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We study a version of the randomized Kaczmarz algorithm for solving systems of linear equations where the iterates are confined to the solution space of a selected subsystem. We show that the subspace constraint leads to an accelerated convergence rate, especially when the system has approximately low-rank structure. On Gaussian-like random data, we show that it results in a form of dimension reduction that effectively increases the aspect ratio of the system. Furthermore, this method serves as a building block for a second, quantile-based algorithm for solving linear systems with arbitrary sparse corruptions, which is able to efficiently utilize external knowledge about corruption-free equations and achieve convergence in difficult settings. Numerical experiments on synthetic and realistic data support our theoretical results and demonstrate the validity of the proposed methods for even more general data models than guaranteed by the theory. [ABSTRACT FROM AUTHOR]

Responsible leadership, a construct at the intersection of corporate social responsibility (CSR), ethics, and leadership, is considered to meet the conflicting demands of modern organizations by striking a balance between internal and external responsibilities. To further explore antecedents and outcomes of responsible leadership, scholars and practitioners need an instrument that can gauge the influence of responsible leadership on organizational members. The present study is a combination of four studies and is aimed at developing a measurement instrument using the sequential mixed method equal status approach. The study aims to explore responsibility‐oriented dimensions and their subdimensions that are perceived to be comparatively more relevant to responsible leadership by organizational members, while simultaneously, being supported by existing literature. An initial qualitative study, comprising 25 in‐depth interviews, revealed six responsibility‐oriented dimensions that could reliably measure the construct. Three quantitative studies, entailing collection and analysis of data for each (N = 608, 745, 800), were conducted to refine and validate the instrument. The study used the opinions of followers (organizational members) to determine the influence of responsible leadership in an organization. Based on the validity and reliability statistics of the three studies, the instrument is considered reliable for measurement of the influence of responsible leadership at individual, group, and organizational levels. [ABSTRACT FROM AUTHOR]

This study investigated reliability and validity of muscle cross-sectional area and echo intensity using an automatic image analysis program. Methods: Twenty-two participants completed two data collection trials consisting of ultrasound imaging of the vastus lateralis (VL) at 10 and 12 MHz. Images were analyzed manually and with Deep Anatomical Cross-Sectional Area (DeepACSA). Reliability statistics (i.e., intraclass correlation coefficient [ICC] model 2,1, standard error of measure expressed as a percentage of the mean [SEM%], minimal differences [MD] values needed to be considered real) and validity statistics (i.e., constant error [CE], total error [TE], standard error of the estimate [SEE]) were calculated. Results: Automatic analyses of ACSA and EI demonstrated good reliability (10 MHz: ICC 2,1 = 0.83 – 0.90; 12 MHz: ICC 2,1 = 0.87–0.88), while manual analyses demonstrated moderate to excellent reliability (10 MHz: ICC 2,1 = 0.82–0.99; 12 MHz: ICC 2,1 = 0.73–0.99). Automatic analyses of ACSA presented greater error at 10 (CE = -0.76 cm2, TE = 4.94 cm2, SEE = 3.65 cm2) than 12 MHz (CE = 0.17 cm2, TE = 3.44 cm2, SEE = 3.11 cm2). Analyses of EI presented greater error at 10 (CE = 3.35 a.u., TE = 2.70 a.u., SEE = 2.58 a.u.) than at 12 MHz (CE = 3.21 a.u., TE = 2.61 a.u., SEE = 2.34 a.u.). Conclusion: The results suggest the DeepACSA program may be less reliable compared to manual analysis for VL ACSA but displayed similar reliability for EI. In addition, the results demonstrated the automatic program had low error for 10 and 12 MHz. [ABSTRACT FROM AUTHOR]

Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system's explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity. [ABSTRACT FROM AUTHOR]

Background: Insertion of an external ventricular drain (EVD) is a first-line treatment of acute hydrocephalus caused by aneurysmal subarachnoid haemorrhage (aSAH). Once the patient is clinically stable, the EVD is either removed or replaced by a permanent internal shunt. The optimal strategy for cessation of the EVD is unknown. Prompt closure carries a risk of acute hydrocephalus or redundant shunt implantations, whereas gradual weaning may increase the risk of EVD-related infections. Methods: DRAIN (Danish RAndomised Trial of External Ventricular Drainage Cessation IN Aneurysmal Subarachnoid Haemorrhage) is an international multicentre randomised clinical trial comparing prompt closure versus gradual weaning of the EVD after aSAH. The primary outcome is a composite of VP-shunt implantation, all-cause mortality, or EVD-related infection. Secondary outcomes are serious adverse events excluding mortality and health-related quality of life (EQ-5D-5L). Exploratory outcomes are modified Rankin Scale, Fatigue Severity Scale, Glasgow Outcome Scale Extended, and length of stay in the neurointensive care unit and hospital. Outcome assessment will be performed 6 months after ictus. Based on the sample size calculation (event proportion 80% in the gradual weaning group, relative risk reduction 20%, alpha 5%, power 80%), 122 participants are required in each intervention group. Outcome assessment for the primary outcome, statistical analyses, and conclusion drawing will be blinded. Two independent statistical analyses and reports will be tracked using a version control system, and both will be published. Based on the final statistical report, the blinded steering group will formulate two abstracts. Conclusion: We present a pre-defined statistical analysis plan for the randomised DRAIN trial, which limits bias, p-hacking, and data-driven interpretations. This statistical analysis plan is accompanied by tables with simulated data, which increases transparency and reproducibility. Trial registration: ClinicalTrials.gov identifier: NCT03948256. Registered on May 13, 2019. [ABSTRACT FROM AUTHOR]

This document is a response to a comment on a study about transporting the results of an HIV clinical trial to external target populations. The authors discuss the importance of generalizing trial results to real-world patient populations and introduce a statistical framework called "genRCT" to address the issue of external validity in clinical trials. They emphasize the need to consider causal assumptions and conduct sensitivity analysis to ensure the validity of the study's conclusions. The letter also discusses ethical considerations in healthcare research, highlighting the importance of using real-world evidence alongside randomized controlled trials to enhance the external validity of study findings. The authors suggest implementing stratified clinical trials and developing transportability techniques that account for individual variation and fairness to address healthcare inequities. They also emphasize the need for further research on precision medicine and fairness constraints in individual treatment decision-making. [Extracted from the article]

