Sample Size Considerations Research Articles

Quantitative and Computational Spinal Imaging in Neurodegenerative Conditions and Acquired Spinal Disorders: Academic Advances and Clinical Prospects

Introduction: Quantitative spinal cord imaging has facilitated the objective appraisal of spinal cord pathology in a range of neurological conditions both in the academic and clinical setting. Diverse methodological approaches have been implemented, encompassing a range of morphometric, diffusivity, susceptibility, magnetization transfer, and spectroscopy techniques. Advances have been fueled both by new MRI platforms and acquisition protocols as well as novel analysis pipelines. The quantitative evaluation of specific spinal tracts and grey matter indices has the potential to be used in diagnostic and monitoring applications. The comprehensive characterization of spinal disease burden in pre-symptomatic cohorts, in carriers of specific genetic mutations, and in conditions primarily associated with cerebral disease, has contributed important academic insights. Methods: A narrative review was conducted to examine the clinical and academic role of quantitative spinal cord imaging in a range of neurodegenerative and acquired spinal cord disorders, including hereditary spastic paraparesis, hereditary ataxias, motor neuron diseases, Huntington’s disease, and post-infectious or vascular disorders. Results: The clinical utility of specific methods, sample size considerations, academic role of spinal imaging, key radiological findings, and relevant clinical correlates are presented in each disease group. Conclusions: Quantitative spinal cord imaging studies have demonstrated the feasibility to reliably appraise structural, microstructural, diffusivity, and metabolic spinal cord alterations. Despite the notable academic advances, novel acquisition protocols and analysis pipelines are yet to be implemented in the clinical setting.

Physical activity for the elderly: systematic review and synthesis of digital health interventions

Abstract Background Physical activity (PA) is key for the well-being of the elderly. Despite its proven efficacy, many older adults remain sedentary. Digital health interventions offer promising results. The study aimed to appraise scientific literature evidence regarding digital interventions to improve physical activity levels among community-dwelling adults aged 60 years or older. Methods A systematic review and semiquantitative analysis were performed. Digital interventions were included if using eHealth/mHealth tools, app-based technologies and text messaging. An intervention control was required. Main outcomes consisted in physical activity levels and related measures. A semiquantitative approach was chosen for evidence synthesis, using a harvest plot to visually facilitate the weighing of results with sample size and quality assessment. Results A total of 10 studies (13 publications) were included, from which five main intervention types were identified (2 studies per each intervention type). A semiquantitative evidence synthesis via a harvest plot was performed, to base the studies evaluation on the simultaneous consideration of sample size, study quality by type and effectiveness of intervention. Interventions adopting dynamically-adjusted exercise plans via wearables and app-based digital peer-support resulted the most successful, whereas PA digital coaching interventions resulted the least recommendable for adoption. Motivating interventions through digital platforms and digital diaries showed inconclusive results. Conclusions Personalized interventions using dynamically-adjusted wearable-based technology and app-based digital peer-support ones resulted as the most promising. Their ability to provide real-time, personalized feedback and foster social support is key for health promotion. It is crucial to leverage these promising tools for enhancing population health, integrating them into comprehensive, equitable, sustainable and engaging public health interventions. Key messages • Interventions using personalized and dynamically-adjusted plans result as the most effective to increase physical activity amond the elderly. • It is key to integrate effective digital health tools into comprehensive, equitable, sustainable and engaging public health interventions to enhance population health.

The N-3 polyunsaturated fatty acids supplementation to prevent depression recurrence in patients with late-life depression: A 52-week double-blind, placebo-controlled trial

BackgroundReports on the efficacy of omega-3 fatty acids (n-3 PUFAs) for the treatment of late-life depression (LLD) are mixed, and most studies focus on the modification of depressive symptoms rather than depression prevention. The aim of the present study was to investigate the efficacy of n-3 PUFAs in preventing depressive recurrence in patients with late-life depression. In addition, we investigated the effects of n-3 PUFAs on changes in depressive and anxiety symptoms and inflammatory markers in LLD. MethodsA 52-week, double-blind, randomized, controlled trial was conducted. We enrolled a total of 39 euthymic patients with LLD. They were randomized to receive either n-3 PUFAs (1.2 g per day of eicosapentaenoic acid and 1 g of docosahexaenoic acid) or placebo for 52 weeks. Recurrence of depression and severity of depression symptoms were assessed at baseline and weeks 4, 8, 16, 24, 32, 40, and 52. ResultsA total of 39 patients completed the trial with 19 in the n-3 PUFAs group and 20 in the placebo group. Cox proportional hazard regression indicated that n-3 PUFAs had significant protective effect on depression recurrence (Hazard Ratio: 0.295, 95 % Confidence Interval: 0.093–0.931, p value =0.037). But n-3 PUFAs intervention had no significant effect in reducing depressive or anxiety symptoms, inflammatory markers over the placebo group. LimitationThe results should be interpreted with consideration of the modest sample size. ConclusionThese findings suggest that n-3 PUFAs may have a prophylactic effect in currently euthymic patients with LLD.

Spectral Zones-Based SHAP/LIME: Enhancing Interpretability in Spectral Deep Learning Models Through Grouped Feature Analysis.

