Cancer rates over age, time, and place: insights from stochastic models of heterogeneous populations

Individuals at the same age in the same population differ along numerous risk factors that affect their chances of various causes of death. The frail and susceptible tend to die first. This differential selection may partially account for some of the puzzles in cancer epidemiology, including the lack of apparent progress in reducing cancer incidence and mortality rates over time. (AUTHORS)

Economic progress as cancer risk factor. II: Why is overall cancer risk higher in more developed countries?

Analysis of data on cancer incidence rates in different countries at different time periods revealed positive association between overall cancer risk and economic progress. Typical explanations of this phenomenon involve improved cancer diagnostics and elevated exposure to carcinogens in industrial countries. Here we provide evidence from human and experimental animal studies suggesting that some other factors associated with high economic development and Western life style may primarily increase the proportion of susceptible to cancer individuals in a population and thus contribute to elevated cancer risks in industrial countries. These factors include (but not limited to): (i) better medical and living conditions that “relax” environmental selection and increase share of individuals prone to chronic inflammation; (ii) several medicines and foods that are not carcinogenic themselves but affect the metabolism of established carcinogens; (iii) nutrition enriched with growth factors; (iv) delayed childbirth. The latter two factors may favor an increase in both cancer incidence rate and longevity in a population. This implies the presence of a trade-off between cancer and aging: factors that postpone aging may simultaneously enhance organism’s susceptibility to several cancers. Key words: cancer risk, individual susceptibility, economic progress, aging

A Bayesian correlated frailty model applied to Swedish breast cancer data

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Unobserved heterogeneity in a model with cure fraction applied to breast cancer

We suggest a cure-mixture model to analyze bivariate time-to-event data, as motivated by the paper of Chatterjee and Shih (2001, Biometrics 57, 779 - 786), but with a simpler estimation procedure and the correlated gamma-frailty model instead of the shared gamma-frailty model. This approach allows us to deal with left truncated and right censored lifetime data and accounts for heterogeneity as well as for an insusceptible (cure) fraction in the study population. We perform a simulation study to evaluate the properties of the estimates in the proposed model and apply it to breast cancer incidence data for 5,857 Swedish female monozygotic and dizygotic twin pairs from the so-called old cohort of the Swedish Twin Registry. This model is used to estimate the size of the susceptible fraction and the correlation between the frailties of the twin partners. Possible extensions, advantages and limitations of the proposed method are discussed.Sweden, breast, cancer, correlation, survival, twins

Modifications of the EM algorithm for survival influenced by an unobserved stochastic process

AbstractLet Y=(Yt)t≥0) be an unobserved random process which influences the distribution of a random variable T which can be interpreted as the time to failure. When a conditional hazard rate corresponding to T is a quadratic function of covariates, Y, the marginal survival function may be represented by the first two moments of the conditional distribution of Y among survivors. Such a representation may not have an explicit parametric form. This makes it difficult to use standard maximum likelihood procedures to estimate parameters - especially for censored survival data. In this paper a generalization of the EM algorithm for survival problems with unobserved, stochastically changing covariates is suggested. It is shown that, for a general model of the stochastic failure model, the smoothing estimates of the first two moments of Y are of a specific form which facilitates the EM type calculations. Properties of the algorithm are discussed

Modeling of immune life history and body growth: the role of antigen burden

In this paper, a recently developed mathematical model of age related changes in population of peripheral T cells (Romanyukha, Yashin, 2003) is used to describe ontogenetic changes of the immune system. The treatise is based on the assumption of linear dependence of antigen load from basal metabolic rate, which, in turn, depends on body mass following the allometric relationship – 3/4 power scaling law (Kleiber, 1932; West, Brown, 2005). Energy cost of antigen burden, i.e. the energy needed to produce and maintain immune cells plus the energy loss due to infectious diseases, is estimated and used as a measure of the immune system effectiveness. The dependence of optimal resource allocation from the parameters of antigen load is studied.

Senescence can play an essential role in modelling and estimation of vector based epidemiological indicators: demographical approach

In the paper basic epidemiological indicators, produced by an aging population of vectors, are calculated. In the study we follow two lines: calculations for demographically structured population and individual life-history approach. We discuss the advantages and limitations of these approaches and compare the results of our calculations with epidemiological indicators obtained for non-aging population of vectors.Gibraltar, age effect, disease control, gerontology

Individual fecundity dynamically predicts remaining life expectancy in medflies

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Genetic analysis of cause of death in a bivariate lifetime model with dependent competing risks

A mixture model in multivariate survival analysis is presented, whereby heterogeneity among subjects creates divergent paths for the individual's risk of experiencing an event (i.e., disease), as well as for the associated length of survival. Dependence among competing risks is included and rendered testable. This method is an extension of the bivariate correlated gamma-frailty model. It is applied to a data set on Danish twins, for whom cause-specific mortality is known. The use of multivariate data solves the identifiability problem which is inherent in the competing risk model of univariate lifetimes. We analyse the in uence of genetic and environmental factors on frailty. Using a sample of 1470 monozygotic (MZ) and 2730 dizygotic (DZ) female twin pairs, we apply five genetic models to the associated mortality data, focusing particularly on death from coronary heart disease (CHD). Using the best fitting model, the inheritance risk of death from CHD was 0.39 (standard error 0.13). The results from this model are compared with the results from earlier analysis that used the restricted model, where the independence of competing risks was assumed. Comparing both cases, it turns out, that heritability of frailty on mortality due to CHD change substantially. Despite the inclusion of dependence, analysis confirms the significant genetic component to an individual's risk of mortality from CHD. Whether dependence or independence is assumed, the best model for analysis with regard to CHD mortality risks is an AE model, implying that additive factors are responsible for heritability in susceptibility to CHD. The paper ends with a discussion of limitations and possible further extensions to the model presented.

Season of birth influences life span of Mediterranean fruit flies, rats and mice

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