Inference for cumulative risk model under step-stress experiments and its application in nanocrystalline data

With the popularity of step-stress accelerated life testing, researchers are exploring more possibilities for models that relate the life distributions under different stress levels. Cumulative risk model assumes that the effects of stress changes have a lag period before they are fully observed, which guarantees the continuity of the hazard rate function. This paper studies the cumulative risk model for Lomax distribution with step-stress experiments. For maximum likelihood estimation, Newton-Rapson method is adopted to get point estimates. Meanwhile, the asymptotic normality of the maximum likelihood estimator is used to obtain asymptotic confidence intervals. For Bayesian estimation, point estimates and highest posterior density credible intervals under squared error loss function with informative prior and non-informative prior are derived using Metropolis-Hastings method and Metropolis-Hastings within Gibbs algorithm. To evaluate the effects of stress change time and the length of lag period, as well as the performance of different methods, numerical simulations are conducted. Then a real nanocrystalline data set is analyzed.

Testing Causal Theories with Learned Proxies

Social scientists commonly use computational models to estimate proxies of unobserved concepts, then incorporate these proxies into subsequent tests of their theories. The consequences of this practice, which occurs in over two-thirds of recent computational work in political science, are underappreciated. Imperfect proxies can reflect noise and contamination from other concepts, producing biased point estimates and standard errors. We demonstrate how analysts can use causal diagrams to articulate theoretical concepts and their relationships to estimated proxies, then apply straightforward rules to assess which conclusions are rigorously supportable. We formalize and extend common heuristics for “signing the bias”—a technique for reasoning about unobserved confounding—to scenarios with imperfect proxies. Using these tools, we demonstrate how, in often-encountered research settings, proxy-based analyses allow for valid tests for the existence and direction of theorized effects. We conclude with best-practice recommendations for the rapidly growing literature using learned proxies to test causal theories. Expected final online publication date for the Annual Review of Political Science, Volume 25 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Regional pleural strain measurements during mechanical ventilation using ultrasound elastography: A randomised, crossover, proof of concept physiologic study

Background Mechanical ventilation is a common therapy in operating rooms and intensive care units. When ill-adapted, it can lead to ventilator-induced lung injury (VILI), which is associated with poor outcomes. Excessive regional pulmonary strain is thought to be a major mechanism responsible for VILI. Scarce bedside methods exist to measure regional pulmonary strain. We propose a novel way to measure regional pleural strain using ultrasound elastography. Research Question The objective of this study was to assess the feasibility and reliability of pleural strain measurement by ultrasound elastography and to determine if elastography parameters would correlate with varying tidal volumes. Study Design and Methods A single-blind randomized crossover proof of concept study was conducted July to October 2017 at a tertiary care referral center. Ten patients requiring general anesthesia for elective surgery were recruited. After induction, patients were received tidal volumes of 6, 8, 10 and 12 mL.kg-1 in random order, while pleural ultrasound cineloops were acquired at 4 standardized locations. Ultrasound radiofrequency speckle tracking allowed computing various pleural translation, strain and shear components. These were screened to identify those with the best dose-response with tidal volumes using linear mixed effect models. Goodness-of-fit was assessed by the coefficient of determination. Intraobserver, interobserver and test-retest reliability were calculated using intraclass correlation coefficients. Results Analysis was possible in 90.7% of ultrasound cineloops. Lateral absolute shear, lateral absolute strain and Von Mises strain varied significantly with tidal volume and offered the best dose-responses and data modelling fits. Point estimates for intraobserver reliability measures were excellent for all 3 parameters (0.94, 0.94 and 0.93, respectively). Point estimates for interobserver (0.84, 0.83 and 0.77, respectively) and test-retest (0.85, 0.82 and 0.76, respectively) reliability measures were good. Interpretation Strain imaging is feasible and reproducible, and may eventually guide mechanical ventilation strategies in larger cohorts of patients.

To Assess the Activities of Self-Regulatory Organizations of Auditors

In the article, from the standpoint of economic theory, the construction of partial and integral assessments of the activity of the SROA is considered through the assessment of the quality of this activity (and not its effectiveness or efficiency). The expediency of using the method of point estimates and its specific application in the construction of these estimates is shown - both from the standpoint of the national economic approach and from the standpoint of the SROA itself. A table of 18 main activities of the SROA is compiled, based mainly on legislative acts. A number of organizational aspects of the evaluation of the SROA activity are analyzed.