Interpretability is just as important as accuracy when it comes to complex models, especially in the context of deep learning models. Explainable artificial intelligence (XAI) approaches have been developed to address this problem. The literature on XAI for spectroscopy mainly emphasizes independent feature analysis with limited application of zone analysis. Individual feature analysis methods, such as shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), have limitations due to their dependence on perturbations. These methods measure how AI models respond to sudden changes in the individual feature values. While they can help identify the most impactful features, the abrupt shifts introduced by replacing these values with zero or the expected ones may not accurately represent real-world scenarios. This can lead to mathematical and computational interpretations that are neither physically realistic nor intuitive to humans. Our proposed method does not rely on individual disturbances. Instead, it targets "spectral zones" to directly estimate the effect of group disturbances on a trained model. Consequently, factors such as sample size, hyperparameter selection, and other training-related considerations are not the primary focus of the XAI methods. To achieve this, we have developed a modified version of LIME and SHAP capable of performing group perturbations, enhancing explainability and realism while minimizing noise in the plots used for interpretability. Additionally, we employed an efficient approach to calculate spectral zones for complex spectra with indistinct spectral boundaries. Users can also define the zones themselves using their domain-specific knowledge.

Health Insurance Factor Analysis

This paper explores the intricacies of health insurance costs in the United States, emphasizing the Affordable Care Act's (ACA) role in reform. Employing a "health insurance factor analysis" approach, the study identifies key determinants using data visualization and regression models. Factors such as age, BMI, family size, gender, smoking status, and geographic region significantly influence insurance prices. The research evaluates various regression models and neural networks, with linear regression standing out for its high accuracy. The findings underscore the impact of smoking, medical history, and coverage level on insurance costs. Despite dataset limitations and sample size considerations, this study contributes to a nuanced understanding of health insurance pricing, aiding insurers, policymakers, and individuals in decision-making. By integrating data insights and predictive models, the research advances comprehension of the complex relationships shaping health insurance costs. The ultimate goal is to optimize insurance programs and promote accessible and affordable health insurance for all.

Sample size considerations for single-arm clinical trials with time-to-event endpoint using the gamma distribution

BackgroundTime-to-event (TTE) endpoints are evaluated as the primary endpoint in single-arm clinical trials; however, limited options are available in statistical software for sample size calculation. In single-arm trials with TTE endpoints, the non-parametric log-rank test is commonly used. Parametric options for single-arm design assume survival times follow exponential distribution or Weibull distribution. MethodsThe exponential- or Weibull-distributed survival time assumption does not always reflect hazard pattern of real-life diseases. We therefore propose gamma distribution as an alternative parametric option for designing single-arm studies with TTE endpoints. We outline a sample size calculation approach using gamma distribution with a known shape parameter and explain how to extract the gamma shape estimate from previously published resources. In addition, we conduct simulations to assess the accuracy of the extracted gamma shape parameter and to explore the impact on sample size calculation when survival time distribution is misspecified. ResultsOur simulations show that if a previously published study (sample sizes ≥ 60 and censoring proportions ≤ 20 %) reported median and inter-quartile range of survival time, we can obtain a reasonably accurate gamma shape estimate, and use it to design new studies. When true survival time is Weibull-distributed, sample size calculation could be underestimated or overestimated depending on the hazard shape. ConclusionsWe show how to use gamma distribution in designing a single-arm trial, thereby offering more options beyond the exponential and Weibull. We provide a simulation-based assessment to ensure an accurate estimation of the gamma shape and recommend caution to avoid misspecification of the underlying distribution.

Evaluating the Desirability of Outcome Ranking (DOOR) and Response Adjusted for Duration of Antibiotic Risk (RADAR) for Clinical Trials of Antibiotics in Pediatric Pneumonia.

Objective outcomes for pediatric community-acquired pneumonia (CAP) are lacking. The desirability of outcome ranking (DOOR) and response adjusted for duration of antibiotic risk (RADAR) outcome encompass clinical benefit and adverse effects, while also accounting for antibiotic exposure. We evaluated DOOR/RADAR through simulations and compared sample size considerations to non-inferiority designs in a hypothetical trial comparing antibiotics to no antibiotics (i.e., placebo) for children with mild CAP. We also evaluated a trial comparing different durations of antibiotics. Three scenarios were considered - one with no difference in DOOR between the two groups, one in which placebo is more efficacious, and another in which amoxicillin is more efficacious than placebo. Power to detect a difference between arms was greater using DOOR/RADAR compared to DOOR. Assuming a sample size of 200, DOOR had 2.5%, 50%, and 65% power to detect a statistical difference between arms for Scenarios 1-3, respectively, significantly less than DOOR/RADAR. Importantly, DOOR/RADAR incorrectly identified placebo as superior in Scenario 3 where amoxicillin was truly efficacious. Sample size requirements for non-inferiority designs were larger to achieve similar levels of power as DOOR and DOOR/RADAR. DOOR/RADAR has the potential to lead to an incorrect conclusion declaring placebo superior when amoxicillin is efficacious.

