Skin Cancer Incidence among Atomic Bomb Survivors from 1958 to 1996

The radiation risk of skin cancer by histological types has been evaluated in the atomic bomb survivors. We examined 80,158 of the 120,321 cohort members who had their radiation dose estimated by the latest dosimetry system (DS02). Potential skin tumors diagnosed from 1958 to 1996 were reviewed by a panel of pathologists, and radiation risk of the first primary skin cancer was analyzed by histological types using a Poisson regression model. A significant excess relative risk (ERR) of basal cell carcinoma (BCC) (n = 123) was estimated at 1 Gy (0.74, 95% confidence interval (CI): 0.26, 1.6) for those age 30 at exposure and age 70 at observation based on a linear-threshold model with a threshold dose of 0.63 Gy (95% CI: 0.32, 0.89) and a slope of 2.0 (95% CI: 0.69, 4.3). The estimated risks were 15, 5.7, 1.3 and 0.9 for age at exposure of 0-9, 10-19, 20-39, over 40 years, respectively, and the risk increased 11% with each one-year decrease in age at exposure. The ERR for squamous cell carcinoma (SCC) in situ (n = 64) using a linear model was estimated as 0.71 (95% CI: 0.063, 1.9). However, there were no significant dose responses for malignant melanoma (n = 10), SCC (n = 114), Paget disease (n = 10) or other skin cancers (n = 15). The significant linear radiation risk for BCC with a threshold at 0.63 Gy suggested that the basal cells of the epidermis had a threshold sensitivity to ionizing radiation, especially for young persons at the time of exposure

Risk analysis of software process measurements

Quantitative process management (QPM) and causal analysis and resolution (CAR) are requirements of capability maturity model (CMM) levels 4 and 5, respectively. They indicate the necessity of process improvement based on objective evidence obtained from statistical analysis of metrics. However, it is difficult to achieve these requirements in practice, and only a few companies have done so successfully. Evidence-based risk-management methods have been proposed for the control of software processes, but are not fully appreciated, compared to clinical practice in medicine. Furthermore, there is no convincing answer as to why these methods are difficult to incorporate in software processes, despite the fact that they are well established in some business enterprises and industries. In this article, we challenge this issue, point out a problem peculiar to software processes, and develop a generally applicable method for identifying the risk of failure for a project in its early stages. The proposed method is based on statistical analyses of process measurements collected continuously throughout a project by a risk assessment and tracking system (RATS). Although this method may be directly applicable to only a limited number of process types, the fundamental idea might be useful for a broader range of applications

Risk Ratio and Risk Difference Estimation in Case-cohort Studies

Background: In case-cohort studies with binary outcomes, ordinary logistic regression analyses have been widely used because of their computational simplicity. However, the resultant odds ratio estimates cannot be interpreted as relative risk measures unless the event rate is low. The risk ratio and risk difference are more favorable outcome measures that are directly interpreted as effect measures without the rare disease assumption. Methods: We provide pseudo-Poisson and pseudo-normal linear regression methods for estimating risk ratios and risk differences in analyses of case-cohort studies. These multivariate regression models are fitted by weighting the inverses of sampling probabilities. Also, the precisions of the risk ratio and risk difference estimators can be improved using auxiliary variable information, specifically by adapting the calibrated or estimated weights, which are readily measured on all samples from the whole cohort. Finally, we provide computational code in R (R Foundation for Statistical Computing, Vienna, Austria) that can easily perform these methods. Results: Through numerical analyses of artificially simulated data and the National Wilms Tumor Study data, accurate risk ratio and risk difference estimates were obtained using the pseudo-Poisson and pseudo-normal linear regression methods. Also, using the auxiliary variable information from the whole cohort, precisions of these estimators were markedly improved. Conclusion: The ordinary logistic regression analyses may provide uninterpretable effect measure estimates, and the risk ratio and risk difference estimation methods are effective alternative approaches for case-cohort studies. These methods are especially recommended under situations in which the event rate is not low

Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort

Abstract Background Common variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze common- and rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods. Results The TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk. Conclusions A stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility

Additional file 1: of Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort

Detailed characterization of genes and pathways used in the analysis. Supplementary Material. Detailed characterization of genes and SNPs used in the analysis. (DOCX 51 kb