Standard Population

Abstract: Objective:Population-based cancer registration data in 2010 were collected,evaluated and analyzed by the National Central Cancer Registry(NCCR) of ChinaCancer incident new cases and cancer deaths were estimatedMethods:There were 219 cancer registries submitted cancer incidence and death data in 2010All data were checked and evaluated on basis of the criteria of data quality from NCCRTotal 145 registries’ data were qualified and accepted for cancer statistics in 2010Pooled data were stratified by urban/rural,area,sex,age group and cancer siteCancer incident cases and deaths were estimated using age-specific rates and national populationThe top ten common cancers in different groups,proportion and cumulative rate were also calculatedChinese census in 2000 and Segi’s population were used for age-standardized incidence/ mortality ratesResults:All 145 cancer registries(63 in urban and 82 in rural) covered a total of 158,403,248 population(92,433,739 in urban and 65,969,509 in rural areas)The estimates of new cancer incident cases and cancer deaths were 3,093,039 and 1,956,622 in 2010,respectivelyThe morphology verified cases(MV%) accounted for 6711% and 299% of incident cases were identified through death certifications only(DCO%) with mortality to incidence ratio(M/I) of 061The crude incidence rate was 23523/100,000(26865/100,000 in males,20021/100,000 in females),age-standardized incidence rates by Chinese standard population(ASIRC,2000) and by world standard population(ASIRW) were 18458/100,000 and 18149/100,000 with the cumulative incidence rate(0-74 years old) of 2111%The cancer incidence and ASIRC were 25641/100,000 and 18753/100,000 in urban areas whereas in rural areas,they were 21371/100,000 and 18110/100,000,respectivelyThe crude cancer mortality in China was 14881/100,000(18637/100,000 in males and 10942/100,000 in females),age-standardized incidence rates by Chinese standard population(ASMRC,2000) and by world standard population(ASMRW) were 11392/100,000 and 11286/100,0

Abstract: Comparing groups on the basis of survival is common in medical research. Survival time data require methods that properly account for the situation when the time of death is not observed because some subjects are still alive at the end of the study (censoring). In addition, methods are required that make no assumptions about the shape of the survival time distribution (nonparametric). There are widely used methods for statistical comparison and graphic display of survival of two samples. The log-rank test (1) provides a comparison of the observed number of deaths in each group versus the number that would be expected if the total mortality were distributed according to the proportion in each group. These statistical comparisons are often accompanied by Kaplan–Meier curves that provide a graphic display of the distribution of survivorship over time (2). This estimator, calculated from samples that are partially censored, is a monotonically non-increasing step function with steps at each observed death time. Although calculated separately for each group, these graphs are displayed simultaneously in a single plot to promote a visual comparison of survival over the entire study period. It is often of interest to compare the survival of a single sample to that of a defined reference population. For example, when a series of patients with a rare, life-threatening disease has been collected, it may be of interest to know if the study sample is experiencing the same survival as the demographically matched standard (general) population, according to actuarial tables. This is especially of interest when the disease is curable or not usually lethal and the age of onset is late in life. It is not appropriate to use methods developed for two-sample comparisons to do this analysis, because the variance would be incorrectly calculated and thus the P value would be invalid. It is possible to provide a single summary measure of the relative survival of a sample compared with a standard population by estimating the standardized mortality ratio (3). However, investigators often want to report a P value from a statistical test that compares the two populations—essentially a one-sample log-rank test. Although such tests are published in the statistical literature (4,5), medical investigators do not generally read this literature, and thus these tests are not widely known and used by this community. In fact, these articles (4,5) have been cited fewer than 10 times in the medical literature over the past 20 years. Some ad hoc methods have been devised for a one-sample survival test. For example, one approach is to use the actuarial tables to determine the expected remaining lifetime at the age of study entry for each of the subjects in the sample and then to treat these times as exact death times of a hypothetical sample of the same number of subjects from the reference population. It is possible then to calculate a two-sample log-rank test and report the resulting P value. However, this test is inappropriate because the variance would be incorrectly calculated and thus the P value would be invalid. Similar pitfalls arise in trying to obtain an accompanying graphical display that would appropriately represent the survival of the standard population in the same manner in which the Kaplan–Meier plot represents the sample. Because there are no methods that are widely cited in the medical literature, there is a tendency to develop ad hoc methods. For example, one approach is to calculate the expected remaining lifetime for each subject in the sample by using the actuarial tables matched by age, sex, and/or race. This set of numbers is then treated as exact observed death times, and the Kaplan–Meier estimator is calculated for this hypothetical population. This calculation results in a step function with the number of steps equal to the size of the sample being studied. This is not correct. As an illustration, suppose everyone in the sample began observation at the same age and, thus, would have the same expected remaining lifetime, say s. The survival distribution for the hypothetical matched sample representing the reference group would then have a value of 1 until s, at which point the curve would drop to 0. The correct method must use the entire remaining survival curve (calculated from the reference actuarial tables) for each subject. The purpose of this commentary is to describe both the simple one-sample log-rank test that is equivalent to the standardized mortality ratio and an estimate for survivorship in the matched standard population that allows a visual comparison of survivorship of the sample and standard populations. We will discuss the issues in designing a study that will rely on one-sample methods. The software to perform analyses discussed in this commentary can be found at our Web site: http://biostatistics.mgh.harvard. edu/biostatistics/resources.html (6). As an illustration, we use these methods to compare the survival of a small cohort of patients diagnosed with extra-mammary Paget’s disease at Massachusetts General Hospital with the survival of the general population (7).

