A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

We describe a simple, efficient, permutation based procedure for selecting the penalty parameter in the LASSO. The procedure, which is intended for applications where variable selection is the primary focus, can be applied in a variety of structural settings, including generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of three real data sets in which permutation selection is compared with cross-validation (CV), the Bayesian information criterion (BIC), and a selection method based on recently developed testing procedures for the LASSO

Fine-Mapping Additive and Dominant SNP Effects Using Group-LASSO and Fractional Resample Model Averaging

Genomewide association studies sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple SNPs simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow-up studies. Current multi-SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA-dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights; it estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing a SNP prioritization that best identifies underlying true signals, we show that: our method easily outperforms a single marker analysis; when additive-only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive-only effects; and, when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation

Results from the Joint Nevergrad and IOHprofiler Open Optimization Competition

Volume 14, issue 4SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computatio

Results from the Joint Nevergrad and IOHprofiler Open Optimization Competition

Volume 14, issue 4SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computatio

Results from the Joint Nevergrad and IOHprofiler Open Optimization Competition

Volume 14, issue 4SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computatio

Results from the Joint Nevergrad and IOHprofiler Open Optimization Competition

Volume 14, issue 4SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computatio

Racial and Socioeconomic Disparities in Bladder Cancer Survival: Analysis of the California Cancer Registry

PurposeTo examine the California Cancer Registry (CCR) for bladder cancer survival disparities based on race, socioeconomic status (SES), and insurance in California patients.Patients and methodsThe CCR was queried for bladder cancer cases in California from 1988 to 2012. The primary outcome was disease-specific survival (DSS), defined as the time interval from date of diagnosis to date of death from bladder cancer. Survival analyses were performed to determine the prognostic significance of racial and socioeconomic factors.ResultsA total of 72,452 cases were included (74.5% men, 25.5% women). The median age was 72 years (range, 18-109 years). The racial distribution among the patients was 81% white, 3.8% black, 8.8% Hispanic, 5.2% Asian, and 1.2% from other races. In black patients, tumors presented more frequently with advanced stage and high grade. Medicaid patients tended to be younger and had more advanced-stage, higher-grade tumors compared to patients with Medicare or managed care (P < .0001). Kaplan-Meier analysis demonstrated significantly poorer 5-year DSS in black, low SES, and Medicaid patients (P < .0001). When controlling for stage, grade, age, and gender, multivariate analysis revealed that black race (DSS hazard ratio = 1.295; 95% confidence interval, 1.212-1.384), low SES (DSS hazard ratio = 1.325; 95% confidence interval, 1.259-1.395), and Medicaid insurance (DSS hazard ratio = 1.349; 95% confidence interval, 1.246-1.460) were independent prognostic factors (P < .0001).ConclusionAn analysis of the CCR demonstrated that black race, low SES, and Medicaid insurance portend poorer DSS. These findings reflect a multifaceted socioeconomic and public health conundrum, and efforts to reduce inequalities should be pursued