A bootstrap-based method to achieve optimality on estimating the extreme-value index

Estimators of the extreme-value index are based on a set of upper order statistics. We present an adaptive method to choose the number of order statistics involved in an optimal way, balancing variance and bias components. Recently this has been achieved for the similar but somewhat less involved case of regularly varying tails (Drees and Kaufmann(1997); Danielsson et al.(1996)). The present paper follows the line of proof of the last mentioned paper

Prostate-Specific Antigen Screening in the United States vs in the European Randomized Study of Screening for Prostate Cancer–Rotterdam

Dissemination of prostate-specific antigen (PSA) testing in the United States coincided with an increasing incidence of prostate cancer, a shift to earlier stage disease at diagnosis, and decreasing prostate cancer mortality. We compared PSA screening performance with respect to prostate cancer detection in the US population vs in the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer (ERSPC–Rotterdam). We developed a simulation model for prostate cancer and PSA screening for ERSPC–Rotterdam. This model was then adapted to the US population by replacing demography parameters with US-specific ones and the screening protocol with the frequency of PSA tests in the US population. We assumed that the natural progression of prostate cancer and the sensitivity of a PSA test followed by a biopsy were the same in the United States as in ERSPC–Rotterdam. The predicted prostate cancer incidence peak in the United States was then substantially higher than the observed prostate cancer incidence peak (13.3 vs 8.1 cases per 1000 man-years). However, the actual observed incidence was reproduced by assuming a substantially lower PSA test sensitivity in the United States than in ERSPC–Rotterdam. For example, for nonpalpable local- or regional-stage cancers (ie, stage T1M0), the estimates of PSA test sensitivity were 0.26 in the United States vs 0.94 in ERSPC–Rotterdam. We conclude that the efficacy of PSA screening in detecting prostate cancer was lower in the United States than in ERSPC–Rotterdam

Population-based mammography screening below age 50: balancing radiation-induced vs prevented breast cancer deaths

INTRODUCTION: Exposure to ionizing radiation at mammography screening may cause breast cancer. Because the radiation risk increases with lower exposure age, advancing the lower age limit may affect the balance between screening benefits and risks. The present study explores the benefit-risk ratio of screening before age 50. METHODS: The benefits of biennial mammography screening, starting at various ages between 40 and 50, and continuing up to age 74 were examined using micro-simulation. In contrast with previous studies that commonly used excess relative risk models, we assessed the radiation risks using the latest BEIR-VII excess absolute rate exposure-risk model. RESULTS: The estimated radiation risk is lower than previously assessed. At a mean glandular dose of 1.3 mGy per view that was recently measured in the Netherlands, biennial mammography screening between age 50 and 74 was predicted to induce 1.6 breast cancer deaths per 100 000 women aged 0-100 (range 1.3-6.3 extra deaths at a glandular dose of 1-5 mGy per view), against 1121 avoided deaths in this population. Advancing the lower age limit for screening to include women aged 40-74 was predicted to induce 3.7 breast cancer deaths per 100 000 women aged 0-100 (range 2.9-14.4) at biennial screening, but would also prevent 1302 deaths. CONCLUSION: The benefits of mammography screening between age 40 and 74 were predicted to outweigh the radiation risks. British Journal of Cancer (2011) 104, 1214-1220. doi: 10.1038/bjc.2011.67 www.bjcancer.com Published online 1 March 2011 (c) 2011 Cancer Research U

To be screened or not to be screened Modeling the consequences of PSA screening for the individual

Background:Screening with prostate-specific antigen (PSA) can reduce prostate cancer mortality, but may advance diagnosis and treatment in time and lead to overdetection and overtreatment. We estimated benefits and adverse effects of PSA screening for individuals who are deciding whether or not to be screened.Methods:Using a microsimulation model, we estimated lifetime probabilities of prostate cancer diagnosis and death, overall life expectancy and expected time to diagnosis, both with and without screening. We calculated anticipated loss in quality of life due to prostate cancer diagnosis and treatment that would be acceptable to decide in favour of screening.Results:Men who were screened had a gain in life expectancy of 0.08 years but their expected time to diagnosis decreased by 1.53 life-years. Of the screened men, 0.99% gained on average 8.08 life-years and for 17.43% expected time to diagnosis decreased by 8.78 life-years. These figures imply that the anticipated loss in quality of life owing to diagnosis and treatment should not exceed 4.8%, for screening to have a positive effect on quality-adjusted life expectancy.Conclusion:The decision to be screened should depend on personal preferences. The negative impact of screening might be reduced by screening men who are more willing to accept the side effects from treatment