Abstract: The Japan Cancer Surveillance Research Group is involved in cancer monitoring in Japan (1–3). This group estimated the cancer incidence in 2003 as part of the Monitoring of Cancer Incidence in Japan (MCIJ) project, on the basis of data collected from 13 of 31 population-based cancer registries: Miyagi, Yamagata, Chiba, Kanagawa, Niigata, Fukui, Shiga, Osaka, Tottori, Okayama, Hiroshima, Saga and Nagasaki. If data from all 31 registries were used, this would have led to a large underestimation of national cancer incidence because of under-registration. The methods of registry selection, estimation of incidence and the limitations of these methods have been explained in previous studies (4–6). There were two major methodologic changes in the present study: (i) this was the first time we invited all 31 population-based cancer registries in Japan to participate, and from these we selected the 13 cancer registries with high-quality data in order to estimate the national incidence, and (ii) in consideration of timeliness, we did not apply the moving average which calculates the annual mean incidence rates of a year by using preceding and following years, and we used 2003 data alone for the national estimation. Because of the enlargement of the coverage area, Hiroshima prefecture was newly selected as one of the registries with high-quality data for the national estimation, but the other registries remained since the previous estimations. In 2007, we estimated incidences with and without the moving average based on the same registry data to compare the two methods. In conclusion, the estimated incidence without the moving average was comparatively unstable from year to year, but the gaps of the incidence numbers between the two estimations were subtle. These new methods therefore do not bring about changes in the estimated incidence numbers. The number of incidences, crude rates, age-standardized rates and completeness of registration in 2003 are shown in Table 1, and the age-specific number of incidences and the rates according to sex and primary site are shown in Tables 2 and 3. The total number of incidences in Japan for 2003 was estimated as 620 011 (C00–C96). The time trends of age-standardized incidence rates for the five major sites and maleand female-specific sites in 1975–2003 are shown in Fig. 1 (standard population: the world population) and in Fig. 2 (standard population: the 1985 Japanese model population). The leading cancer site according to the crude and age-standardized incidence rates was the stomach for men and breast for women, as shown in Figs 1 and 2. The apparent increase in age-standardized incidence rates in 2003 is considered to be caused primarily by the development of hospital-based cancer registry in designated cancer care hospitals. The estimated cancer incidence data in Japan by sex, site, 5-year age group and calendar year during the period 1975–2003 are available as a booklet (7) and as an electronic database on the website (http://ganjoho.ncc.go.jp/ professional/statistics/statistics.html).