Power Approximations for Meta-Analysis of Dependent Effect Sizes

Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon the most common models for handling dependent effect sizes. In a Monte Carlo simulation, we show that the new power formulas can accurately approximate the true power of common meta-analytic models for dependent effect sizes. Lastly, we investigate the Type I error rate and power for several common models, finding that tests using robust variance estimation provide better Type I error calibration than tests with model-based variance estimation. We consider implications for practice with respect to selecting a working model and an inferential approach.

Rhetoric and Visual Argument: proposition of methodological models based on the analysis of infographics

This study proposes analytic models for multimodal genres, specifically for the infographic, based on a finished master’s research and in light of a brief literature review regarding multimodal argumentation. This study is justified by the fact that visual rhetoric is a relatively new field of investigation which still needs analytic proposals that promote the increase of studies in the area. This work, of a descriptive and qualitative nature, is grounded in theories and methodologies of the visual field (BLAIR, 2008; KJELDSEN, 2012; 2015; MATEUS, 2016; 2018; ROQUE, 2012; 2016; TSERONIS; FORCEVILLE, 2017; GONÇALVES-SEGUNDO, 2021; LEAL, 2021), and the gathering of data was based on Google Scholar through software Harzing’s Publish or Perish. The results show that, even though the procedures have been developed for the analysis of infographics, the analytic method may collaborate with the growing studies in multimodal argumentation and may be applied to other genres circulating in society

Economic Evaluation of Universal Lynch Syndrome Screening Protocols among Newly Diagnosed Patients with Colorectal Cancer

We conducted an updated economic evaluation, from a healthcare system perspective, to compare the relative effectiveness and efficiency of eight Lynch syndrome (LS) screening protocols among newly diagnosed colorectal cancer (CRC) patients. We developed decision analytic models for a hypothetical cohort of 1000 patients. Model assumptions and parameter values were based on literature and expert opinion. All costs were in 2018 USD. For identifying LS cases, the direct germline sequencing (DGS) protocol provided the best performance (sensitivity 99.90%, 99.57–99.93%; specificity 99.50%, 97.28–99.85%), followed by the tumor sequencing to germline sequencing (TSGS) protocol (sensitivity, 99.42%, 96.55–99.63%; specificity, 96.58%, 96.46–96.60%). The immunohistochemistry (IHC) protocol was most efficient at $20,082 per LS case identified, compared to microsatellite instability (MSI) ($22,988), DGS ($31,365), and TSGS ($104,394) protocols. Adding double-somatic testing to IHC and MSI protocols did not change sensitivity and specificity, increased costs by 6% and 3.5%, respectively, but reduced unexplained cases by 70% and 50%, respectively. DGS would be as efficient as the IHC protocol when the cost of germline sequencing declines under $368 indicating DGS could be an efficient option in the near future. Until then, IHC and MSI protocols with double-somatic testing would be the optimal choices.

Measuring public knowledge on nuclear weapons in the post-Cold War: dimensionality and measurement invariance across eight European countries

Research on public opinion and international security has extensively examined attitudes toward nuclear weapons, but the diffusion of basic knowledge about nuclear weapons among the everyday citizens has nevertheless been mostly missed. This study proposes a working definition and advances a measurement model of knowledge on nuclear weapons in the general public. It analyzes data from two novel surveys conducted in 2018 (N = 6559) and 2019 (N = 6227) where respondents from Belgium, France, Germany, Italy, the Netherlands, Poland, Sweden, and the United Kingdom answered a web survey on attitudes and factual knowledge on nuclear weapons. Exploratory and confirmatory factor analytic models are used to examine the dimensionality and to assess the measurement invariance of a scale of knowledge about nuclear weapons. A bifactor measurement model, where a strong general factor represents the construct of interest and specific factors account for the presence of testlets due to questionnaire design, is established and validated. Configural, metric, and scalar invariance are established across the eight samples. The findings indicate that knowledge about nuclear weapons in the general, non-expert public can be reliably measured cross-nationally.

Duration of Family Caregiving and Its Effects on Inflammation in the Caregiving Transitions Study

Sustained caregiving for older adult family members with disabilities can be a chronically stressful experience that may adversely affect the health of caregivers. Systemic inflammation is thought to be one mechanism by which caregiving stress might impact health, but previous studies of inflammation in caregivers have generally found inconsistent or very small effects with questionable clinical significance when comparing caregiving and non-caregiving control samples. The Caregiving Transitions Study (CTS) enrolled 283 family caregivers and 283 carefully-matched controls from an ongoing national epidemiologic study. This population-based sample of caregivers included an unusual subsample of 32 long-term caregivers who had been providing care to the same care recipients for over 9 consecutive years. Analyses of covariance indicated that these 32 long-term caregivers had statistically significant (p < 0.05) elevations on three circulating biomarkers of inflammation – C-reactive protein, Interleukin-6, and D-dimer – compared 1) to their 32 individually-matched non-caregiving controls, and 2) to the 248 caregivers who had been providing care for less than 9 years. Covariates in the analytic models included age, sex, race, and body mass index. Similar effects were observed for caregivers of persons with or without dementia. Polynomial regression models across all caregivers revealed significant curvilinear associations of inflammation with caregiving duration. Inflammation was not markedly elevated throughout the first several years of caregiving but then begin to increase more dramatically at around 10 years of caregiving. These findings suggest that long-term caregiving, in particular, may be associated with specific physical health risks through chronically elevated systemic inflammation.

Effective in-plane elastic properties of honeycomb-polymer composites

Honeycomb composites are now common materials in applications where high specific stiffness is required. Previous research has found that honeycombs with polymer infills in their cells, here referred to as honeycomb-polymer composites (HPCs), exhibit effective stiffnesses greater than the honeycomb or polymer alone. Currently, the state of analytic models for predicting the elastic properties of these composites is limited, and further research is needed to better characterize the behavior of these materials. In this research, a nonlinear finite element analysis was employed to perfor2m parametric studies of a filled honeycomb unit cell with isotropic wall and infill materials. A rigid wall model was created as an upper bound on the deformable wall model’s performance, and an empty honeycomb model was employed to better understand the mechanisms of stiffness amplification. Parametric studies were completed for infill material properties and cell geometry, with the effective Young’s modulus studied in two in-plane material directions. The mechanisms by which the stiffness amplification occurs are studied, and comparisons to existing analytic models are made. It has been observed that both the volume change within the honeycomb cell under deformation and the mismatch in Poisson’s ratios between the honeycomb and infill influence the effective properties. Stiffness amplifications of over 4000 have been observed, with auxetic behavior achieved by tailoring of the HPC geometry. Additionally, the effect of large effective strains up to 10% is explored, where the cell geometry changes significantly. This research provides an important step toward understanding the design space and benefits of HPCs.

Data Analytic Models That Redress the Limitations of MapReduce

The amount of data in today’s world is increasing exponentially. Effectively analyzing Big Data is a very complex task. The MapReduce programming model created by Google in 2004 revolutionized the big-data comput-ing market. Nowadays the model is being used by many for scientific and research analysis as well as for commercial purposes. The MapReduce model however is quite a low-level progamming model and has many limitations. Active research is being undertaken to make models that overcome/remove these limitations. In this paper we have studied some popular data analytic models that redress some of the limitations of MapReduce; namely ASTERIX and Pregel (Giraph) We discuss these models briefly and through the discussion highlight how these models are able to overcome MapReduce’s limitations.