It is unknown to what extent medical researchers generalize study findings beyond their samples when their sample size, sample diversity, or knowledge of conditions that support external validity do not warrant it. It is also unknown to what extent medical researchers describe their results with precise quantifications or unquantified generalizations, i.e., generics, that can obscure variations between individuals. We therefore systematically reviewed all prospective studies (n = 533) published in the top four highest ranking medical journals, Lancet, New England Journal of Medicine (NEJM), Journal of the American Medical Association (JAMA), and the British Medical Journal (BMJ), from January 2022 to May 2023. We additionally reviewed all NEJM Journal Watch clinical research summaries (n = 143) published during the same time. Of all research articles reporting prospective studies, 52.5% included generalizations beyond specific national study populations, with the numbers of articles with generics varying significantly between journals (JAMA = 12%; Lancet = 77%) (p < 0.001, V = 0.48). There was no evidence that articles containing broader generalizations or generics were correlated with larger or more nationally diverse samples. Moreover, only 10.2% of articles with generalizations beyond specific national populations reported external validity strengthening factors that could potentially support such extrapolations. There was no evidence that original research articles and NEJM Journal Watch summaries intended for practitioners differed in their use of broad generalizations, including generics. Finally, from the journal with the highest citation impact, articles containing broader conclusions were correlated with more citations. Since there was no evidence that studies with generalizations beyond specific national study populations or with generics were associated with larger, more nationally diverse samples, or with reports of population similarity that may permit extensions of conclusions, our findings suggest that the generalizations in many articles were insufficiently supported. Caution against overly broad generalizations in medical research is warranted. [ABSTRACT FROM AUTHOR]

Plastic has become part of daily life because it is used in all industrial applications. Although it is beneficial and practical, it is the material that generates the most pollution in ecosystems. Therefore, the main objective of this research is to produce plastic films capable of degrading the environment in a shorter time (bioplastics). For this purpose, different samples were made with cassava starch (pulp or commercial), using amounts in the solution of 4% w/v and 30% w/w of cassava starch and glycerol, respectively, considering variations between the percentages of chitosan. The results showed that it is possible to obtain bioplastic by the casting method with the mixture of cassava starch (pulp or commercial), glycerol, and chitosan. Likewise, thanks to the laboratory tests carried out, was possible to determine that the use of commercial starch to produce biodegradable plastic films significantly favors moisture adsorption, solubility, and biodegradability, compared to starch extracted from cassava by hand, with good statistics validity of the analyzed data. [ABSTRACT FROM AUTHOR]

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Objective: The aim of this research is to adapt the Mini-Service User Recovery Evaluation (MiniSeRvE) Scale to Turkish culture and establish its psychometric properties. Materials and Methods: This methodological study included 150 patients who applied to psychiatry outpatient clinics between March 2020 and January 2021. "The Personal Information Form," "The MiniService User Recovery Evaluation (MiniSeRvE) Scale," "The Subjective Recovery Assessment Scale," and "The Spiritual Well-Being Scale" were used to collect the data. Results: The CFA of the MiniSeRvE Scale determined that the three sub-dimensions and factor loadings were between 0.50 and 0.91, consistent with the original scale. The Cronbach's alpha coefficient was found to be 0.864 for the total scale and between 0.77 and 0.95 for the subdimensions. Conclusions: The MiniSeRvE Scale is a valid and reliable measurement tool. [ABSTRACT FROM AUTHOR]

A joint modeling framework was developed using data from 75 patients of early amcenestrant phase I–II AMEERA‐1‐2 dose escalation and expansion cohorts. A semi‐mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure‐driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression‐free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA‐3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I–II data. This provides a good modeling and simulation tool to inform early development decisions. [ABSTRACT FROM AUTHOR]

Given the urgent need for early detection of learning disorders such as dysgraphia, dyslexia, and dyscalculia in children, this study aimed to evaluate an expert system developed in Python to facilitate early diagnosis of these disorders. The background highlights the importance of providing parents, educators, and health professionals with an effective tool for early detection of these disorders. In 21 simulated cases, the system showed impressive performance with an accuracy rate of 95%, a precision of 100%, a sensitivity of 93%, and a specificity of 100%. Furthermore, the acceptability evaluation, conducted with 15 parents selected by convenience sampling, showed a high level of satisfaction, with an overall mean of 4.78 and a standard deviation of 0.45, indicating consistency in responses. In conclusion, this study confirms the effectiveness of the expert system in the early diagnosis of learning disabilities, providing parents, educators, and health professionals with a valuable tool. Despite these encouraging results, the need for additional research is recognized to address limitations and improve the external validity of the system to ensure its widespread utility and adaptability in real clinical settings. [ABSTRACT FROM AUTHOR]

Researchers and practitioners in marketing, economics, and public policy often use preference elicitation tasks to forecast real-world behaviors. These tasks typically ask a series of similarly structured questions. The authors posit that every time a respondent answers an additional elicitation question, two things happen: (1) they provide information about some parameter(s) of interest, such as their time preference or the partworth for a product attribute, and (2) the respondent increasingly "adapts" to the task—that is, using task-specific decision processes specialized for this task that may or may not apply to other tasks. Importantly, adaptation comes at the cost of potential mismatch between the task-specific decision process and real-world processes that generate the target behaviors, such that asking more questions can reduce external validity. The authors used mouse and eye tracking to trace decision processes in time preference measurement and conjoint choice tasks. Respondents increasingly relied on task-specific decision processes as more questions were asked, leading to reduced external validity for both related tasks and real-world behaviors. Importantly, the external validity of measured preferences peaked after as few as seven questions in both types of tasks. When measuring preferences, less can be more. [ABSTRACT FROM AUTHOR]

Background: The CLN2 Clinical Rating Scale evaluates disease progression in CLN2 disease, an ultra-rare, neurodegenerative disorder with late infantile onset. To validate the Clinical Rating Scale, a comparison with the Pediatric Quality of Life Inventory (PedsQL) was conducted utilising clinical trial data investigating cerliponase alfa use in CLN2 disease. Methods: Linear regression and mixed effects models were used to investigate the relationship between the Clinical Rating Scale and PedsQL using open-label, single-arm, phase 1/2 (NCT01907087) and ongoing extension study (NCT02485899) data of 23 children with CLN2 disease treated with cerliponase alfa for ≥96 weeks. Results: Correlations between the four Clinical Rating Scale domains were low. Linear mixed effects analyses showed significant correlation between PedsQL and Clinical Rating Scale (Total score or motor-language [ML] score adjusted p-values <0.05), driven by the relationship with the PedsQL Physical domain. A statistically significant relationship was identified between the Clinical Rating Scale motor domain and PedsQL (Total score: adjusted p-value = 0.048, parameter estimate [PE] = 8.10; Physical domain score: adjusted p-value = 0.012; PE = 13.79). Conclusions: Each domain of the Clinical Rating Scale provides unique information on disease state. Validity of the scale is supported by its relationship with the PedsQL. Among the four domains of the Clinical Rating Scale, motor has the highest correlation to PedsQL, suggesting motor function as a driver of patients' quality of life. The lack of association between the remaining domains of the Clinical Rating Scale and PedsQL suggests that additional disease-specific measures may be needed to fully capture the quality of life impact of CLN2 disease. Trial registration: NCT01907087, NCT02485899. [ABSTRACT FROM AUTHOR]

