Subgroup Identification and Variable Selection from Randomized Clinical Trial Data.

We consider the situation of a randomized clinical trial with a moderate number of baseline covariates. While the overall treatment effect may be modest, it is plausible that there is a subgroup of individuals, defined by their baseline covariate values, that have an enhanced treatment effect. The challenge is to identify a subgroup from the randomized trial data. Such subgroups may be used to aid in future treatment decisions, so it is desirable that they be simple, and depend on only a limited number of covariates. We consider two methods which use randomized clinical trial data to identify such subgroups, the first of which is a penalized monotone single-index model. Though not a stand-alone subgroup identification method, this model can be used to perform the variable selection stage of a subgroup identification procedure. In this method, the expected treatment effect is assumed to be an unknown monotone function of a linear combination of covariates. To estimate this unknown function and the linear combination, an adaptive LASSO-penalized monotone single-index model is employed. The second method is a two-stage subgroup identification procedure. In stage 1, nonparametric regression is used to estimate treatment effects for each subject. In the stage 2, a systematic evaluation of many subgroups of a simple, pre-specified form is performed. Using a criterion which is based on the estimated treatment effects obtained in stage 1, the best of these subgroups is identified. To help reduce the risk of false positive findings, we also propose a number of permutation-based methods for obtaining p-values for treatment-by-covariate interactions, which can be used to test whether or not the identified subgroup is truly enhanced. All methods are evaluated in simulations studies, and illustrated using randomized clinical trial data.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102382/1/jaredcf_1.pd

Variable selection in monotone single‐index models via the adaptive LASSO

We consider the problem of variable selection for monotone single‐index models. A single‐index model assumes that the expectation of the outcome is an unknown function of a linear combination of covariates. Assuming monotonicity of the unknown function is often reasonable and allows for more straightforward inference. We present an adaptive LASSO penalized least squares approach to estimating the index parameter and the unknown function in these models for continuous outcome. Monotone function estimates are achieved using the pooled adjacent violators algorithm, followed by kernel regression. In the iterative estimation process, a linear approximation to the unknown function is used, therefore reducing the situation to that of linear regression and allowing for the use of standard LASSO algorithms, such as coordinate descent. Results of a simulation study indicate that the proposed methods perform well under a variety of circumstances and that an assumption of monotonicity, when appropriate, noticeably improves performance. The proposed methods are applied to data from a randomized clinical trial for the treatment of a critical illness in the intensive care unit. Copyright © 2013 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/100172/1/sim5834.pd

Accrual of Older Patients With Breast Cancer to Alliance Systemic Therapy Trials Over Time: Protocol A151527

Despite increasing awareness of accrual challenges, it is unknown if accrual of older patients to breast cancer treatment trials is improving

Updated standardized definitions for efficacy endpoints in adjuvant breast cancer clinical trials: STEEP Version 2.0

Purpose The Standardized Definitions for Efficacy End Points (STEEP) criteria, established in 2007, provide standardized definitions of adjuvant breast cancer clinical trial end points. Given the evolution of breast cancer clinical trials and improvements in outcomes, a panel of experts reviewed the STEEP criteria to determine whether modifications are needed.Methods We conducted systematic searches of ClinicalTrials.gov for adjuvant systemic and local-regional therapy trials for breast cancer to investigate if the primary end points reported met STEEP criteria. On the basis of common STEEP deviations, we performed a series of simulations to evaluate the effect of excluding non-breast cancer deaths and new nonbreast primary cancers from the invasive disease-free survival end point.Results Among 11 phase III breast cancer trials with primary efficacy end points, three had primary end points that followed STEEP criteria, four used STEEP definitions but not the corresponding end point names, and four used end points that were not included in the original STEEP manuscript. Simulation modeling demonstrated that inclusion of second nonbreast primary cancer can increase the probability of incorrect inferences, can decrease power to detect clinically relevant efficacy effects, and may mask differences in recurrence rates, especially when recurrence rates are low.Conclusion We recommend an additional end point, invasive breast cancer-free survival, which includes all invasive disease-free survival events except second nonbreast primary cancers. This end point should be considered for trials in which the toxicities of agents are well-known and where the risk of second primary cancer is small. Additionally, we provide end point recommendations for local therapy trials, low-risk populations, noninferiority trials, and trials incorporating patient-reported outcomes

Standardized Definitions for Efficacy End Points in Neoadjuvant Breast Cancer Clinical Trials: NeoSTEEP.

