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Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.
Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.
Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.
In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18–1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05–1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06–1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11–1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02–1.09), showing a genetic risk gradient across the conditions and the comorbidity.
This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.
Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.
We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.
Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.
An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.
Adoption of cover crops in arid agroecosystems has been slow due to concerns regarding limited water resources and possible soil moisture depletion. In irrigated organic systems, potential ecosystem services from cover crops also must be considered in light of the concerns for water conservation. A constructive balance could be achieved with fall-sown small grain cover crops; however, their impacts on irrigated organic systems are poorly understood. Our first objective was to determine the ability of fall-sown small grains [cereal rye (Secale cereale L), winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.) and oat (Avena sativa L.)] to suppress winter weeds in an irrigated, organic transition field in the southwestern USA. Small grains were planted following the legume sesbania (Sesbania exaltata (Raf.) Rydb. ex A.W. Hill) during Fall 2012 and Fall 2013. In Spring 2013 and 2014, weed densities and biomass were determined within each cover crop treatment and compared against unplanted controls. Results indicated that both barley and oat were effective in suppressing winter weeds. Our second objective was to compare weed suppression and soil moisture levels among seven barley varieties developed in the western United States. Barley varieties (‘Arivat’, ‘Hayes Beardless’, ‘P919’, ‘Robust’, ‘UC603’, ‘UC937’, ‘Washford Beardless’) were fall-sown in replicated strip plots in Fall 2016. Weed densities were measured in Spring 2017 and volumetric soil moisture near the soil surface (5.1 cm depth) was measured at time intervals beginning in December 2016 and ending in March 2017. With the exception of ‘UC937’, barley varieties caused marked reductions in weed density in comparison with the unplanted control. Soil moisture content for the unplanted control was consistently lower than soil moisture contents for barley plots. Barley variety did not influence volumetric soil moisture. During the 2017–2018 growing season, we re-examined three barley varieties considered most amenable to the cropping system requirements (‘Robust’, ‘UC603’, ‘P919’), and these varieties were again found to support few weeds (≤ 5.0 weeds m−2). We conclude that several organically certified barley varieties could fill the need for a ‘non-thirsty’ cover crop that suppresses winter weeds in irrigated organic systems in the southwestern United States.