Non-negative integer-valued time series are usually encountered in practice, and a variety of integer-valued autoregressive processes based on various thinning operators are commonly used to model these count data with temporal dependence. In this paper, we consider a first-order integer-valued autoregressive process constructed by the negative binomial thinning operator with random coefficients, to address the problem of constant thinning parameters which might not always accurately represent real-world settings because of numerous external and internal causes. We estimate the model parameters of interest by the two-step conditional least squares method, obtain the asymptotic behaviors of the estimators, and furthermore devise a technique to test the constancy of the thinning parameters, which is essential for determining whether or not the proposed model should consider the parameters' randomness. The effectiveness and dependability of the suggested approach are illustrated by a series of thorough simulation studies. Finally, two real-world data analysis examples reveal that the suggested approach is very useful and flexible for applications. [ABSTRACT FROM AUTHOR]

While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This article describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only. [ABSTRACT FROM AUTHOR]

Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually under-represent minorities. Model-based methods use population registries to improve external validity of risk estimation but assume hazard ratios are generalisable from samples to the target finite population. 'Pseudoweighting' methods improve representativeness of studies by using an external probability-based survey as the reference, but the resulting estimators can be biased due to propensity model misspecification and inefficient due to highly variable pseudoweights or small sample sizes of minorities in the cohort and/or survey. We propose a two-step pseudoweighting procedure that post-stratifies the event rates among age/race/sex strata in the pseudoweighted cohort to the population rates, to produce efficient and robust pure risk estimation (i.e. a cause-specific absolute risk in the absence of competing events). For developing an all-cause mortality risk model representative for the USA, our findings suggest that hazard ratios for minorities are not generalisable, and that surveys can have inadequate numbers of events for minorities. Post-stratification on event rates is crucial for obtaining reliable risk estimation for minority subgroups. [ABSTRACT FROM AUTHOR]

Incorporation of external information is becoming increasingly common when designing clinical trials. Availability of multiple sources of information has inspired the development of methodologies that account for potential heterogeneity not only between the prospective trial and the pooled external data sources but also between the different external data sources themselves. Our approach proposes an intuitive way of handling such a scenario for the continuous outcomes setting by using propensity score-based stratification and then utilizing robust meta-analytic predictive priors for each stratum to incorporate the prior data to distinguish among different external data sources in each stratum. Through extensive simulations, our approach proves to be more efficient and less biased than the currently available methods. A real case study using clinical trials that study schizophrenia from multiple different sources is also included. [ABSTRACT FROM AUTHOR]

Objectives: The Alberta Stroke Program Early CT Score (ASPECTS) is a semi-quantitative method to evaluate the severity of early ischemic change on non-contrast computed tomography (NCCT) in patients with acute ischemic stroke (AIS). In this work, we propose an automated ASPECTS method based on large cohort of data and machine learning. Methods: For this study, we collected 3626 NCCT cases from multiple centers and annotated directly on this dataset by neurologists. Based on image analysis and machine learning methods, we constructed a two-stage machine learning model. The validity and reliability of this automated ASPECTS method were tested on an independent external validation set of 300 cases. Statistical analyses on the total ASPECTS, dichotomized ASPECTS, and region-level ASPECTS were presented. Results: On an independent external validation set of 300 cases, for the total ASPECTS results, the intraclass correlation coefficient between automated ASPECTS and expert-rated was 0.842. The agreement between ASPECTS threshold of ≥ 6 versus < 6 using a dichotomized method was moderate (κ = 0.438, 0.391–0.477), and the detection rate (sensitivity) was 86.5% for patients with ASPECTS threshold of ≥ 6. Compared with the results of previous studies, our method achieved a slight lead in sensitivity (67.8%) and AUC (0.845), with comparable accuracy (78.9%) and specificity (81.2%). Conclusion: The proposed automated ASPECTS method driven by a large cohort of NCCT images performed equally well compared with expert-rated ASPECTS. This work further demonstrates the validity and reliability of automated ASPECTS evaluation method. Clinical relevance statement: The automated ASPECTS method proposed by this study may help AIS patients to receive rapid intervention, but should not be used as a stand-alone diagnostic basis. Key Points: NCCT-based manual ASPECTS scores were poorly consistent. Machine learning can automate the ASPECTS scoring process. Machine learning model design based on large cohort data can effectively improve the consistency of ASPECTS scores. [ABSTRACT FROM AUTHOR]

The article discusses the reproducibility initiative at the Journal of the American Statistical Association (JASA). The initiative aims to address the lack of standardized practices for reproducibility in scientific research. It establishes minimum criteria for the inclusion of code, data, and workflow in research manuscripts and has implemented a new editorial role, the Associate Editor of Reproducibility (AER), to enforce these standards. The article explains the type of reproducibility being addressed, describes the steps of the review process, and provides guidelines for authors to streamline their reproducibility materials. The JASA reproducibility initiative is part of a larger movement in the scientific community to address the replicability crisis in science. The article emphasizes the importance of reproducibility in statistics to support the validity of research and foster transparency and accountability. It also addresses common questions and misconceptions about the reproducibility review process at JASA. The article concludes by encouraging wider discussions and the adoption of common standards for reproducibility in statistical journals. [Extracted from the article]

The past decade has seen substantial innovation in clinical trials, including new trial formats, endpoints, and others. Also there have been regulatory changes, increasing competitive pressures and other external factors which impact clinical trials. In parallel, trial timelines have increased and success rates remain stubbornly low. This has led many observers to question whether clinical trials have become overly complex and if this complexity is always needed. Here we present a large-scale analysis of protocols and other data from over 16,000 trials. Using a machine learning algorithm, we automatically assessed key features of these trials, such as number of endpoints, number of inclusion–exclusion criteria and others. Using a regression analysis we combined these features into a new metric, the Trial Complexity Score, which correlates with overall clinical trial duration. Looking at this score across different clinical phases and therapeutic areas we see substantial increases over time, suggesting that clinical trials are indeed becoming more complex. We discuss drivers of increasing trial complexity, necessary or helpful ('good') complexity versus unnecessary ('bad') complexity, and we explore mechanisms of how sponsors of clinical trials can reduce trial complexity where appropriate. [ABSTRACT FROM AUTHOR]