PURPOSE: The Standardized Definitions for Efficacy End Points (STEEP) criteria, established in 2007 and updated in 2021 (STEEP 2.0), provide standardized definitions of adjuvant breast cancer (BC) end points. STEEP 2.0 identified a need to separately address end points for neoadjuvant clinical trials. The multidisciplinary NeoSTEEP working group of experts was convened to critically evaluate and align neoadjuvant BC trial end points. METHODS: The NeoSTEEP working group concentrated on neoadjuvant systemic therapy end points in clinical trials with efficacy outcomes-both pathologic and time-to-event survival end points-particularly for registrational intent. Special considerations for subtypes and therapeutic approaches, imaging, nodal staging at surgery, bilateral and multifocal diseases, correlative tissue collection, and US Food and Drug Administration regulatory considerations were contemplated. RESULTS: The working group recommends a preferred definition of pathologic complete response (pCR) as the absence of residual invasive cancer in the complete resected breast specimen and all sampled regional lymph nodes (ypT0/Tis ypN0 per AJCC staging). Residual cancer burden should be a secondary end point to facilitate future assessment of its utility. Alternative end points are needed for hormone receptor-positive disease. Time-to-event survival end point definitions should pay particular attention to the measurement starting point. Trials should include end points originating at random assignment (event-free survival and overall survival) to capture presurgery progression and deaths as events. Secondary end points adapted from STEEP 2.0, which are defined from starting at curative-intent surgery, may also be appropriate. Specification and standardization of biopsy protocols, imaging, and pathologic nodal evaluation are also crucial. CONCLUSION: End points in addition to pCR should be selected on the basis of clinical and biologic aspects of the tumor and the therapeutic agent investigated. Consistent prespecified definitions and interventions are paramount for clinically meaningful trial results and cross-trial comparison

A possible dose–response association between distance to farmers’ markets and roadside produce stands, frequency of shopping, fruit and vegetable consumption, and body mass index among customers in the Southern United States

Background: The association between farmers’ market characteristics and consumer shopping habits remains unclear. Our objective was to examine associations among distance to farmers’ markets, amenities within farmers’ markets, frequency of farmers’ market shopping, fruit and vegetable consumption, and body mass index (BMI). We hypothesized that the relationship between frequency of farmers’ market shopping and BMI would be mediated by fruit and vegetable consumption. Methods: In 15 farmers’ markets in northeastern North Carolina, July–September 2015, we conducted a crosssectional survey among 263 farmers’ market customers (199 provided complete address data) and conducted farmers’ market audits. To participate, customers had to be over 18 years of age, and English speaking. Dependent variables included farmers’ market shopping frequency, fruit and vegetable consumption, and BMI. Analysis of variance, adjusted multinomial logistic regression, Poisson regression, and linear regression models, adjusted for age, race, sex, and education, were used to examine associations between distance to farmers’ markets, amenities within farmers’ markets, frequency of farmers’ market shopping, fruit and vegetable consumption, and BMI. Results: Those who reported shopping at farmers’ markets a few times per year or less reported consuming 4.4 (standard deviation = 1.7) daily servings of fruits and vegetables, and those who reported shopping 2 or more times per week reported consuming 5.5 (2.2) daily servings. There was no association between farmers’ market amenities, and shopping frequency or fruit and vegetable consumption. Those who shopped 2 or more times per week had a statistically significantly lower BMI than those who shopped less frequently. There was no evidence of mediation of the relationship between frequency of shopping and BMI by fruit and vegetable consumption. Conclusions: More work should be done to understand factors within farmers’ markets that encourage fruit and vegetable purchases.ECU Open Access Publishing Support Fun

Hunt for new phenomena using large jet multiplicities and missing transverse momentum with ATLAS in 4.7 fb−1 of s√=7TeV proton-proton collisions

Results are presented of a search for new particles decaying to large numbers of jets in association with missing transverse momentum, using 4.7 fb−1 of pp collision data at s√=7TeV collected by the ATLAS experiment at the Large Hadron Collider in 2011. The event selection requires missing transverse momentum, no isolated electrons or muons, and from ≥6 to ≥9 jets. No evidence is found for physics beyond the Standard Model. The results are interpreted in the context of a MSUGRA/CMSSM supersymmetric model, where, for large universal scalar mass m 0, gluino masses smaller than 840 GeV are excluded at the 95% confidence level, extending previously published limits. Within a simplified model containing only a gluino octet and a neutralino, gluino masses smaller than 870 GeV are similarly excluded for neutralino masses below 100 GeV

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN

Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC

Measurements are presented of production properties and couplings of the recently discovered Higgs boson using the decays into boson pairs, H →γ γ, H → Z Z∗ →4l and H →W W∗ →lνlν. The results are based on the complete pp collision data sample recorded by the ATLAS experiment at the CERN Large Hadron Collider at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV, corresponding to an integrated luminosity of about 25 fb−1. Evidence for Higgs boson production through vector-boson fusion is reported. Results of combined fits probing Higgs boson couplings to fermions and bosons, as well as anomalous contributions to loop-induced production and decay modes, are presented. All measurements are consistent with expectations for the Standard Model Higgs boson

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011