Showing papers by "Eric J. Feuer published in 2000"

Permutation tests for joinpoint regression with applications to cancer rates

Abstract: The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid-search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p-value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non-constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates.

Partitioning linear trends in age-adjusted rates.

Abstract: Objective: Surveillance of chronic diseases includes monitoring trends in age-adjusted rates in the general population. Statistics that are calculated to describe and compare trends include the annual percent change and the percent change for a specified time period. However, it is also of interest to determine the contribution specific diseases make to an overall trend in order to better understand the impact of interventions and changes in the prevalence of risk factors. The objective here is to provide a method for partitioning a linear trend in age-adjusted rates into disease-specific components.

Cumulative cause-specific mortality for cancer patients in the presence of other causes: a crude analogue of relative survival.

Abstract: A common population-based cancer progress measure for net survival (survival in the absence of other causes) of cancer patients is relative survival. Relative survival is defined as the ratio of a population of observed survivors in a cohort of cancer patients to the proportion of expected survivors in a comparable set of cancer-free individuals in the general public, thus giving a measure of excess mortality due to cancer. Relative survival was originally designed to address the question of whether or not there is evidence that patients have been cured. It has proven to be a useful survival measure in several areas, including the evaluation of cancer control efforts and the application of cure models. However, it is not representative of the actual survival patterns observed in a cohort of cancer patients. This paper suggests a measure for cumulative crude (in the presence of other causes) cause-specific probability of death for a population diagnosed with cancer. The measure does not use cause of death information which can be unreliable for population cancer registries. Point estimates and variances are derived for crude cause-specific probability of death using relative survival instead of cause of death information. Examples are given for men diagnosed with localized prostate cancer over the age of 70 and women diagnosed with regional breast cancer using Surveillance, Epidemiology and End Results (SEER) Program data. The examples emphasize the differences in crude and net mortality measures and suggest areas where a crude measure is more informative. Estimates of this type are especially important for older patients as new screening modalities detect cancers earlier and choice of treatment or even 'watchful waiting' become viable options. Published in 2000 by John Wiley & Sons, Ltd.

Cancer prevalence estimates based on tumour registry data in the Surveillance, Epidemiology, and End Results (SEER) Program

Abstract: Background The Connecticut Tumor Registry (CTR) has collected cancer data for a sufficiently long period of time to capture essentially all prevalent cases of cancer, and to provide unbiased estimates of cancer prevalence. However, prevalence proportions estimated from Connecticut data may not be representative of the total US, particularly for racial/ethnic subgroups. The purpose of this study is to apply the modelling approach developed by Capocaccia and De Angelis to cancer data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute to obtain more representative US site-specific cancer prevalence proportion estimates for white and black patients. Methods Incidence and relative survival were modelled and used to obtain estimated completeness indices of SEER prevalence proportions for all cancer sites combined, stomach, cervix uteri, skin melanomas, non-Hodgkin's lymphomas, lung and bronchus, colon/rectum, female breast, and prostate. For validation purposes, modelled completeness indices were computed for Connecticut and compared with empirical completeness indices (the ratio of Connecticut based prevalence proportion estimates using 1973-1993 data to 1940-1993 data). The SEER-based modelled completeness indices were used to adjust SEER prevalence proportion estimates for white and black patients. Results Model validation showed that the adjusted SEER cancer prevalence proportions provided reasonably unbiased prevalence proportion estimates in general, although more complex modelling of the completeness indices is necessary for female cancers of the colon, melanoma, breast, cervix, and all cancers combined. The SEER-based cancer prevalence proportions are incomplete for most cancer sites, more so for women, whites, and at older ages. For all cancers combined, prevalence proportions tended to be higher for whites than blacks. For the site-specific cancers this was true for stomach, prostate, cervix uteri, and lung and bronchus (men only). For colon/rectal cancers the prevalence proportions were higher for blacks through ages 59 (men) and 64 (women), and then for the remaining ages they were higher for whites. Prevalence proportions were lowest for stomach cancer and highest for prostate and female breast cancers. Men experienced higher prevalence proportions than women for skin melanomas, non-Hodgkin's lymphomas, lung and bronchus, and colon/rectal cancers. Conclusion The modelling approach applied to SEER data generally provided reasonable estimates of cancer prevalence. These estimates are useful because they are more representative of cancer prevalence than previously obtained and reported in the US.