Introduction: In causal inference, the correct formulation of the scientific question of interest is a crucial step. The purpose of this study was to apply causal inference principles to external control analysis using observational data and illustrate the process to define the estimand attributes. Methods: This study compared long-term survival outcomes of a pooled set of three previously reported randomized phase 3 trials studying patients with metastatic non-small cell lung cancer receiving front-line chemotherapy and similar patients treated with front-line chemotherapy as part of routine clinical care. Causal inference frameworks were applied to define the estimand aligned with the research question and select the estimator to estimate the estimand of interest. Results: The estimand attributes of the ideal trial were defined using the estimand framework. The target trial framework was used to address specific issues in defining the estimand attributes using observational data from a nationwide electronic health record-derived de-identified database. The two frameworks combined allow to clearly define the estimand and the aligned estimator while accounting for key baseline confounders, index date, and receipt of subsequent therapies. The hazard ratio estimate (point estimate with 95% confidence interval) comparing the randomized clinical trial pooled control arm with the external control was close to 1, which is indicative of similar survival between the two arms. Discussion: The proposed combined framework provides clarity on the causal contrast of interest and the estimator to adopt, and thus facilitates design and interpretation of the analyses. [ABSTRACT FROM AUTHOR]

Generalizability of trial-based cost-effectiveness estimates to real-world target populations is important for decision making. In the context of independent aggregate time-to-event baseline and relative effects data, complex hazards can make modeling of data for use in economic evaluation challenging. Our article provides an overview of methods that can be used to apply trial-derived relative treatment effects to external real-world baselines when faced with complex hazards and follows with a motivating example. Approaches for applying trial-derived relative effects to real-world baselines are presented in the context of complex hazards. Appropriate methods are applied in a cost-effectiveness analysis using data from a previously published study assessing the real-world cost-effectiveness of a treatment for carcinoma of the head and neck as a motivating example. Lack of common hazards between the trial and target real-world population, a complex baseline hazard function, and nonproportional relative effects made the use of flexible models necessary to adequately estimate survival. Assuming common distributions between trial and real-world reference survival substantially affected survival and cost-effectiveness estimates. Modeling time-dependent vs proportional relative effects affected estimates to a lesser extent, dependent on assumptions used in cost-effectiveness modeling. Appropriately capturing reference treatment survival when attempting to generalize trial-derived relative treatment effects to real-world target populations can have important impacts on cost-effectiveness estimates. A balance between model complexity and adequacy for decision making should be considered where multiple data sources with complex hazards are being evaluated. • Generalizability is an important consideration for decision making. • Approaches used for applying trial-derived treatment effects to real-world target population baseline risks can substantially affect cost-effectiveness estimates. • Model complexity in light of requirements for informed decision making should be considered when generalizing cost-effectiveness estimates. [ABSTRACT FROM AUTHOR]

This study introduces distinct types of validity and reliability evidence of the Authenticity Scale (AS) in a sample from Brazil and Portugal. It consists of an online survey with 1,077 Brazilian citizens and 622 Portuguese citizens. The study tested the model with three correlated factors (self-alienation, authentic living, and accepting external influence), the unidimensional model, and the bi-factor model. The model with three correlated factors was retained, with the three subscales demonstrating moderate to good reliability. Multigroup confirmatory factor analysis suggested scalar invariance across culture, gender, age, education, occupation, and Covid-related concern and impact. The items were assessed by graded response model (GRM), which suggested that the three subscales are not able to distinguish respondents with high authenticity traits. GRM and descriptive statistics indicated that the rating-scale is inappropriate, particularly for authentic living subscale, which is affected by ceiling effect. Associations with presence of meaning showed additional validity evidence. Despite the limitations, the AS is an effective measure to assess authenticity across different groups. Potential modifications for the improvement of the AS are discussed. [ABSTRACT FROM AUTHOR]

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the "scale-agnostic" $G_{+}$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $k$ groups, we show that $G_{+}$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $G_{+}$, referred to as $H_{+}$, and demonstrate that $H_{+}$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $H_{+}$, which are available in the $\mathtt{fasthplus}$ R package. [ABSTRACT FROM AUTHOR]

This article explores the significance of communication science and research methodology, focusing on the advancements made by the journal Communication Methods and Measures (CMM). It acknowledges the existing challenges in the field, such as the lack of standardization and outdated data analysis techniques. The article emphasizes the need for clear standards and expectations in research methodology, particularly in areas like scale development, validation, and computational methods. It also discusses the importance of computational methods in studying the impact of algorithms on social media platforms, while addressing the issues of fragmentation and lack of transparency in computational research. The authors express their commitment to maintaining high-quality research and extending its impact beyond communication science. [Extracted from the article]

Background: Full pulpotomy has been proposed as an alternative to root canal treatment in teeth with signs and symptoms indicative of irreversible pulpitis (IRP), but the evidence is limited, relying on underpowered studies with a high risk of bias. The aim of this study is to conduct a prospective meta-analysis (PMA) of individual participant data of a series of individual randomised trials to provide robust evidence on the clinical and cost-effectiveness of pulpotomy compared with root canal treatment. Methods: Individual participant data will be obtained from a series of randomised trials designed and conducted by a consortium of multi-national investigators with an interest in vital pulp treatment. These individualised trials will be conducted using a specified protocol, defined outcomes, and outcome measures. Ten parallel-group randomised trials currently being conducted in 10 countries will provide data from more than 500 participants. The primary outcome is a composite measure defined as (1) the absence of pain indicative of IRP, (2) the absence of signs and symptoms indicative of acute or chronic apical periodontitis, and (3) the absence of radiographic evidence of failure including radiolucency or resorption. Individual participant data will be obtained, assessed, and checked for quality by two independent reviewers prior to the PMA. Pooled estimates on treatment effects will be generated using a 2-stage meta-analysis approach. The first stage involves a standard regression analysis in each trial to produce aggregate data on treatment effect estimates followed by an inverse variance weighted meta-analysis to combine these aggregate data and produce summary statistics and forest plots. Cost-effectiveness analysis based on the composite outcome will be undertaken as a process evaluation to evaluate treatment fidelity and acceptability by patients and dentists. Results: The research question and trial protocol were developed and approved by investigators in all 10 sites. All sites use shared resources including study protocols, data collection forms, participant information leaflets, and consent forms in order to improve flow, consistency, and reproducibility. Each site obtained its own Institutional Review Board approval, and trials were registered in appropriate open access platforms. Patient recruitment has started in most sites, as of July 2023. Discussion: PMA offers a rigorous, flexible, and efficient methodology to answer this important research question and provide results with improved generalisability and external validity compared with traditional trials and retrospective meta-analyses. The results of this study will have implications for both the delivery of clinical practice and structured clinical guidelines' development. Trial registration: PROSPERO CRD42023446809. Registered on 08 February 2023. [ABSTRACT FROM AUTHOR]

