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

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

Metabolomics reveals a link between homocysteine and lipid metabolism and leukocyte telomere length: the ENGAGE consortium

Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10−6), methionine (p-value = 9.2 × 10−5), tyrosine (p-value = 2.1 × 10−4), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10−4), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10−4), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10−4). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality

Skewed X-inactivation is common in the general female population

X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants

A note on the catch-up time method for estimating lead or sojourn time in prostate cancer screening

Models of cancer screening assume that cancers are detectable by screening before being diagnosed clinically through symptoms. The duration of this preclinical phase is called sojourn time, and it determines how much diagnosis might be advanced in time by the screening test (lead time). In the catch-up time method, mean sojourn time or lead time are estimated as the time needed for cumulative incidence in an unscreened population to catch up with the detection rate (prevalence) at a first screening test. The method has been proposed as a substitute of the prevalence/incidence ratio in the case of prostate cancer where incidence cannot be treated as a constant. A model is proposed to justify this estimator. It is shown that this model is different from classic Markov-type models developed for breast cancer screening. In both models, the catch-up time method results in biased estimates of mean sojourn time