G. vaginalis, P. bivia, A. vaginae, and Megasphaera type I may play significant roles in iBV.

This paper considers the task of building efficient regression models for sparse multivariate analysis of high dimensional data sets, in particular it focuses on cases where the numbers q of responses Y = (y k , 1 ≤ k ≤ q) and p of predictors X = (x j , 1 ≤ j ≤ p) to analyse jointly are both large with respect to the sample size n, a challenging bi-directional task. The analysis of such data sets arise commonly in genetical genomics, with X linked to the DNA characteristics and Y corresponding to measurements of fundamental biological processes such as transcription, protein or metabolite production. Building on the Bayesian variable selection setup for the linear model and associated efficient MCMC algorithms developed for single responses, we discuss the generic framework of hierarchical related sparse regressions, where parallel regressions of y k on the set of covariates X are linked in a hierarchical fashion, in particular through the prior model of the variable selection indicators γ kj , which indicate among the covariates x j those which are associated to the response y k in each multivariate regression. Structures for the joint model of the γ kj , which correspond to different compromises between the aims of controlling sparsity and that of enhancing the detection of predictors that are associated with many responses ('hot spots'), will be discussed and a new multiplicative model for the probability structure of the γ kj will be presented. To perform inference for these models in high dimensional setups , novel adaptive MCMC algorithms are needed. As sparsity is paramount and most of the associations expected to be zero, new algorithms that progressively focus on part of the space where the most interesting associations occur are of great interest. We shall discuss their formulation and theoretical properties, and demonstrate their use on simulated and real data from genomics.

BackgroundRisk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients.MethodsWe propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest.ResultsWe compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method.ConclusionsThe proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0336-x) contains supplementary material, which is available to authorized users.

IMPORTANCE The population of cancer survivors is rapidly growing in the US. Tobacco smoking is associated with many cancers; however, whether cigarette smoking behaviors among cancer survivors vary according to cancer type-that is, smoking-related cancers (SRCs) vs non-smokingrelated cancers (NSRCs)-remains unclear. OBJECTIVES To examine cigarette smoking prevalence and behaviors (ie, continuing or quitting smoking) among cancer survivors and to compare them between survivors of SRCs and NSRCs. DESIGN, SETTING, AND PARTICIPANTS This study was a cross-sectional analysis of the 2017 National Health Interview Survey, a household survey of civilian US residents who were aged 18 years or older. The National Health Interview Survey is population based and is representative of the US population. Data analysis was performed from June to October 2019. MAIN OUTCOMES AND MEASURES The primary outcomes were prevalence of current cigarette smoking among cancer survivors and prevalence of continuing smoking and quitting smoking after a cancer diagnosis. Secondary outcomes included factors associated with continued smoking vs quitting smoking after a cancer diagnosis. RESULTS A total of 26 742 respondents (mean [SD] age, 50.97 [18.61] years; 14 646 women [51.76%]) to the 2017 National Health Interview Survey were included in this study. Of the 3068 individuals (9.42%) in the study population who had cancer, 589 (19.96%) were SRC survivors, 2297 (74.50%) were NSRC survivors, 168 (4.96%) were survivors of both SRC and NSRC, and the remaining 14 (0.58%) had missing information about the type of cancer. Four hundred forty-nine SRC survivors (54.08%) were women, compared with 1412 NSRC survivors (54.30%). Ninety-six SRC survivors (15.69%) and 151 NSRC survivors (7.99%) were younger than 45 years. Overall, 372 cancer survivors (13.16%) were current smokers. Current smoking prevalence was higher among survivors of SRCs (145 survivors [19.78%]) compared with NSRC survivors (251 survivors [10.63%]). Among cancer survivors, 309 current smokers at cancer diagnosis (43.96%) reported having successfully quit smoking and 372 (56.04%) reported continuing smoking. Among the continuing smokers, 176 (56.49%) reported an unsuccessful quit attempt in the last 12 months. After cancer diagnosis, SRC survivors had higher odds of continued smoking compared with NSRC survivors (odds ratio [OR],