Prior research based on Swedish data suggests that collective optimism, as measured by monthly incidence of suicides, correlates inversely with selection in utero against male twins in a population. We test this finding in the US, which reports the highest suicide rate of all high-income countries, and examine whether monthly changes in overall suicides precede changes in the ratio of male twin to male singleton live births. Consistent with prior work, we also examine as a key independent variable, suicides among women aged 15−49 years. We retrieved monthly data on suicides and the ratio of male twin to singleton live births from CDC WONDER, 2003 to 2019, and applied Box-Jenkins iterative time-series routines to detect and remove autocorrelation from both series. Results indicate that a 1% increase in monthly change in overall suicides precedes a 0.005 unit decline in male twin live births ratio 6 months later (coefficient = −.005, p value =.004). Results remain robust to use of suicides among reproductive-aged women as the independent variable (coefficient = −.0012, p value =.014). Our study lends external validity to prior research and supports the notion that a decline in collective optimism corresponds with greater selection in utero. [ABSTRACT FROM AUTHOR]

While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well‐represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a novel class of conditional cross‐design synthesis estimators that combine randomized and observational data, while addressing their estimates' respective biases—lack of overlap and unmeasured confounding. These methods enable estimating the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City, which requires obtaining estimates for the 7% of beneficiaries randomized to a plan and 93% who choose a plan, who do not resemble randomized beneficiaries. Our new estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. Applying these methods, we find substantial heterogeneity in spending effects across managed care plans. This has major implications for our understanding of Medicaid, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in this setting. [ABSTRACT FROM AUTHOR]

Dynamic Structural Equation Model (DSEM) is a powerful statistical modeling approach that has recently gained popularity among researchers studying intensive longitudinal data. Despite its exciting potential, the stability and replicability of DSEM is yet to be closely examined. This study empirically investigates DSEM using recently published data to explore its strengths and potential limitations. The results show that while some of its parameter estimates are stable, others are characterized by substantial variation as a function of seemingly innocuous initial model estimation conditions. Indeed, some parameters fluctuate between significance and non-significance for the same model estimated using the same data. The instability of DSEM estimates poses a serious threat to the internal and external validity of conclusions drawn from its analyses, challenging the reproducibility of findings from applied research. Given the recent focus on the replication crisis in psychology, it is critical to address these issues as the popularity of DSEM in psychological research continues to rise. Several potential solutions are investigated to address this problem and recommendations of best practice are offered to applied researchers who plan to use DSEM in intensive longitudinal data analysis. [ABSTRACT FROM AUTHOR]

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19. [ABSTRACT FROM AUTHOR]

A key concern in crime analysis is the "hidden crime" problem. Crime events unaccounted for in police records limit the external validity of official statistics and, more importantly, hinder the ability of the police to manage crime and utilize their resources effectively. The problem is exacerbated in proactive initiatives aimed at curbing violence through hotspot policing, where inaccuracies and imprecision, or, worse, no data at all, diminish prevention efforts. Previous studies have sought to overcome the data problem by juxtaposing police records with ambulance data on assault callouts and have found profound disparities. Specifically, researchers matched "crime hotspots" with "ambulance hotspots" (rather than individual events) because patient confidentiality considerations have prevented health professionals from sharing subject‐level data with the police. However, health services can safely share spatial data on wider areas that do not disclose personal information. We build on this line of inquiry by analyzing data from the Thames Valley, United Kingdom, and observing spatial hotspots of different sizes. The results demonstrate that while the police and ambulance services attend to the same communities and similar types of facilities, the police are "blinded" to the location of nearly 8 out of 10 assaults. The incongruency is shown even with severe assaults, but to a lesser extent. We then simulate the reduction in injuries if the police had access to health data at different spatial levels and show that even under the most conservative set of assumptions, such an approach can prevent between 113 and 116 violent injuries each year that might otherwise require hospitalization. [ABSTRACT FROM AUTHOR]

Introduction: Physical harm from Colorectal Cancer Screening tends to be inadequately measured and reported in clinical trials. Also, studies of ongoing Colorectal Cancer Screening programs have found more frequent and severe physical harm from screening procedures, e.g., bleeding and perforation, than reported in previous trials. Therefore, the objectives of the study were to systematically review the evidence on the risk of bleeding and perforation in Colorectal Cancer Screening. Design: Systematic review with descriptive statistics and random-effects meta-analyses. Methods: We systematically searched five databases for studies investigating physical harms related to Colorectal Cancer Screening. We assessed the internal and the external validity using the ROBINS-I tool and the GRADE approach. Harm estimates was calculated using mixed Poisson regression models in random-effect meta-analyses. Results: We included 89 studies. Reporting and measurement of harms was inadequate in most studies. In effect, the risk of bias was critical in 97.3% and serious in 98.3% of studies. All GRADE ratings were very low. Based on severe findings with not-critical risk of bias and 30 days follow-up, the risk of bleedings per 100,000 people screened were 8 [2;24] for sigmoidoscopy, 229 [129;408] for colonoscopy following fecal immunochemical test, 68 [39;118] for once-only colonoscopy, and 698 [443;1045] for colonoscopy following any screening tests. The risk of perforations was 88 [56;138] for colonoscopy following fecal immunochemical test and 53 [25;112] for once-only colonoscopy. There were no findings within the subcategory severe perforation with long-term follow-up for colonoscopy following any screening tests and sigmoidoscopy. Discussion: Harm estimates varied widely across studies, reporting and measurement of harms was mostly inadequate, and the risk of bias and GRADE ratings were very poor, collectively leading to underestimation of harm. In effect, we consider our estimates of perforation and bleeding as conservative, highlighting the need for better reporting and measurement in future studies. Trial registration: PROSPERO registration number:CRD42017058844. [ABSTRACT FROM AUTHOR]