Lead times and overdetection due to prostate-specific antigen screening: estimates from the European Randomized Study of Screening for Prostate Cancer

BACKGROUND: Screening for prostate cancer advances the time of diagnosis (lead time) and detects cancers that would not have been diagnosed in the absence of screening (overdetection). Both consequences have considerable impact on the net benefits of screening. METHODS: We developed simulation models based on results of the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer (ERSPC), which enrolled 42,376 men and in which 1498 cases of prostate cancer were identified, and on baseline prostate cancer incidence and stage distribution data. The models were used to predict mean lead times, overdetection rates, and ranges (corresponding to approximate 95% confidence intervals) associated with different screening programs. RESULTS: Mean lead times and rates of overdetection depended on a man's age at screening. For a single screening test at age 55, the estimated mean lead time was 12.3 years (range = 11.6-14.1 years) and the overdetection rate was 27% (range = 24%-37%); at age 75, the estimates were 6.0 years (range = 5.8-6.3 years) and 56% (range = 53%-61%), respectively. For a screening program with a 4-year screening interval from age 55 to 67, the estimated mean lead time was 11.2 years (range = 10.8-12.1 years), and the overdetection rate was 48% (range = 44%-55%). This screening program raised the lifetime risk of a prostate cancer diagnosis from 6.4% to 10.6%, a relative increase of 65% (range = 56%-87%). In annual screening from age 55 to 67, the estimated overdetection rate was 50% (range = 46%-57%) and the lifetime prostate cancer risk was increased by 80% (range = 69%-116%). Extending annual or quadrennial screening to the age of 75 would result in at least two cases of overdetection for every clinically relevant cancer detected. CONCLUSIONS: These model-based lead-time estimates support a prostate cancer screening interval of more than 1 year

Breast cancer screening: evidence for false reassurance?

Tumour stage distribution at repeated mammography screening is, unexpectedly, often not more favourable than stage distribution at first screenings. False reassurance, i.e., delayed symptom presentation due to having participated in earlier screening rounds, might be associated with this, and unfavourably affect prognosis. To assess the role of false reassurance in mammography screening, a consecutive group of 155 breast cancer patients visiting a breast clinic in Rotterdam (The Netherlands) completed a questionnaire on screening history and self-observed breast abnormalities. The length of time between the initial discovery of breast abnormalities and first consultation of a general practitioner ("symptom-GP period") was compared between patients with ("screening group") and without a previous screening history ("control group"), using Kaplan-Meier survival curves and log-rank testing. Of the 155 patients, 84 (54%) had participated in the Dutch screening programme at least once before tumour detection; 32 (38%) of whom had noticed symptoms. They did not significantly differ from control patients (n = 42) in symptom-GP period (symptom-GP period > or = 30 days: 31.2% in the symptomatic screened group, 31.0% in the control group; p = 0.9). Only 2 out of 53 patients (3.8%) with screen-detected cancer had noticed symptoms prior to screening, reporting symptom-GP periods of 2.5 and 4 years. The median period between the first GP- and breast clinic visit was 7.0 days (95% C.I. 5.9-

Overdiagnosis and overtreatment of breast cancer: Microsimulation modelling estimates based on observed screen and clinical data

There is a delicate balance between the favourable and unfavourable side-effects of screening in general. Overdiagnosis, the detection of breast cancers by screening that would otherwise never have been clinically diagnosed but are now consequently treated, is such an unfavourable side effect. To correctly model the natural history of breast cancer, one has to estimate mean durations of the different pre-clinical phases, transition probabilities to clinical cancer stages, and sensitivity of the applied test based on observed screen and clinical data. The Dutch data clearly show an increase in screen-detected cases in the 50 to 74 year old age group since the introduction of screening, and a decline in incidence around age 80 years. We had estimated that 3% of total incidence would otherwise not have been diagnosed clinically. This magnitude is no reason not to offer screening for women aged 50 to 74 years. The increases in ductal carcinoma in situ (DCIS) are primarily due to mammography screening, but DCIS still remains a relatively small proportion of the total breast cancer problem

Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2 total), and the proportion of heritability captured by known metabolite loci (h2 Metabolite-hits) for 309 lipids and