Child stature, maternal education, and early childhood development in Nigeria

Data from the 2016–17 Multiple Indicator Cluster Survey from Nigeria are used to study the relationship between child stature, mother’s years of education, and indicators of early childhood development (ECD). The relationships are contrasted between two empirical approaches: the conventional approach whereby control variables are selected in an ad-hoc manner, and the double machine-learning (DML) approach that employs data-driven methods to select controls from a much wider set of variables and thus reducing potential omitted variable bias. Overall, the analysis confirms that maternal education and the incidence of chronic malnutrition have a significant direct effect on measures of early childhood development. The point estimates based on the ad-hoc specification tend to be larger in absolute value than those based on the DML specification. Frequently, the point estimates based on the ad-hoc specification fall inside the confidence interval of the DML point estimates, suggesting that in these cases the omitted variable bias is not serious enough to prevent making causal inferences based on the ad-hoc specification. However, there are instances where the omitted variable bias is sufficiently large for the ad hoc specification to yield a statistically significant relationship when in fact the more robust DML specification suggests there is none. The DML approach also reveals a more complex picture that highlights the role of context. In rural areas, mother’s education affects early childhood development both directly and indirectly through its impact on the nutritional status of both older and younger children. In contrast, in urban areas, where the average level of maternal education is much higher, increases in a mother’s education have only a direct effect on child ECD measures but no indirect effect through child nutrition. Thus, DML provides a practical and feasible approach to reducing threats to internal validity for robust inferences and policy design based on observational data.

Wheelchair Skills Test Outcomes across Multiple Wheelchair Skills Training Bootcamp Cohorts

User training is a critical component of wheelchair service delivery to ensure individuals with a mobility impairment can negotiate environmental barriers and promote their social participation. A wheelchair “bootcamp”, delivered during professional preparation education, is one strategy to better prepare occupational therapists for clinical rehabilitation practice by developing their own wheelchair skills. The purpose of this study was a retrospective review of a large dataset of student cohorts from a single site and delineate bootcamp effects on the Wheelchair Skills Test-Questionnaire (WST-Q) scores. Participant data from eight cohorts was consolidated (n = 307). Comparison of two WST-Q scoring formats revealed significantly lower scores for cohorts using the 4-point version, which was subsequently standardized to the other 3-point version. WST-Q change scores were similar between cohorts, and differences were more reflective of variability in skill level prior to bootcamp than post-bootcamp scores. Students were able to master most basic and intermediate level skills, while advanced skill acquisition was much more variable. This study provides more precise point estimates of wheelchair skill acquisition among occupational therapy students than previous studies. While confirming the benefits of bootcamp education, recommendations for further investigation were identified.

BISoN: A Bayesian Framework for Inference of Social Networks

Social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around point estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent analyses. We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of observational social data, can capture confounds and model effects at the level of observations, and is fully compatible with popular methods of social network analysis. We show how the framework can be applied to common types of data and how various types of downstream analyses can be performed, including non-random association tests and regressions on network properties. Our framework opens up the opportunity to test new types of hypotheses, make full use of observational datasets, and increase the reliability of scientific inferences. We have made example R code available to enable adoption of the framework.

Basic principles of descriptive statistics in medical research

Descriptive statistics provides tools to explore, summarize and illustrate the research data. In this tutorial we discuss two main types of data - qualitative and quantitative variables, and the most common approaches to characterize data distribution numerically and graphically. This article presents two important sets of parameters - measures of the central tendency (mean, median and mode) and variation (standard deviation, quantiles) and suggests the most suitable conditions for their application. We explain the difference between the general population and random samples, that are usually analyzed in studies. The parameters which characterize the sample (for example, measures of the central tendency) are point estimates, that can differ from the respective parameters of the general population. We introduce the concept of confidence interval - the range of values, which likely includes the true value of the parameter for the general population. All concepts and definitions are illustrated with examples, which simulate the research data.

Allied but Deviating NATO in the Multipolar World

“How long can NATO last in a post-US hegemonic, multipolar world?” has become an important question in contemporary world politics. By statistically analyzing NATO alliance cohesion since its inception, this analysis contributes to the literature by developing an original set of indicators that rely on the ideal point estimates from a recent UN General Assembly voting dataset. It empirically verifies that NATO members have higher cohesion than other UN members, although the United States has been the most significant deviating member since 1980. The findings support some earlier proposals such as the external threat hypothesis. They also contradict some others, notably the literature on the Donald Trump administration’s withdrawal doctrine, and the decline of US hegemony and its policy implications. The article concludes that the future challenge for NATO cohesion not only would be the possibility of US abdication or abandonment, but also other members’ balancing the United States as the hegemon.

Noise Increases Anchoring Effects

We introduce a theoretical framework distinguishing between anchoring effects, anchoring bias, and judgmental noise: Anchoring effects require anchoring bias, but noise modulates their size. We tested this framework by manipulating stimulus magnitudes. As magnitudes increase, psychophysical noise due to scalar variability widens the perceived range of plausible values for the stimulus. This increased noise, in turn, increases the influence of anchoring bias on judgments. In 11 preregistered experiments ( N = 3,552 adults), anchoring effects increased with stimulus magnitude for point estimates of familiar and novel stimuli (e.g., reservation prices for hotels and donuts, counts in dot arrays). Comparisons of relevant and irrelevant anchors showed that noise itself did not produce anchoring effects. Noise amplified anchoring bias. Our findings identify a stimulus feature predicting the size and replicability of anchoring effects—stimulus magnitude. More broadly, we show how to use psychophysical noise to test relationships between bias and noise in judgment under uncertainty.