Background: Colorectal adenoma (CRA) is a significant precancerous lesion of sporadic colorectal cancer (CRC). CRA is likely to recur after polypectomy, increasing the risk of CRC. Chinese herbal medicine (CHM) has been used to reduce CRA recurrence. This review aims to evaluate the effectiveness and safety of oral CHM in reducing CRA recurrence compared to other treatments (placebo, routine care, no treatment, and conventional medicine). Methods: We will search for randomised controlled trials (RCTs) from nine major biomedical databases in English and Chinese from their inception to July 2023. The RCTs that investigate the effects of oral CHM in reducing CRA recurrence compared to other treatments will be involved. We will exclude trials using CHM extract or external application of CHM, cohort study and cross-section study. The Cochrane Risk of Bias Tool version 2 will be used to assess the quality of included studies. Data will be analysed using Review Manager software 5.4 and STATA. The random effect model will be used. The heterogeneity of intervention effects will be tested by Chi2 (Cochrane Q) and I2 statistics. Funnel plots will assess publication bias if more than ten studies are included. Subgroup and sensitivity analysis will be conducted when possible. Discussion: This review will discuss the effectiveness and safety of oral CHM in reducing CRA recurrence. It will show the critical information for clinicians in the decision-making process and countries to develop clinical guidelines on CRA management. Systematic review registration PROSPERO CRD42023324197. [ABSTRACT FROM AUTHOR]

Objective: Equinus foot deformity (EFD) is the most common deviation after stroke. Several physiotherapy interventions have been suggested to treat it. However, studies evaluating the efficacy of these treatments vary widely in terms of assessment modalities, type of data analysis, and nomenclature. This scoping review aimed to map current available evidence on outcome measures and the modalities employed to assess the effectiveness of physiotherapy programs for the reduction of triceps surae (TS) spasticity and EFD in patients with stroke. Methods: Scoping review methodological frameworks have been used. Three databases were investigated. Primary literature addressing TS spasticity in adult patients with stroke using physiotherapy interventions was included. Findings were systematically summarized in tables according to the intervention used, intervention dosage, control group, clinical, and instrumental outcome measures. Results: Of the 642 retrieved studies, 53 papers were included. TS spasticity was assessed by manual maneuvers performed by clinicians (mainly using the Ashworth Scale), functional tests, mechanical evaluation through robotic devices, or instrumental analysis and imaging (such as the torque-angle ratio, the H-reflex, and ultrasound images). A thorough critical appraisal of the construct validity of the scales and of the statistics employed was provided, particularly focusing on the choice of parametric and non-parametric approaches when using ordinal scales. Finally, the complexity surrounding the concept of "spasticity" and the possibility of assessing the several underlying active and passive causes of EFD, with a consequent bespoke treatment for each of them, was discussed. Conclusion: This scoping review provides a comprehensive description of all outcome measures and assessment modalities used in literature to assess the effectiveness of physiotherapy treatments, when used for the reduction of TS spasticity and EFD in patients with stroke. Clinicians and researchers can find an easy-to-consult summary that can support both their clinical and research activities. [ABSTRACT FROM AUTHOR]

As there are not enough measurement instruments to assess emotions during epistemic activities, this research aimed to evaluate the psychometric properties of the epistemic emotions scale among speakers of the Persian language. The scale has previously been used and studied in different cultural contexts. The present study sought to expand the application of this scale in the Iranian context. A total of 1153 students (58.6% women) within the age range of 17 to 29 years participated in this study. The mean and standard deviation of the age of the participants were 21.63 and 2.24, respectively. The data collection tools in this study were the Epistemic Emotions Scale, the Achievement Emotions Questionnaire, the Academic Buoyancy Scale, and the Academic Satisfaction Scale. CFA was performed to test the factorial structure (χ2 = 1895.96, df = 394, GFI = .86, CFI = .97, IFI = .97, NFI = .97, NNFI = .96, SRMR = .069, and RMSEA = .086) and its validity and reliability were also evaluated. The seven-factor model of the Epistemic Emotions Scale was the same as that of the original scale version, and the Epistemic Emotions Scale showed good internal consistency with Cronbach’s alpha for this scale ranging from .85 to .88.. The results showed that the EES scores also correlated in expected directions with measures of related constructs included in this study thus also supporting the external validity of this version of the scale.We can conclude that the Epistemic Emotions Scale is a valid and reliable tool for measuring students’ epistemic emotions, and as a result, researchers can use this tool for research purposes. [ABSTRACT FROM AUTHOR]

In single-arm clinical trials with external controls, usually the estimand of interest is defined as the average treatment effect on the treated (ATT), and external controls are leveraged to provide an estimator of the estimand. Recently, virtual control approaches have been proposed to predict the outcomes for experimental study subjects as if they were receiving the control treatment, resulting in so-called “virtual controls” for comparison with their observed outcomes under the experimental intervention. We consider the virtual control approaches within the causal inference framework, discussing the properties of the vanilla virtual control approach and the targeted virtual control approach. We illustrate via simulation that the targeted virtual control approach is doubly robust, whereas the vanilla virtual control approach is not. We demonstrate the targeted virtual control approach is the same as the targeted maximum likelihood estimator (TMLE) when targeted at the ATT estimand. Therefore, through the notion of virtual controls, we offer a tangible way to understand and interpret TMLE when targeted at the ATT estimand. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the association between the working relationship between internal and external auditors and the moral courage of internal auditors to report management fraud in the Tunisian setting. Design/methodology/approach: Data are gathered from 163 internal auditors working in Tunisian companies and a partial least squares–structural equation model (PLS-SEM) is used to test the hypothesis regarding the effect of the cooperation between internal and external auditors on internal auditors' moral courage. Findings: The results of this study provide strong empirical support for the positive impact of the working relationship between internal and external auditors on internal auditors' moral courage to report management fraud and unethical behaviors. Practical implications: The reported results increase the awareness of Tunisian regulators to enact regulations that strengthen the collaboration between internal and external auditors to promote internal auditors' moral courage and then limit fraud and improve organizational performance in the Tunisian setting. Originality/value: This paper fills one of the major research gaps in internal audit and moral courage research streams by revealing that the courageous behavior of internal auditors can be fostered by specific means efficacy such as the working relationship between internal and external auditors. [ABSTRACT FROM AUTHOR]

