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PubMed1970

Cancer report.

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Breast Neoplasms↗
PubMed1982

Cancer survival corrected for heterogeneity in patient withdrawal.

Survival from cancer over a certain time period is often measured by the 'relative survival rate'. This is the ratio of the observed survival rate in the group of patients to the survival rate expected in a group of people in the general population, who are similar to the patients with respect to all of the possible factors affecting survival at the beginning of the period, except for the disease under study. When patterns of patient withdrawal differ for a number of subgroups of patients with equal relative survival rates, the current method of derivation of the relative survival rate is biased. A method based on the concept of an 'expected life table' is proposed for removal of the bias. Examples based on material from the Finnish Cancer Registry suggest that the practical performance of the proposed method is better than that of other alternatives, even when the relative survival rates in the subgroups are not equal.

Adult↗
PubMed1995

Cigarette use and the estimation of lung cancer attributable to radon in the United States.

Residential exposure to radioactive radon and its decay products has been estimated to account for 10-12% of all lung cancer deaths in the U.S. It has been difficult to evaluate fully the impact of cigarette smoking, the most important cause of lung cancer, on this estimate, because factors for patterns of tobacco use have not been included in the risk models, since risk models are derived from studies of underground miners exposed to radon and detailed data on smoking are limited. Lung cancer risk estimates for exposure to radon progeny in smoker and non-smoker populations are obtained by applying the same risk model to each population group, thereby assuming the joint effects of smoking and exposure to radon progeny are multiplicative. However, in miners, joint relative risks (RR) for the two exposures are most consistent with an intermediate relationship between multiplicative and additive, so that the present approach likely results in an overestimate of risk in smokers and an underestimate of risk in nonsmokers. Lubin et al. (National Institutes of Health Publication No. 94-3644, 1994) present an ad hoc approach for adjusting risk models to incorporate smoking status. The approach is based on the relative magnitude of the effects of radon progeny in smokers and nonsmokers and therefore may not be applicable to non-miner populations if the proportion of smokers and the RR for smoking differ. We show that the modification can be derived explicitly by assuming an arithmetic mixture model for the joint RR for smoking and exposure to radon progeny. In this way, smoking parameters in the population of interest (the proportion of smokers and the RR of smoking) can be used directly to adjust radon progeny risk models and obtain risk estimates that are specific for smokers and nonsmokers. With an intermediate RR relationship for smoking and radon progeny, the attributable percentage of lung cancer deaths from residential radon may be twofold greater in non-smokers than in smokers.

Air Pollution, Indoor↗
PubMed1977

[Incidence statistics of malignant stomach neoplasms in the Estonian SSR during 1963-1972].

Crude and age-adjusted (UICC world standard) morbidity rates of gastric cancer have been presented. The linear-regression analysis of age-ajusted rates for 1963--1972 shows a significant mean annual decrease of 2.9% for males and 4.9% for females. In 1968--1971 the ratio of Russian to Estonian age-ajusted rates was 1.9--2.0 for urban and 1.7 for rural population. 46.1 +/- 2.7% of Russian migrants (gastric cancer patients) have migrated to Estonia from high risk areas. 38.3--61.3% of males and 27.1--47.7% of females have a microscopic verification of the diagnosis.

Adult↗
PubMed1997

Nonparametric estimation of solid cancer size at metastasis and probability of presenting with metastasis at detection.

Two probabilistic characterizations of the relationship between the size of primary cancers and the occurrence of metastases are considered. The first is the distribution of tumor size at the point of metastatic transition, while the second is the probability that detectable metastases are present when cancer comes to medical attention. We show that the general model for this problem developed by Kimmel and Flehinger (1991, Biometrics 47, 987-1004) is unidentifiable. That is, the quantities to be estimated cannot be uniquely determined by the available information. An identifiable model that includes one limiting model of Kimmel and Flehinger (1991) as a special case is developed by using odds ratios. Under the new model, both quantities of interest are estimated by using a nonparametric maximum likelihood approach. The results are applied to two lung cancer data sets.

Adenocarcinoma↗
PubMed1998

Testing for trend with count data.

Among the tests that can be used to detect dose-related trends in count data from toxicological studies are nonparametric tests such as the Jonckheere-Terpstra and likelihood-based tests, for example, based on a Poisson model. This paper was motivated by a data set of tumor counts in which conflicting conclusions were obtained using these two tests. To define situations where one test may be preferable, we compared the small and large sample performance of these two tests as well as a robust and conditional version of the likelihood-based test in the absence and presence of a dose-related trend for both Poisson and overdispersed Poisson data. Based on our results, we suggest using the Poisson test when little overdispersion is present in the data. For more overdispersed data, we recommend using the robust Poisson test for highly discrete data (response rate lower than 2-3) and the robust Poisson test or the Jonckheere-Terpstra test for moderately discrete or continuous data (average responses larger than 2 or 3). We also studied the effects of dose metameter misspecification. A clear effect on efficiency was seen when the 'wrong' dose metameter was used to compute the test statistic. In general, unless there is strong reason to do otherwise, we recommend the use of equally spaced dose levels when applying the Poisson or robust Poisson test for trend.

Animals↗