An ecological investigation of the incidence of cancer in Welsh children for the period 1985–1994 in relation to residence near the coastline

An alarming report from an environmental pressure group raised concerns about childhood leukaemia and the Irish Sea. In response, this ecological study explores the hypotheses that childhood cancer rates are increased by living near the coast of Wales, especially in the north, and in particular near estuaries and mud-flats. Using Poisson regression to adjust for possible confounding variables, no evidence was found for a coastline proximity effect at the level of census wards (5 km). Moreover the rates were significantly lower near estuaries than for the rest of the coast, but there was a small but non-significant increase near mud-flats. Case–control modelling of postcoded cases living within the coastal wards using Stone's method also failed to detect any monotonic reduction in relative risk near the coastline.     

A marginal regression model for multivariate failure time data with a surviving fraction

A marginal regression approach for correlated censored survival data has become a widely used statistical method. Examples of this approach in survival analysis include from the early work by Wei et al. (J Am Stat Assoc 84:1065–1073, 1989) to more recent work by Spiekerman and Lin (J Am Stat Assoc 93:1164–1175, 1998). This approach is particularly useful if a covariate’s population average effect is of primary interest and the correlation structure is not of interest or cannot be appropriately specified due to lack of sufficient information. In this paper, we consider a semiparametric marginal proportional hazard mixture cure model for clustered survival data with a surviving or “cure” fraction. Unlike the clustered data in previous work, the latent binary cure statuses of patients in one cluster tend to be correlated in addition to the possible correlated failure times among the patients in the cluster who are not cured. The complexity of specifying appropriate correlation structures for the data becomes even worse if the potential correlation between cure statuses and the failure times in the cluster has to be considered, and thus a marginal regression approach is particularly attractive. We formulate a semiparametric marginal proportional hazards mixture cure model. Estimates are obtained using an EM algorithm and expressions for the variance–covariance are derived using sandwich estimators. Simulation studies are conducted to assess finite sample properties of the proposed model. The marginal model is applied to a multi-institutional study of local recurrences of tonsil cancer patients who received radiation therapy. It reveals new findings that are not available from previous analyses of this study that ignored the potential correlation between patients within the same institution.     

An age–period–cohort analysis of cancer incidence among the oldest old,Utah 1973–2002

We used age–period–cohort (APC) analyses to describe the simultaneous effects of age, period, and cohort on cancer incidence rates in an attempt to understand the population dynamics underlying their patterns among those aged 85+. Data from the Utah Cancer Registry (UCR), the US Census, the National Center for Health Statistics (NCHS), and the National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) programme were used to generate age-specific estimates of cancer incidence at ages 65–99 from 1973 to 2002 for Utah. Our results showed increasing cancer incidence rates up to the 85–89 age group followed by declines at ages 90–99 when not confounded by the separate influences of period and cohort effects. We found significant period and cohort effects, suggesting the role of environmental mechanisms in cancer incidence trends between the ages of 85 and 100.     

Preoperative sexual status of Japanese localized prostate cancer patients: comparison of sexual activity and EPIC scores

Objective: This study aimed to evaluate the relationship between sexual activity and sexual function using questionnaires distributed to middle-aged Japanese patients with localized prostate cancer.

Methods: A total of 145 patients who underwent radical prostatectomy were enrolled in the survey reported on herein. Sexual activity and sexual function were investigated via the Expanded Prostate Cancer Index Composite (EPIC) and an original self-reported questionnaire.

Results: Of participants, 24.1% and 20.7% had sexual activity within a month period as investigated via the EPIC and original questionnaire, respectively. However, 29.7% of all those who reported sexual activity rate reported “about once every 2?months to a year,” as shown in the original questionnaire. Regarding sexual function as addressed through EPIC, no results within that questionnaire’s measure of sexual function showed significant differences among patients with a rate of actual sexual activity.

Conclusions: The present survey showed that more than quarter of preoperative middle-aged Japanese prostate cancer patients surveyed had actual sexual activity, though not within the preceding 4?weeks. To precisely evaluate sexual function of middle-aged Japanese patients, it is necessary to consider actual sexual activity.     

Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization

In cancer diagnosis studies, high‐throughput gene profiling has been extensively conducted, searching for genes whose expressions may serve as markers. Data generated from such studies have the ‘large d, small n’ feature, with the number of genes profiled much larger than the sample size. Penalization has been extensively adopted for simultaneous estimation and marker selection. Because of small sample sizes, markers identified from the analysis of single data sets can be unsatisfactory. A cost‐effective remedy is to conduct integrative analysis of multiple heterogeneous data sets. In this article, we investigate composite penalization methods for estimation and marker selection in integrative analysis. The proposed methods use the minimax concave penalty (MCP) as the outer penalty. Under the homogeneity model, the ridge penalty is adopted as the inner penalty. Under the heterogeneity model, the Lasso penalty and MCP are adopted as the inner penalty. Effective computational algorithms based on coordinate descent are developed. Numerical studies, including simulation and analysis of practical cancer data sets, show satisfactory performance of the proposed methods.     