Justifying sample size for a pilot trial is a reporting requirement, but few pilot trials report a clear rationale for their chosen sample size. Unlike full-scale trials, pilot trials should not be designed to test effectiveness, and so, conventional sample size justification approaches do not apply. Rather, pilot trials typically specify a range of primary and secondary feasibility objectives. Often, these objectives relate to estimation of parameters that inform the sample size justification for the full-scale trial, many of which are binary. These binary outcomes are referred to as "feasibility outcomes" and include expected prevalence of the primary trial outcome, primary outcome availability, or recruitment or retention proportions. For pilot cluster trials, sample size calculations depend on the number of clusters, the cluster sizes, the anticipated intra-cluster correlation coefficient for the feasibility outcome and the anticipated proportion for that outcome. Of key importance is the intra-cluster correlation coefficient for the feasibility outcome. It has been suggested that correlations for feasibility outcomes are larger than for clinical outcomes measuring effectiveness. Yet, there is a dearth of information on realised values for these correlations. In this tutorial, we demonstrate how to justify sample size in external pilot cluster trials where the objective is to estimate a binary feasibility outcome. We provide sample size calculation formulae for a variety of scenarios, make available an R Shiny app for implementation, and compile a report of intra-cluster correlations for feasibility outcomes from a convenience sample. We demonstrate that unless correlations are very low, external pilot cluster trials can be made more efficient by including more clusters and fewer observations per cluster. [ABSTRACT FROM AUTHOR]

Methods to extend the strong internal validity of randomized controlled trials to reliably estimate treatment effects in target populations are gaining attention. This paper enumerates steps recommended for undertaking such extended inference, discusses currently viable choices for each one, and provides recommendations. We demonstrate a complete extended inference from a clinical trial studying a pharmaceutical treatment for Alzheimer's disease (AD) to a realistic target population of European residents diagnosed with AD. This case study highlights approaches to overcoming practical difficulties and demonstrates limitations of reliably extending inference from a trial to a real-world population. [ABSTRACT FROM AUTHOR]

Background: A modeling framework was previously developed to simulate overall survival (OS) using tumor growth inhibition (TGI) data from six randomized phase 2/3 atezolizumab monotherapy or combination studies in non-small-cell lung cancer (NSCLC). We aimed to externally validate this framework to simulate OS in patients with treatment-naive advanced anaplastic lymphoma kinase (ALK)-positive NSCLC in the alectinib ALEX study. Methods: TGI metrics were estimated from a biexponential model using longitudinal tumor size data from a Phase 3 study evaluating alectinib compared with crizotinib in patients with treatment-naive ALK-positive advanced NSCLC. Baseline prognostic factors and TGI metric estimates were used to predict OS. Results: 286 patients were evaluable (at least baseline and one post-baseline tumor size measurements) out of 303 (94%) followed for up to 5 years (cut-off: 29 November 2019). The tumor growth rate estimate and baseline prognostic factors (inflammatory status, tumor burden, Eastern Cooperative Oncology Group performance status, race, line of therapy, and sex) were used to simulate OS in ALEX study. Observed survival distributions for alectinib and crizotinib were within model 95% prediction intervals (PI) for approximately 2 years. Predicted hazard ratio (HR) between alectinib and crizotinib was in agreement with the observed HR (predicted HR 0.612, 95% PI 0.480–0.770 vs. 0.625 observed HR). Conclusion: The TGI-OS model based on unselected or PD-L1 selected NSCLC patients included in atezolizumab trials is externally validated to predict treatment effect (HR) in a biomarker-selected (ALK-positive) population included in alectinib ALEX trial suggesting that TGI-OS models may be treatment independent. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units. Methods: APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis. Results: Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability. Conclusions: Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units. [ABSTRACT FROM AUTHOR]

Background: Although there has been a significant focus on improving maternal and newborn health and expanding services in Nepal, the expected positive impact on the health of mothers and newborns has not been achieved to the desired extent. Nepal not only needs to focus on improving access to and coverage of services but also the quality to achieve Sustainable Development Goals (SDG) by 2030. In this context, we aimed to analyze Basic Emergency Obstetric and Neonatal Care (BEmONC) service availability and readiness in Health Facilities (HFs) of Nepal. Methods: We analyzed data from nationally representative Nepal Health Facility Survey (NHFS), 2021. BEmONC service availability and readiness in HFs was measured based on the "Service Availability and Readiness" manual of World Health Organization (WHO). We measured service availability by seven BEmONC signal functions. The readiness score was calculated for three domains- guidelines and staff training, essential equipment/supplies, and essential medicines on a scale of 100, and the average score for the three domains was the overall readiness score. We performed weighted descriptive and inferential analysis to account complex survey design of NHFS 2021. We summarized continuous variables with descriptive statistics like mean, standard deviation, median and interquartile range whereas categorical variables with percent and 95% confidence interval (CI). We applied simple, and multivariate linear regression to determine factors associated with the readiness of HFs for BEmONC services, and results were presented as beta (β) coefficients and 95% CI. Results: Of total 804 HFs offering normal vaginal delivery services, 3.1%, 89.2%, 7.7% were federal/provincial hospitals, local HFs, and private hospitals respectively. A total of 45.0% (95% CI: 34.9, 55.6) federal/provincial hospitals, 0.3% (95% CI: 0.2, 0.6), local HFs (district hospital, primary health care centers, health posts, urban health centers) and 10.5% (95% CI: 6.6, 16.4) private hospitals, had all seven BEmONC signal functions. The overall readiness of federal/provincial hospitals, local HFs, and private hospitals were 72.9±13.6, 54.2±12.8, 53.1±15.1 respectively. In multivariate linear regression, local HFs (β = -12.64, 95% CI: -18.31, -6.96) and private hospitals had lower readiness score (β = -18.08, 95% CI: -24.08, -12.08) compared to federal/provincial level hospitals. HFs in rural settings (β = 2.60, 95% CI: 0.62, 4.58), mountain belts (β = 4.18, 95% CI: 1.65, 6.71), and HFs with external supervision (β = 2.99, 95% CI:1.08, 4.89), and quality assurance activities (β = 3.59, 95% CI:1.64, 5.54) had better readiness scores. Conclusion: The availability of all seven BEmONC signal functions and readiness of HFs for BEmONC services are relatively low in local HFs and private hospitals. Accelerating capacity development through training centers at the federal/provincial level, onsite coaching, and mentoring, improving procurement and supply of medicines through federal/provincial logistic management centers, and regular supportive supervision could improve the BEmONC service availability and readiness in facilities across the country. [ABSTRACT FROM AUTHOR]

