Wave climatology in the Apostle Islands, Lake Superior

The wave climate of the Apostle Islands in Lake Superior for 35 year (1979–2013) was hindcast and examined using a third‐generation spectral wave model. Wave measurements within the Apostle Islands and offshore NOAA buoys were used to validate the model. Statistics of the significant wave height, peak wave period, and mean wave direction were computed to reveal the spatial variability of wave properties within the archipelago for average and extreme events. Extreme value analysis was performed to estimate the significant wave height at the 1, 10, and 100 year return periods. Significant wave heights in the interior areas of the islands vary spatially but are approximately half those immediately offshore of the islands. Due to reduced winter ice cover and a clockwise shift in wind direction over the hindcast period, long‐term trend analysis indicates an increasing trend of significant wave heights statistics by as much as 2% per year, which is approximately an order of magnitude greater than similar analysis performed in the global ocean for areas unaffected by ice. Two scientific questions related to wave climate are addressed. First, the wave climate change due to the relative role of changing wind fields or ice covers over the past 35 years was revealed. Second, potential bluff erosion affected by the change of wave climate and the trend of lower water levels in the Apostle Islands, Lake Superior was examined.Key Points:Wave climate of the Apostle Islands in Lake Superior for 35 year was hindcastStatistics of the wave climate reveal the spatial variability of wave propertiesAn increasing trend of SWH is found due to climate changePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113131/1/jgrc21305.pd

Evaluation of Progress Towards the UNAIDS 90-90-90 HIV Care Cascade: A Description of Statistical Methods Used in an Interim Analysis of the Intervention Communities in the SEARCH Study

WHO guidelines call for universal antiretroviral treatment, and UNAIDS has set a global target to virally suppress most HIV-positive individuals. Accurate estimates of population-level coverage at each step of the HIV care cascade (testing, treatment, and viral suppression) are needed to assess the effectiveness of test and treat strategies implemented to achieve this goal. The data available to inform such estimates, however, are susceptible to informative missingness: the number of HIV-positive individuals in a population is unknown; individuals tested for HIV may not be representative of those whom a testing intervention fails to reach, and HIV-positive individuals with a viral load measured may not be representative of those for whom no viral load is obtained. We provide an in-depth description of the statistical methods (target parameters, assumptions, statistical estimands, and algorithms) used in an interim analysis of the intervention arm of the SEARCH Study (NCT01864603) to analyze progress towards the UNAIDS 90-90-90 target at study baseline and after one and two years. We describe the methods used to account for informative measurement in all analyses as well as for informative censoring in longitudinal analyses. We use targeted maximum likelihood estimation (TMLE) with Super Learning to generate semi-parametric efficient and double robust estimates of the care cascade among a open cohort of prevalent HIV-positive adults and among a closed cohort of baseline HIV-positive adults. TMLE is also used to evaluate predictors of poor outcomes

Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models

This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpar

Efficient and Robust Approaches for Analysis of SMARTs: Illustration using the ADAPT-R Trial

Personalized intervention strategies, in particular those that modify treatment based on a participant's own response, are a core component of precision medicine approaches. Sequential Multiple Assignment Randomized Trials (SMARTs) are growing in popularity and are specifically designed to facilitate the evaluation of sequential adaptive strategies, in particular those embedded within the SMART. Advances in efficient estimation approaches that are able to incorporate machine learning while retaining valid inference can allow for more precise estimates of the effectiveness of these embedded regimes. However, to the best of our knowledge, such approaches have not yet been applied as the primary analysis in SMART trials. In this paper, we present a robust and efficient approach using Targeted Maximum Likelihood Estimation (TMLE) for estimating and contrasting expected outcomes under the dynamic regimes embedded in a SMART, together with generating simultaneous confidence intervals for the resulting estimates. We contrast this method with two alternatives (G-computation and Inverse Probability Weighting estimators). The precision gains and robust inference achievable through the use of TMLE to evaluate the effects of embedded regimes are illustrated using both outcome-blind simulations and a real data analysis from the Adaptive Strategies for Preventing and Treating Lapses of Retention in HIV Care (ADAPT-R) trial (NCT02338739), a SMART with a primary aim of identifying strategies to improve retention in HIV care among people living with HIV in sub-Saharan Africa

