Explore the vast range of titles available.

Power analysis is a key component for planning prospective studies such as clinical trials. However, some journals in biomedical and psychosocial sciences ask for power analysis for data already collected and analysed before accepting manuscripts for publication. In this report, post hoc power analysis for retrospective studies is examined and the informativeness of understanding the power for detecting significant effects of the results analysed, using the same data on which the power analysis is based, is scrutinised. Monte Carlo simulation is used to investigate the performance of posthoc power analysis.

Comparison of groups is a common statistical test in many biomedical and psychosocial research studies. When there are more than two groups, one first performs an omnibus test for an overall difference across the groups. If this null is rejected, one then proceeds to the next step of post‐hoc pairwise group comparisons to determine sources of difference. Otherwise, one stops and declares no group difference. A common belief is that if the omnibus test is significant, there must exist at least two groups that are significantly different and vice versa. Thus, when the omnibus test is significant, but no post‐hoc between‐group comparison shows significant difference, one is bewildered at what is going on and wondering how to interpret the results. At the end of the spectrum, when the omnibus test is not significant, one wonders if all post‐hoc tests will be non‐significant as well so that stopping after a nonsignificant omnibus test will not lead to any missed opportunity of finding group difference. In this report, we investigate this perplexing phenomenon and discuss how to interpret such results.

The governor valve actuator (GVA), as the actuating mechanism of the steam turbine governing system, directly impacts production safety and economic efficiency. Its highly coupled nature leads to high-dimensional operational data, complex fault modes, and inherent opacity in diagnostic algorithms, posing significant challenges to the real-time performance, reliability, and generalizability of fault diagnosis and early warning tasks. To address these challenges in complex multi-sensor networks, this paper proposes a post-hoc and pseudo-post-hoc predictive maintenance (PPPM) framework leveraging advanced machine learning and SHapley Additive exPlanations, an XAI technology. The PPPM optimizes fault diagnosis and early warning models and provides interpretable attribution analysis to guide predictive maintenance workflows. Experimental results on the GVA fault testing platform prove the effectiveness of the proposed method. For the fault diagnosis and localization task, taking the random forest model as an example, PPPM achieves the optimization of 50% of the measurement points of the sensor network and the attribution analysis of fault localization, which improves the real-time, generality and reliability of the diagnosis model. For the warning task, PPPM carries out sensor network optimization and attribution analysis to improve the pseudo-supervised warning model through the pseudo-supervised learning method. Taking isolated forests as an example, the optimized model improves the W-F1 score by 5.997% and the AUC by 6.942%.

Long-term treatment with antipsychotic agents is indicated for patients with schizophrenia, but treatment is associated with adverse events (AEs) that contribute to medication discontinuation and nonadherence. Understanding drug safety profiles is critical to avoid unwanted side effects. Cariprazine is a potent dopamine D /D receptor partial agonist that is approved for the treatment of adults with schizophrenia (EU, US) and acute manic/mixed and depressive episodes associated with bipolar I disorder (US). Post hoc analyses were conducted to characterize the safety profile of cariprazine within the recommended 1.5-6 mg/d dose range for schizophrenia; data from 8 short- or long-term clinical trials were analyzed. In the pooled cariprazine-treated safety population (n=2048), the rate of study completion was 52.8%, with withdrawal of consent, insufficient response, and AEs the most common reasons for premature discontinuation. The most commonly reported AEs (>10%) in the overall cariprazine-treatment group were akathisia (14.6%), insomnia (14.0%), and headache (12.1%); most AEs were considered mild (71.0%) or moderate (26.5%). Most akathisia was mild/moderate (97.5%) and >93% of patients remained on treatment; akathisia events were managed by rescue medications (56.3%) or dose reduction (18.3%). The metabolic profile of cariprazine was neutral in patients with short- and long-term exposure; mean weight gain was 1 kg for overall cariprazine, with an AE of weight increased reported for 5.1%. Other AEs of special interest that occurred at >3% for overall cariprazine were extrapyramidal disorder (7.0%), sedation (3.7%), and somnolence (3.1%); prolactin elevation, cognition impairment, sexual dysfunction, suicidality, and QT prolongation occurred at ≤1%. Akathisia, the most common cariprazine-related AE, was mild/moderate and resulted in few study discontinuations; symptoms were well managed and most patients remained on treatment. Results of this analysis indicated that cariprazine in the recommended dose range was safe and generally well tolerated in patients with schizophrenia. Studies registered with ClinicalTrials.gov (NCT00404573, NCT01104779, NCT00694707, NCT01104766, NCT01104792, NCT00839852, and NCT01412060) and EudraCT (2012-005485-36).

This paper presents a theory of contrasts designed for modified Freeman–Tukey (FT) statistics which are derived through square-root transformations of observed frequencies (proportions) in contingency tables. Some modifications of the original FT statistic are necessary to allow for ANOVA-like exact decompositions of the global goodness of fit (GOF) measures. The square-root transformations have an important effect of stabilizing (equalizing) variances. The theory is then used to derive Tukey’s post-hoc pairwise comparison tests for contingency tables. Tukey’s tests are more restrictive, but are more powerful, than Scheffè’s post-hoc tests developed earlier for the analysis of contingency tables. Throughout this paper, numerical examples are given to illustrate the theory. Modified FT statistics, like other similar statistics for contingency tables, are based on a large-sample rationale. Small Monte-Carlo studies are conducted to investigate asymptotic (and non-asymptotic) behaviors of the proposed statistics.

