Bounds in Competing Risks Models and the War on Cancer

In 1971, President Nixon declared war on cancer. Thirty years later, many declared this war a failure: the age‐adjusted mortality rate from cancer in 2000 was essentially the same as in the early 1970s. Meanwhile the age‐adjusted mortality rate from cardiovascular disease fell dramatically. Since the causes that underlie cancer and cardiovascular disease are likely dependent, the decline in mortality rates from cardiovascular disease may partially explain the lack of progress in cancer mortality. Because competing risks models (used to model mortality from multiple causes) are fundamentally unidentified, it is difficult to estimate cancer trends. We derive bounds for aspects of the underlying distributions without assuming that the underlying risks are independent. We then estimate changes in cancer and cardiovascular mortality since 1970. The bounds for the change in duration until death for either cause are fairly tight and suggest much larger improvements in cancer than previously estimated.     

Nurturing Life with Dreams: Therapeutic Dream Work with Cancer Patients

This paper describes the use of dream work as a component of individual counseling for cancer patients. Motivated by the work of experts in the field, who use dream work with their clients facing life-threatening illness and corroborated by a review of the literature, this therapeutic modality was introduced with cancer patients at a social service agency. The clinical work seems to indicate that the use of dreams helps create a safe environment for therapy by encouraging clients to discuss the emotional aspects of their disease and introducing topics, like death and dying, which are normally difficult to discuss.     

Parameterization and inference for nonparametric regression problems

We consider local likelihood or local estimating equations, in which a multivariate function () is estimated but a derived function () of () is of interest. In many applications, when most naturally formulated the derived function is a non-linear function of (). In trying to understand whether the derived non-linear function is constant or linear, a problem arises with this approach: when the function is actually constant or linear, the expectation of the function estimate need not be constant or linear, at least to second order. In such circumstances, the simplest standard methods in nonparametric regression for testing whether a function is constant or linear cannot be applied. We develop a simple general solution which is applicable to nonparametric regression, varying-coefficient models, nonparametric generalized linear models, etc. We show that, in local linear kernel regression, inference about the derived function () is facilitated without a loss of power by reparameterization so that () is itself a component of (). Our approach is in contrast with the standard practice of choosing () for convenience and allowing ()> to be a non-linear function of (). The methods are applied to an important data set in nutritional epidemiology.     

Radon as a Tracer of Lung Changes Induced by Smoking

After smoking, exposure to radon and its progeny is the second leading cause of lung cancer. The probability of inducing lung carcinomas by inhaled radon progeny depends on the deposited radiation dose, and is significantly affected by physiological and morphometric changes induced by smoking. Due to irritation of the airways, the inhalation of cigarette smoke leads to the hyperproduction of mucus. Two concurrent processes occur: on one hand, increased production of mucus protects the target cells against radiation damage; on the other hand, in the case of long-term smokers, a chronic lung obstruction develops, causing an increase in the radiation dose to the lungs. Depending on the duration and intensity of smoking, these processes contribute to the final radiation dose with different weights. The primary objective of this study was to investigate to what extent these smoke-induced changes can modify the resulting absorbed dose of inhaled radon progeny relative to healthy nonsmokers. Since the bronchial dose depends on the degree of lung tissue damage, we have used this dose as a tool for detecting the effects of smoking on the lung epithelium. In other words, the biological effect of radon served as a tracer of changes induced by smoking.     

How Tautological Are Interspecies Correlations of Carcinogenic Potencies?

Crouch and Wilson demonstrated a strong correlation between carcinogenic potencies in rats and mice, supporting the extrapolation from mouse to man. Bernstein et al. , however, show that the observed correlation is mainly a statistical artifact of bioassay design. Crouch et al. have a comeback. This paper will review the arguments and present some new data. The correlation is largely (but not totally) tautological, confirming results in Bernstein et al.     

Biological Models of Carcinogenesis and Quantitative Cancer Risk Assessment

Biologically-based models of carcinogenesis were originally developed to explain certain quanti-tative phenomena associated with carcinogenesis, and to provide a framework within which questions regarding the process could be addressed. Some limitations in the use of these models for quantitative cancer risk assessment are discussed.     

How Do Cancer Risks Predicted From Animal Bioassays Compare with the Epidemiologic Evidence? The Case of Ethylene Dibromide

Cancer risks for ethylene dibromide (EDB) were estimated by fitting several linear non-threshold additive models to data from a gavage bioassay. Risks predicted by these models were compared to the observed cancer mortality among a cohort of workers occupationally exposed to the same chemical. Models that accounted for the shortened latency period in the gavaged rats predicted upper bound risks that were within a factor of 3 of the observed cancer deaths. Data from an animal inhalation study of EDB also were compatible with the epidemiologic data. These findings contradict those of Ramsey et al. (1978), who reported that extrapolation from animal data produced highly exaggerated risk estimates for EDB-exposed workers. This paper explores the reasons for these discrepant findings.     

Ambiguous carcinogens and their regulation

Examination of five animal and one human studies suggest that certain agents increase the incidence of some cancers but simultaneously reduce the incidence of other cancers. Yellow die #3, for example, sharply increases the incidence of liver tumors but practically eliminates naturally occurring leukemia/lymphoma in F-344 male rates. Such ambiguity in the action of presumed carcinogens suggests that caution must be used by regulatory bodies in proscribing suspected carcinogens, or even in recommending changes in lifestyle or dietary habits as a means of reducing incidence of cancer.     

Improving Scientists'Judgments of Risk

Scientists disagree in their risk analyses because they use intuitive judgments to generalize results from the laboratory to circumstances not yet studied. If this assertion is correct, techniques intended to reduce intuitive judgments and increase analytical cognition should reduce scientific dispute. The results of a test case involving allegedly high risk and sharp dispute among scientists are described.     

Extending the Stochastic Two-Stage Model of Carcinogenesis to Include Self-Regulation of the Nonmalignant Cell Population

One of the challenges of introducing greater biological realism into stochastic models of cancer induction is to find a way to represent the homeostatic control of the normal cell population over its own size without complicating the analysis too much to obtain useful results. Current two-stage models of carcinogenesis typically ignore homeostatic control. Instead, a deterministic growth path is specified for the population of "normal" cells, while the population of "initiated" cells is assumed to grow randomly according to a birth-death process with random immigrations from the normal population. This paper introduces a simple model of homeostatically controlled cell division for mature tissues, in which the size of the nonmalignant population remains essentially constant over time. Growth of the nonmalignant cell population (normal and initiated cells) is restricted by allowing cells to divide only to fill the "openings" left by cells that die or differentiate, thus maintaining the constant size of the nonmalignant cell population. The fundamental technical insight from this model is that random walks, rather than birth-and-death processes, are the appropriate stochastic processes for describing the kinetics of the initiated cell population. Qualitative and analytic results are presented, drawn from the mathematical theories of random walks and diffusion processes, that describe the probability of spontaneous extinction and the size distribution of surviving initiated populations when the death/differentiation rates of normal and initiated cells are known. The constraint that the nonmalignant population size must remain approximately constant leads to much simpler analytic formulas and approximations, flowing directly from random walk theory, than in previous birth-death models.(ABSTRACT TRUNCATED AT 250 WORDS)