Practical statistical considerations for investigating anti-tumor treatments in mice

Anti-tumor treatment outcomes in mouse experiments can be challenging to interpret and communicate accurately. In reporting these experiments, rigorous statistical considerations are commonly absent, although statistical applications have been proposed. We investigated the practicality and utility of different statistical strategies for the analysis of anti-tumor responses in a longitudinal mouse case study. Each analysis that we performed had different endpoints, investigated different questions, and was based on different assumptions. We found rudimentary visual and risk analysis insufficient without additional considerations, and upon further investigation we found improvements in key anti-tumor parameter estimates associated with a drug combination in our case study. We offer practical statistical considerations for investigating anti-cancer treatments in mice, applying a multi-tier statistical approach.     

Prevalence analysis of urinary incontinence after radical prostatectomy and influential preoperative factors in a single institution

Aims: To assess prevalence of urinary incontinence (UI) after radical prostatectomy (RP) and to analyze which preoperative characteristics of the patients have influence on UI.

Methods: Between 2002 and 2012, 746 consecutive patients underwent RP for clinically localized prostate cancer. We defined UI according to International Continence Society (ICS) definition: “the complaint of any involuntary leakage of urine” after 12?months of recovery, international consultation on incontinence questionnaire (ICIQ-SF) and pads/day was collected too. Clinical features and magnetic resonance imaging measurements were assessed. A multivariable logistic regression model predicting incontinence were built-in after adjust by cofounding factors and bootstrapping.

Results: About 172 (23%) of the patients were classified as incontinent according to the ICS definition. The mean value of the ICIQ-SF was 10.87 (±4). 17.8% of patients use at least one pad/day, 11.9% use more than one pad/day. The preoperative factors independently influential in UI are: age [OR: 1.055; CI 95% (1.006–1.107), p?=?.028], urethral wall thickness [OR: 5.03; CI 95% (1.11–22.8), p?=?.036], history of transurethral resection of the prostate [OR: 6.13; CI 95% (1.86–20.18), p?=?.003] and membranous urethral length [OR: 0.173; CI 95% (0.046–0.64), p?=?.009]. The predictive accuracy of the model is 78.7% and the area under the curve (AUC) value 71.7%.

Conclusions: Urinary incontinence after radical prostatectomy has different prevalence depending on the definition. Age, prior transurethral resection of the prostate (TURP), membranous urethral length (MUL) and urethral wall thickness (UWT) were risk factors.     

Review of Radon and Lung Cancer Risk

Radon, a long-established cause of lung cancer in uranium and other underground miners, has recently emerged as a potentially important cause of lung cancer in the general population. The evidence for widespread exposure of the population to radon and the well-documented excess of lung cancer among underground miners exposed to radon decay products have raised concern that exposure to radon progeny might also be a cause of lung cancer in the general population. To date, epidemiological data on the lung cancer risk associated with environmental exposure to radon have been limited. Consequently, the lung cancer hazard posed by radon exposure in indoor air has been addressed primarily through risk estimation procedures. The quantitative risks of lung cancer have been estimated using exposure-response relations derived from the epidemiological investigations of uranium and other underground miners. We review five of the more informative studies of miners and recent risk projection models for excess lung cancer associated with radon. The principal models differ substantially in their underlying assumptions and consequently in the resulting risk projections. The resulting diversity illustrates the substantial uncertainty that remains concerning the most appropriate model of the temporal pattern of radon-related lung cancer. Animal experiments, further follow-up of the miner cohorts, and well-designed epidemiological studies of indoor exposure should reduce this uncertainty.     

A Risk Assessment for Occupational Acrylonitrile Exposure Using Epidemiology Data

The extensive data from the Blair et al.((1)) epidemiology study of occupational acrylonitrile exposure among 25460 workers in eight plants in the United States provide an excellent opportunity to update quantitative risk assessments for this widely used commodity chemical. We employ the semiparametric Cox relative risk (RR) regression model with a cumulative exposure metric to model cause-specific mortality from lung cancer and all other causes. The separately estimated cause-specific cumulative hazards are then combined to provide an overall estimate of age-specific mortality risk. Age-specific estimates of the additional risk of lung cancer mortality associated with several plausible occupational exposure scenarios are obtained. For age 70, these estimates are all markedly lower than those generated with the cancer potency estimate provided in the USEPA acrylonitrile risk assessment.((2)) This result is consistent with the failure of recent occupational studies to confirm elevated lung cancer mortality among acrylonitrile-exposed workers as was originally reported by O'Berg,((3)) and it calls attention to the importance of using high-quality epidemiology data in the risk assessment process.     

A statistical framework for expression-based molecular classification in cancer

Summary. Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.