Aims/Introduction Substantial variability in demographic and clinical characteristics exists among patients with type 2 diabetes mellitus, which may impact treatment. This post‐hoc analysis evaluated the efficacy and safety of imeglimin 1,000 mg twice daily (BID) monotherapy in type 2 diabetes mellitus patients according to demographic and clinical characteristics. Materials and Methods Data were pooled from two placebo‐controlled, 24 week, randomized, double‐blind studies in adults with type 2 diabetes mellitus. Outcomes (least squares mean [LSM] change in HbA1c from baseline to week 24, and safety) were analyzed according to subgroups based on demographics, clinical characteristics, and comorbidities. Results The difference in LSM change in HbA1c from baseline to week 24 was statistically significant for imeglimin vs placebo in all patient subgroups analyzed (P < 0.05 each), including demographics (age, body mass index), clinical characteristics (duration of type 2 diabetes mellitus, chronic kidney disease [CKD] stage, and prior medication use) and comorbidities (hypertension, dyslipidemia, risk of hepatic fibrosis and liver function parameter status). A statistically significant separation from placebo in HbA1c was observed at week 4 and maintained through week 24. No new safety concerns were identified with imeglimin in any patient subpopulations. Conclusions The efficacy and safety of imeglimin was demonstrated across patient subgroups, irrespective of baseline demographic and clinical characteristics. Our findings confirm the efficacy and safety of imeglimin across a broad spectrum of patients with type 2 diabetes mellitus. Substantial variability in the demographic and clinical characteristics exists among patients with type 2 diabetes mellitus, which may impact treatment. In this post‐hoc analysis, the efficacy of imeglimin was demonstrated across patient subgroups irrespective of baseline demographic and clinical characteristics, with a statistically significant separation from placebo in HbA1c observed at week 4 and maintained through week 24. No new safety concerns were identified with imeglimin in any patient subpopulations.

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.

Digital process data are becoming increasingly important for social science research, but their quality has been gravely neglected so far. In this article, we adopt a process perspective and argue that data extracted from socio-technical systems are, in principle, subject to the same error-inducing mechanisms as traditional forms of social science data, namely biases that arise before their acquisition (observational design), during their acquisition (data generation), and after their acquisition (data processing). As the lack of access and insight into the actual processes of data production renders key traditional mechanisms of quality assurance largely impossible, it is essential to identify data quality problems in the data available—that is, to focus on the possibilities post-hoc quality assessment offers to us. We advance a post-hoc strategy of data quality assurance, integrating simulation and explorative identification techniques. As a use case, we illustrate this approach with the example of bot activity and the effects this phenomenon can have on digital process data. First, we employ agent-based modelling to simulate datasets containing these data problems. Subsequently, we demonstrate the possibilities and challenges of post-hoc control by mobilizing geometric data analysis, an exemplary technique for identifying data quality issues.

Evocalcet is a novel oral calcimimetic drug that has demonstrated similar efficacy to cinacalcet in regulating serum parathyroid hormone (PTH), calcium, and phosphate levels, with fewer upper gastrointestinal tract-related adverse drug reactions (ADRs) in patients with secondary hyperparathyroidism undergoing hemodialysis in Japan. We investigated the efficacy and safety of once-daily oral evocalcet under different dialysate calcium concentrations. A post hoc analysis by dialysate calcium concentration (2.5, 2.75, and 3.0 mEq/L) was performed using data from a previous Phase 3 study that included cinacalcet as an active control. Efficacy endpoints were the proportion of patients who achieved the target intact PTH levels of ≥60 and ≤240 pg/mL between Week 28 and Week 30; time-course changes in serum intact PTH; calcium and phosphorus levels, bone turnover markers, and fibroblast growth factor 23 (FGF23) over the 30-week study period. Safety endpoints were overall ADRs and hypocalcemia- and upper gastrointestinal tract-related ADRs. A total of 634 patients were included in the analysis. Levels of intact PTH, calcium, phosphate, bone turnover markers, and FGF23 showed improvement in all sub-groups, irrespective of dialysate calcium concentration. The incidence of upper gastrointestinal tract-related ADRs was significantly lower in the evocalcet group than the cinacalcet group with dialysate calcium concentrations of 2.75 and 3.0 mEq/L ( <0.05 for both concentrations). Evocalcet was effective and safe in regulating the levels of serum intact PTH, calcium, and phosphate in patients with secondary hyperparathyroidism undergoing hemodialysis, irrespective of dialysate calcium concentration.

Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. The Local Interpretable Model-agnostic Explanations (LIME) and Shaply Additive exPlanation (SHAP) frameworks have grown as popular interpretive tools for ML and DL models. This article provides a systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer’s disease (AD). Adhering to PRISMA and Kitchenham’s guidelines, we identified 23 relevant articles and investigated these frameworks’ prospective capabilities, benefits, and challenges in depth. The results emphasise XAI’s crucial role in strengthening the trustworthiness of AI-based AD predictions. This review aims to provide fundamental capabilities of LIME and SHAP XAI frameworks in enhancing fidelity within clinical decision support systems for AD prognosis.