When effect modifiers influence the decision to participate in randomized trials, generalizing causal effect estimates to an external target population requires the knowledge of two scores - the propensity score for receiving treatment and the sampling score for trial participation. While the former score is known due to randomization, the latter score is usually unknown and estimated from data. Under unconfounded trial participation, we characterize the asymptotic efficiency bounds for estimating two causal estimands - the population average treatment effect and the average treatment effect among the non-participants - and examine the role of the scores. We also study semiparametric efficient estimators that directly balance the weighted trial sample toward the target population, and illustrate their operating characteristics via simulations. [ABSTRACT FROM AUTHOR]

In the context of an increasingly interconnected global economy, deciphering the complex ripple effects of external financial disruptions on national economies is a task of utmost significance. This article dives deep into the intricate repercussions of such disturbances on the macroeconomic dynamics of China using the example of the potential insolvency of a Silicon Valley bank. Grounded in empirical scrutiny, we leverage data spanning from Q1 2000 to Q1 2022 and the analytical utility of the impulse response function to illuminate our findings. We find that external financial tumult triggers a global recession, adversely impacting China's export-driven economy while simultaneously unsettling aggregate output, employment levels, and wage stability. Simultaneously, these disruptions induce variability in consumption tendencies, investment trajectories, and import volumes and inject instability into interest rate paradigms. We also acknowledge the potential for currency depreciation and bank insolvency incidents to induce inflationary stresses, primarily by escalating the costs of imports. However, these inflationary tendencies may be offset by the concomitant economic slowdown and diminished demand inherent to global recessions. Importantly, the tightening of global credit conditions, coupled with existing financial ambiguities, may obstruct investment initiatives, curtail imports, and exert influence on both risk-free and lending interest rates. Our investigation also probes into the response of the Chinese government's monetary policy to these external financial shocks. Despite the vital role of monetary policy in alleviating the impacts of these shocks, the potential secondary effects on China's domestic economy warrant attention. Our study underscores the imperative of proper policy design rooted in a profound understanding of the intricate economic interdependencies for effective management and mitigation of the potentially detrimental consequences of such financial upheavals on China's macroeconomic resilience within the tapestry of a tightly knit global financial ecosystem. [ABSTRACT FROM AUTHOR]

The external validity of causal findings is a focus of long-standing debates in the social sciences. Although the issue has been extensively studied at the conceptual level, in practice few empirical studies include an explicit analysis that is directed toward externally valid inferences. In this article, we make three contributions to improve empirical approaches for external validity. First, we propose a formal framework that encompasses four dimensions of external validity: $ X $ -, $ T $ -, $ Y $ -, and C-validity (populations, treatments, outcomes, and contexts). The proposed framework synthesizes diverse external validity concerns. We then distinguish two goals of generalization. To conduct effect-generalization —generalizing the magnitude of causal effects—we introduce three estimators of the target population causal effects. For sign-generalization —generalizing the direction of causal effects—we propose a novel multiple-testing procedure under weaker assumptions. We illustrate our methods through field, survey, and lab experiments as well as observational studies. [ABSTRACT FROM AUTHOR]

Real‐world data (RWD)‐derived external controls can be used to contextualize efficacy findings for investigational therapies evaluated in uncontrolled trials. As the number of submissions to regulatory and health technology assessment (HTA) bodies using external controls rises, and in light of recent regulatory and HTA guidance on the appropriate use of RWD, there is a need to address the operational and methodological challenges impeding the quality of real‐world evidence (RWE) generation and the consistency in evaluation of RWE across agencies. This systematic review summarizes publicly available information on the use of external controls to contextualize outcomes from uncontrolled trials for all indications from January 1, 2015, through August 20, 2021, that were submitted to the European Medicines Agency, the US Food and Drug Administration, and/or select major HTA bodies (National Institute for Health and Care Excellence (NICE), Haute Autorité de Santé (HAS), Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG), and Gemeinsamer Bundesausschuss (G‐BA)). By systematically reviewing submissions to regulatory and HTA bodies in the context of recent guidance, this study provides quantitative and qualitative insights into how external control design and analytic choices may be viewed by different agencies in practice. The primary operational and methodological aspects identified for discussion include, but are not limited to, engagement of regulators and HTA bodies, approaches to handling missing data (a component of data quality), and selection of real‐world endpoints. Continued collaboration and guidance to address these and other aspects will inform and assist stakeholders attempting to generate evidence using external controls. [ABSTRACT FROM AUTHOR]

The assessment of muscle mass and physical performance is essential for the diagnosis of sarcopenia. This study examined the validity of bioimpedance analysis (BIA) and a sensor-based short physical performance battery (SPPB) device for analyzing appendicular skeletal muscle mass (ASM) and physical performance. Forty-one older adults were measured for ASM and physical performance with two BIA devices (InBody770 vs. T-SCAN PLUS III) and two SPPB devices (manual mSPPB vs. sensor-based sSPPB). Validity statistics included the intraclass correlation coefficient (ICC) and Bland–Altman plots to examine the agreement of data from the BIA (InBody770 vs. T-SCAN PLUS III) and the SPPBs (mSPPB vs. sSPPB). There was a significant ICC for skeletal muscle mass between the T-SCAN PLUS III and InBody770 devices (ICC = 0.8822; p < 0.0001). The mSPPB and sSPPB values showed agreement across all components: 0.8654 for the total scores, 0.8879 for the walking speed, 0.8889 for the chair stand, and 0.6863 for the standing balance. No systemic bias was observed between the two methods for the BIA and SPPB devices. Measurements using the T-SCAN PLUS III and sSPPB seem to be highly correlated with the InBody770 and mSPPB devices in older adults and may be valid for assessing muscle mass and physical performance. [ABSTRACT FROM AUTHOR]

As the regulatory environment becomes progressively receptive toward utilizing real-world evidence, a spectrum of real-world data incorporation techniques in trial conduct and analysis has seen increasing interest and adoption in different stages of drug development. Of particular interest is leveraging external control data to augment the control arm in a concurrent randomized controlled trial, where patients are enrolled in both investigational treatment arm and the control arm. Yet despite the emerging literature in external data borrowing in a hybrid trial setting, very little discussion focuses on delineating what should be matched and what is actually being estimated, especially when a variety of matching schemes can be considered. In general, external control can be matched in four different ways: (1) matching with the intersection between investigational treatment and concurrent control, (2) matching with the union of concurrent investigational treatment and concurrent control, (3) matching with concurrent control alone, and (4) matching with investigational treatment alone. In this article, the formulation of estimands for different matching schemes are detailed to describe what these matching methods facilitate to answer. Simulation studies are also conducted to evaluate the performance characteristics under different matching schemes, estimation methods, effect size assumptions, and missingness of confounders. [ABSTRACT FROM AUTHOR]