual cancer sites, probably because the ACS method was less sensitive to recent changes in cancer mortality rates (and associated counts) that occurred for several major cancer sites in the early and mid 1990s. The improved accuracy of the new method was particularly evident for prostate cancer, for which mortality rates changed dramatically in the late 1980s and early 1990s. At the state level, the accuracy of the two methods was comparable. Based on these results, the ACS has elected to use the new method for the annual prediction of the number of cancer deaths at the national and state levels.

Application of a local linear autoregressive model to BOD time series

In this paper, we analyze the biochemical oxygen demand data collected over two years from McDowell Creek, Charlotte, North Carolina, U.S.A., by fitting an autoregressive model with time-dependent coefficients. The local linear smoothing technique is developed and implemented to estimate the coefficient functions of the autoregressive model. A nonparametric version of the Akaike information criterion is developed to determine the order of the model and to select the optimal bandwidth. We also propose a hypothesis testing technique, based on the residual sum of squares and F-test, to detect whether certain coefficients in the model are really varying or whether any variables are significant. The approximate null distributions of the test are provided. The proposed model has some advantages, such as it is determined completely by data, it is easily implemented and it provides a better prediction. Copyright

Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini‐Sentinel pilot

This manuscript describes the current statistical methodology available for active postmarket surveillance of pre‐specified safety outcomes using a prospective incident user concurrent control cohort design with existing electronic healthcare data.

Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data

In geographical spatial epidemiology and disease surveillance, all the existing spatial scan methods for cluster detection using continuous data are designed for evaluating clusters of individuals and analyzing individual-level data. Motivated by growing demands to study the spatial heterogeneity of continuous measures in population data, such as mortality rates, survival rates, average body mass indexes and pollution at state, county, and census tract levels, we propose a weighted normal scan statistic for investigating the clusters of the cells (geographic units such as counties) with unusual high/low continuous regional measures, where the weights reflect the uncertainty of the regional measures or sample size (number of observed cases) in the cells. Power, precision, the effect of the weights, and the sensitivity of the proposed test statistic to data from various distributions are investigated through intensive simulation. The method is applied to 1988–2002 stage I and II lung cancer survival data in Los Angeles County in order to search for clusters of geographic units with high/low survival rates in a short-term/long-term survival after diagnosis, and to 1999–2003 breast cancer age-adjusted mortality rate data in the U.S. collected by the Surveillance, Epidemiology and End Results (SEER) program in order to evaluate the clustering pattern of counties with high mortality rate. The proposed method is included in the latest release of the SaTScan software (www.satscan.org).