Bayesian assessment of times to diagnosis in breast cancer screening

Breast cancer is one of the diseases with the most profound impact on health in developed countries and mammography is the most popular method for detecting breast cancer at a very early stage. This paper focuses on the waiting period from a positive mammogram until a confirmatory diagnosis is carried out in hospital. Generalized linear mixed models are used to perform the statistical analysis, always within the Bayesian reasoning. Markov chain Monte Carlo algorithms are applied for estimation by simulating the posterior distribution of the parameters and hyperparameters of the model through the free software WinBUGS.     

Cancer Dose-Response Modeling of Epidemiological Data on Worker Exposures to Aldrin and Dieldrin

The paper applies classical statistical principles to yield new tools for risk assessment and makes new use of epidemiological data for human risk assessment. An extensive clinical and epidemiological study of workers engaged in the manufacturing and formulation of aldrin and dieldrin provides occupational hygiene and biological monitoring data on individual exposures over the years of employment and provides unusually accurate measures of individual lifetime average daily doses. In the cancer dose-response modeling, each worker is treated as a separate experimental unit with his own unique dose. Maximum likelihood estimates of added cancer risk are calculated for multistage, multistage-Weibull, and proportional hazards models. Distributional characterizations of added cancer risk are based on bootstrap and relative likelihood techniques. The cancer mortality data on these male workers suggest that low-dose exposures to aldrin and dieldrin do not significantly increase human cancer risk and may even decrease the human hazard rate for all types of cancer combined at low doses (e.g., 1 g/kg/day). The apparent hormetic effect in the best fitting dose-response models for this data set is statistically significant. The decrease in cancer risk at low doses of aldrin and dieldrin is in sharp contrast to the U.S. Environmental Protection Agency's upper bound on cancer potency based on mouse liver tumors. The EPA's upper bound implies that lifetime average daily doses of 0.0000625 and 0.00625 g/kg body weight/day would correspond to increased cancer risks of 0.000001 and 0.0001, respectively. However, the best estimate from the Pernis epidemiological data is that there is no increase in cancer risk in these workers at these doses or even at doses as large as 2 g/kg/day.     

Probabilistic Risk Analysis for a High-Level Radioactive Waste Repository

A probabilistic risk analysis (PRA) for a high-level radioactive waste repository is very important since it gives an estimate of its health impacts, allowing comparisons to be made with the health impacts of competing technologies. However, it is extremely difficult to develop a credible PRA for a specific repository site because of large uncertainties in future climate, hydrology, geological processes, etc. At best, such a PRA would not be understandable to the public. An alternative proposed here is to develop a PRA for an average U.S. site, taking all properties of the site to be the U.S. average. The results are equivalent to the average results for numerous randomly selected sites. Such a PRA is presented here; it is easy to understand, and it is not susceptible to substantial uncertainty. Applying the results to a specific repository site then requires only a simple, intuitively acceptable "leap of faith" in assuming that with large expenditures of effort and money, experts can select a site that would be at least as secure as a randomly selected site.     

Significance of Exposure Assessment to Analysis of Cancer Risk from Inorganic Arsenic in Drinking Water in Taiwan

The primary source of evidence that inorganic arsenic in drinking water is associated with increased mortality from cancer at internal sites (bladder, liver, lung, and other organs) is a large ecologic study conducted in regions of Southwest Taiwan endemic to Blackfoot disease. The dose-response patterns for lung, liver, and bladder cancers display a nonlinear dose-response relationship with arsenic exposure. The data do not appear suitable, however, for the more refined task of dose-response assessment, particularly for inference of risk at the low arsenic concentrations found in some U.S. water supplies. The problem lies in variable arsenic concentrations between the wells within a village, largely due to a mix of shallow wells and deep artesian wells, and in having only one well test for 24 (40%) of the 60 villages. The current analysis identifies 14 villages where the exposure appears most questionable, based on criteria described in the text. The exposure values were then changed for seven of the villages, from the median well test being used as a default to some other point in the village's range of well tests that would contribute to smoothing the appearance of a dose-response curve. The remaining seven villages, six of which had only one well test, were deleted as outliers. The resultant dose-response patterns showed no evidence of excess risk below arsenic concentrations of 0.1 mg/l. Of course, that outcome is dependent on manipulation of the data, as described. Inclusion of the seven deleted villages would make estimates of risk much higher at low doses. In those seven villages, the cancer mortality rates are significantly high for their exposure levels, suggesting that their exposure values may be too low or that other etiological factors need to be taken into account.     