Fluctuation Domains in Adaptive Evolution

We derive an expression for the variation between parallel trajectories in phenotypic evolution, extending the well known result that predicts the mean evolutionary path in adaptive dynamics or quantitative genetics. We show how this expression gives rise to the notion of fluctuation domains - parts of the fitness landscape where the rate of evolution is very predictable (due to fluctuation dissipation) and parts where it is highly variable (due to fluctuation enhancement). These fluctuation domains are determined by the curvature of the fitness landscape. Regions of the fitness landscape with positive curvature, such as adaptive valleys or branching points, experience enhancement. Regions with negative curvature, such as adaptive peaks, experience dissipation. We explore these dynamics in the ecological scenarios of implicit and explicit competition for a limiting resource

Current and Emerging Uses of Statins in Clinical Therapeutics: A Review

Statins, a class of cholesterol-lowering medications that inhibit 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase, are commonly administered to treat atherosclerotic cardiovascular disease. Statin use may expand considerably given its potential for treating an array of cholesterol-independent diseases. However, the lack of conclusive evidence supporting these emerging therapeutic uses of statins brings to the fore a number of unanswered questions including uncertainties regarding patient-to-patient variability in response to statins, the most appropriate statin to be used for the desired effect, and the efficacy of statins in treating cholesterol-independent diseases. In this review, the adverse effects, costs, and drug–drug and drug–food interactions associated with statin use are presented. Furthermore, we discuss the pleiotropic effects associated with statins with regard to the onset and progression of autoimmune and inflammatory diseases, cancer, neurodegenerative disorders, strokes, bacterial infections, and human immunodeficiency virus. Understanding these issues will improve the prognosis of patients who are administered statins and potentially expand our ability to treat a wide variety of diseases

Outcomes associated with social distancing policies in St Louis, Missouri, during the early phase of the COVID-19 pandemic

Importance: In the absence of a national strategy in response to the COVID-19 pandemic, many public health decisions fell to local elected officials and agencies. Outcomes of such policies depend on a complex combination of local epidemic conditions and demographic features as well as the intensity and timing of such policies and are therefore unclear. Objective: To use a decision analytical model of the COVID-19 epidemic to investigate potential outcomes if actual policies enacted in March 2020 (during the first wave of the epidemic) in the St Louis region of Missouri had been delayed. Design, Setting, and Participants: A previously developed, publicly available, open-source modeling platform (Local Epidemic Modeling for Management & Action, version 2.1) designed to enable localized COVID-19 epidemic projections was used. The compartmental epidemic model is programmed in R and Stan, uses bayesian inference, and accepts user-supplied demographic, epidemiologic, and policy inputs. Hospital census data for 1.3 million people from St Louis City and County from March 14, 2020, through July 15, 2020, were used to calibrate the model. Exposures: Hypothetical delays in actual social distancing policies (which began on March 13, 2020) by 1, 2, or 4 weeks. Sensitivity analyses were conducted that explored plausible spontaneous behavior change in the absence of social distancing policies. Main Outcomes and Measures: Hospitalizations and deaths. Results: A model of 1.3 million residents of the greater St Louis, Missouri, area found an initial reproductive number (indicating transmissibility of an infectious agent) of 3.9 (95% credible interval [CrI], 3.1-4.5) in the St Louis region before March 15, 2020, which fell to 0.93 (95% CrI, 0.88-0.98) after social distancing policies were implemented between March 15 and March 21, 2020. By June 15, a 1-week delay in policies would have increased cumulative hospitalizations from an observed actual number of 2246 hospitalizations to 8005 hospitalizations (75% CrI: 3973-15 236 hospitalizations) and increased deaths from an observed actual number of 482 deaths to a projected 1304 deaths (75% CrI, 656-2428 deaths). By June 15, a 2-week delay would have yielded 3292 deaths (75% CrI, 2104-4905 deaths)-an additional 2810 deaths or a 583% increase beyond what was actually observed. Sensitivity analyses incorporating a range of spontaneous behavior changes did not avert severe epidemic projections. Conclusions and Relevance: The results of this decision analytical model study suggest that, in the St Louis region, timely social distancing policies were associated with improved population health outcomes, and small delays may likely have led to a COVID-19 epidemic similar to the most heavily affected areas in the US. These findings indicate that an open-source modeling platform designed to accept user-supplied local and regional data may provide projections tailored to, and more relevant for, local settings