Study of the bivariate survival data using frailty models based on Lévy processes

Frailty models allow us to take into account the non-observable inhomogeneity of individual hazard functions. Although models with time-independent frailty have been intensively studied over the last decades and a wide range of applications in survival analysis have been found, the studies based on the models with time-dependent frailty are relatively rare. In this paper, we formulate and prove two propositions related to the identifiability of the bivariate survival models with frailty given by a nonnegative bivariate Lévy process. We discuss parametric and semiparametric procedures for estimating unknown parameters and baseline hazard functions. Numerical experiments with simulated and real data illustrate these procedures. The statements of the propositions can be easily extended to the multivariate case

A case–control study of the impact of the East Anglian breast screening programme on breast cancer mortality

Although breast cancer screening has been shown to work in randomised trials, there is a need to evaluate service screening programmes to ensure that they are delivering the benefit indicated by the trials. We carried out a case–control study to investigate the effect of mammography service screening, in the NHS breast screening programme, on breast cancer mortality in the East Anglian region of the UK. Cases were deaths from breast cancer in women diagnosed between the ages of 50 and 70 years, following the instigation of the East Anglia Breast Screening Programme in 1989. The controls were women (two per case) who had not died of breast cancer, from the same area, matched by date of birth to the cases. Each control was known to be alive at the time of death of her matched case. All women were known to the breast screening programme and were invited, at least once, to be screened. There were 284 cases and 568 controls. The odds ratio (OR) for risk of death from breast cancer in women who attended at least one routine screen compared to those who did not attend was 0.35 (CI: 0.24, 0.50). Adjusting for self-selection bias gave an estimate of the breast cancer mortality reduction associated with invitation to screening of 35% (OR=0.65, 95% CI: 0.48, 0.88). The effect of actually being screened was a 48% breast cancer mortality reduction (OR=0.52, 95% CI: 0.32, 0.84). The results suggest that the National Breast Screening Programme in East Anglia is achieving a reduction in breast cancer deaths, which is at least consistent with the results from the randomised controlled trials of mammographic screening

Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model

One important goal in multi-state modeling is the estimation of transition probabilities. In longitudinal medical studies these quantities are particularly of interest since they allow for long-term predictions of the process. In recent years signi ficant contributions have been made regarding this topic. However, most of the approaches assume independent censoring and do not account for the influence of covariates. The goal of the paper is to introduce feasible estimation methods for the transition probabilities in an illness-death model conditionally on current or past covariate measures. All approaches are evaluated through a simulation study, leading to a comparison of two di erent estimators. The proposed methods are illustrated using real a colon cancer data set.This research was nanced by FEDER Funds through Programa Operacional Factores de Competitividade COMPETE and by Portuguese Funds through FCT - Funda ção para a Cência e a Tecnologia, within Projects Est-C/MAT/UI0013/2011 and PTDC/MAT/104879/2008. We also acknowledge nancial support from the project Grants MTM2008-03129 and MTM2011-23204 (FEDER support included) of the Spanish Ministerio de Ciencia e Innovaci on and 10PXIB300068PR of the Xunta de Galicia. Partial support from a grant from the US National Security Agency (H98230-11-1-0168) is greatly appreciated

A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations

Abstract Background Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods We used age standardised incidence ratios (SIRs) of esophageal cancer (EC) from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1) Poisson regression with agglomeration-specific nonspatial random effects; (2) Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC), Akaike's information criterion (AIC) and adjusted pseudo R2, were used for model comparison. Results A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. Conclusions The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.</p

Alcohol and head and neck cancer risk in a prospective study

We investigated the relation between head and neck cancer risk and alcohol consumption in the NIH-AARP Diet and Health Study. During 2 203 500 person-years of follow-up, 611 men and 183 women developed head and neck cancer. With moderate drinking (up to one alcoholic drink per day) as the referent group, non-drinkers showed an increased risk of head and neck cancer (men: hazard ratio (HR) 1.68, 95% confidence interval (95% CI) 1.37–2.06; women: 1.46, 1.02–2.08). Among male and female alcohol drinkers, we observed a significant dose–response relationship between alcohol consumption and risk. The HR for consuming >3 drinks per day was significantly higher in women (2.52, 1.46–4.35) than in men (1.48, 1.15–1.90; P for interaction=0.0036). The incidence rates per 100 000 person-years for those who consumed >3 drinks per day were similar in men (77.6) and women (75.3). The higher HRs observed in women resulted from lower incidence rates in the referent group: women (14.7), men (34.4). In summary, drinking >3 alcoholic beverages per day was associated with increased risk in men and women, but consumption of up to one drink per day may be associated with reduced risk relative to non-drinking

High-risk mammographic parenchymal patterns and anthropometric measures: a case–control study

Mammographic parenchymal patterns are related to breast cancer risk and are also affected by anthropometric measure. We carried out a case–control study comprising 200 cases with high-risk (P2 and DY) mammographic parenchymal pattern and 200 controls with low-risk (N1 and P1) patterns in order to investigate the effect of body size and shape and breast size on mammographic patterns. Women in the highest quartile of body mass index (BMI) distribution were significantly less likely to have a high-risk pattern (odds ratio (OR) = 0.21, 95% confidence interval (CI) 0.08–0.52, P-value for trend = 0.001) compared to those in the lowest quartile. Relative to women with a waist to hip ratio (WHR) of less than 0.75, the OR of having a high-risk pattern in women with a WHR greater than 0.80 was 0.30 (95% CI 0.14–0.63). Breast size as measured by cup size was significantly and negatively related to high-risk pattern. Our study indicates that both BMI and WHR are negatively associated with high-risk patterns. However, both phenomena are associated with increased risk of breast cancer in post-menopausal women. This negative confounding of two positive risk factors means that the effect of parenchymal patterns on risk will tend to be underestimated when not adjusted for BMI and WHR and vice versa. Thus we may have underestimated the importance of BMI and mammographic parenchymal patterns in the past. Further studies are needed to determine a measure of parenchymal density that is independent of anthropometric measures and breast size. © 1999 Cancer Research Campaig

Risk of Kaposi's sarcoma and of other cancers in Italian renal transplant patients

A follow-up study of 1844 renal transplant patients in Italy showed a 113-fold increased risk for Kaposi's sarcoma. Kaposi's sarcoma risk was higher in persons born in southern than in northern Italy. Significant increases were also observed for cancers of the lip, liver, kidney and for non-Hodgkin's lymphoma