Detecting multiple change points in piecewise constant hazard functions

The National Cancer Institute (NCI) suggests a sudden reduction in prostate cancer mortality rates, likely due to highly successful treatments and screening methods for early diagnosis. We are interested in understanding the impact of medical breakthroughs, treatments, or interventions, on the survival experience for a population. For this purpose, estimating the underlying hazard function, with possible time change points, would be of substantial interest, as it will provide a general picture of the survival trend and when this trend is disrupted. Increasing attention has been given to testing the assumption of a constant failure rate against a failure rate that changes at a single point in time. We expand the set of alternatives to allow for the consideration of multiple change-points, and propose a model selection algorithm using sequential testing for the piecewise constant hazard model. These methods are data driven and allow us to estimate not only the number of change points in the hazard function but where those changes occur. Such an analysis allows for better understanding of how changing medical practice affects the survival experience for a patient population. We test for change points in prostate cancer mortality rates using the NCI Surveillance, Epidemiology, and End Results dataset.     

Potential surrogate endpoints in cancer research – some considerations and examples

We present an introductory survey of the use of surrogates in cancer research, in particular in clinical trials. The concept of a surrogate endpoint is introduced and contrasted with that of a biomarker. It is emphasized that a surrogate endpoint is not universal for an indication but will depend on the mechanism of treatment. We discuss the measures of validity of a surrogate and give examples of both cancer surrogates and biomarkers on the path to surrogacy. Circumstances in which a surrogate endpoint may actually be preferred to the clinical endpoint are described. We provide pointers to the recent substantive literature on surrogates. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Prostate-specific antigen decline pattern in advanced prostate cancer receiving androgen deprivation therapy and relationship with prostate-specific antigen progression

Introduction: The aim of this study is to evaluate prostate-specific antigen decline pattern including prostate-specific antigen kinetics following androgen deprivation therapy on prostate-specific antigen progression in the patients with advanced prostate cancer.

Materials and methods: Ninety-seven advanced prostate cancer patients receiving maximum androgen deprivation therapy were enrolled in case–control study. Baseline prostate-specific antigen, Gleason Score, bone metastase, nadir prostate-specific antigen, time to nadir prostate-specific antigen, declining slope to nadir prostate-specific antigen, estimated baseline prostate-specific antigen half-time, current prostate-specific antigen, post-nadir prostate-specific antigen time, estimated prostate-specific antigen, estimated decline of baseline prostate-specific antigen as quantitative, and ratio were recorded and calculated.

Results: The ratio of prostate-specific antigen progression was significantly lower at the patients who had slower declining slope to prostate-specific antigen, longer time to nadir prostate-specific antigen, and lower estimated decline ratio of baseline prostate-specific antigen (p: .016, p: .020, and p: .026, respectively).

Conclusions: The shorter time to nadir prostate-specific antigen following androgen deprivation therapy, faster declining slope to nadir prostate-specific antigen and higher estimated decline ratio of baseline prostate-specific antigen are associated with higher risk of disease progression in patients with hormone-sensitive prostate cancer.     

Validation of prognostic indices using the frailty model

A major issue when proposing a new prognostic index is its generalisibility to daily clinical practice. Validation is therefore required. Most validation techniques assess whether “on average” the results obtained by the prognostic index in classifying patients in a new sample of patients are similar to the results obtained in the construction set. We introduce a new important aspect of the generalisibility of a prognostic index: the heterogeneity of the prognostic index risk group hazard ratios over different centers. If substantial variability between centers exists, the prognostic index may have no discriminatory capability in some of the centers. To model such heterogeneity, we use a frailty model including a random center effect and a random prognostic index by center interaction. Statistical inference is based on a Bayesian approach using a Laplacian approximation for the marginal posterior distribution of the variances of the random effects. We investigate different ways to summarize the information available from this marginal posterior distribution. Our approach is applied to a real bladder cancer database for which we demonstrate how to investigate and interpret heterogeneity in prognostic index effect over centers.