Hypothesis testing and sample size considerations for the test-negative design

The test-negative design (TND) is an observational study design to evaluate vaccine effectiveness (VE) that enrolls individuals receiving diagnostic testing for a target disease as part of routine care. VE is estimated as one minus the adjusted odds ratio of testing positive versus negative comparing vaccinated and unvaccinated patients. Although the TND is related to case–control studies, it is distinct in that the ratio of test-positive cases to test-negative controls is not typically pre-specified. For both types of studies, sparse cells are common when vaccines are highly effective. We consider the implications of these features on power for the TND. We use simulation studies to explore three hypothesis-testing procedures and associated sample size calculations for case–control and TND studies. These tests, all based on a simple logistic regression model, are a standard Wald test, a continuity-corrected Wald test, and a score test. The Wald test performs poorly in both case–control and TND when VE is high because the number of vaccinated test-positive cases can be low or zero. Continuity corrections help to stabilize the variance but induce bias. We observe superior performance with the score test as the variance is pooled under the null hypothesis of no group differences. We recommend using a score-based approach to design and analyze both case–control and TND. We propose a modification to the TND score sample size to account for additional variability in the ratio of controls over cases. This work enhances our understanding of the data generating mechanism in a test-negative design (TND) and how it is distinct from that of a case-control study due to its passive recruitment of controls.

P-228 Competence of injected MI versus MII sibling oocytes in terms of normal fertilization, blastocyst development and euploidy

Abstract Study question Is the injection of immature MI oocytes as effective as injection of their siblings mature MII oocytes regarding normal fertilization, blastocyst development and euploidy? Summary answer MI oocytes show a lower rate of fertilization, a similar blastulation rate, and a slightly lower rate of euploidy compared to their MII sibling oocytes. What is known already Although several studies have been performed on immature MI oocytes evaluating their developmental competence after intracytoplasmic sperm injection (ICSI), all have been performed on oocytes that extruded the first polar body by the time of injection. ICSI is typically performed on MII oocytes which have completed the process of maturation. Some laboratories also inject MI oocytes, nevertheless, they are generally not used due to the lack of required molecular and cellular changes to support normal development. However, there is limited research on the reproductive potential of oocytes in MI. Consequently, our current practices are founded on assumptions. Study design, size, duration This is a retrospective, observational study of 168 oocytes injected in MI and 839 sibling oocytes injected in MII from 82 ICSI cycles with Preimplantation Genetic Testing for Aneuploidies (PGT-A) performed in a single IVF clinic between April 2018 and December 2023. All PGT-A cycles included in this study contained at least one MI oocyte injected. Participants/materials, setting, methods After oocyte retrieval and denudation, the oocytes were classified as MII, MI or GV. MI oocytes with extruded polar body by the moment of ICSI were reclassified as MII. All MII and MI oocytes were inseminated and the embryos that reached blastocyst stage were biopsied on day 5, 6 or 7. Normal fertilization rate, blastocyst formation rate and euploidy rate were compared between MI-derived embryos and their MII-derived siblings. Main results and the role of chance A total of 1,094 oocytes were retrieved from 83 ICSI cycles with PGT-A, comprising 839 (76.7%) MII, 168 (15.4%) MI y 87 (7.9%) VG. The normal fertilization rate was significantly higher in oocytes injected at MII compared to MI (76.9% vs 53.0%, P < 0.001). The rates of abnormal fertilization in MII and MI oocytes were as follows: 1 PN 0.8% vs 0.6%, P = 0.342; 3PN 2.6% vs 1.2%, P = 0.075; 0 PN 19.7% vs 45.2%, P < 0.001. Interestingly, the blastulation rate of zygotes with normal fertilization was very similar between both groups (46.4% vs 43.8%, P = 0.708), as well as the proportion of biopsied blastocysts on day 5 (74.2% vs 71.8%, P = 0.858), on day 6 (23.1% vs 25.6%, P = 0.946), and on day 7 (2.7% vs 2.6%, P = 0.594). The euploidy rate of the 298 biopsied MII-derived blastocyst was 36.2%, compared with 23.1% of the 39 MI-derived blastocysts biopsied (P = 0.077). Despite the limited sample size, it is noteworthy that three out of the four euploid blastocysts derived from MI oocytes, which were transferred in single embryo transfers, successfully led to pregnancy resulting in one healthy live birth, one ongoing pregnancy and one miscarriage at 9 weeks of pregnancy. Limitations, reasons for caution While this study provides valuable insights into the developmental competence of MI oocytes, it is not exempt from certain limitations, such as its retrospective nature, limited sample size and ethical considerations regarding the injection of putative immature oocytes. Wider implications of the findings MI oocytes showed lower fertilization rates compared to their MII siblings. However, once fertilized, MI-derived embryos had similar blastulation rates and a trend towards lower euploidy rates. Our study suggests that MI oocytes may result in successful pregnancies and live birth, but further research is needed to validate these findings. Trial registration number Not applicable

Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.

Large language models (LLMs) have the potential to support promising new applications in health informatics. However, practical data on sample size considerations for fine-tuning LLMs to perform specific tasks in biomedical and health policy contexts are lacking. This study aims to evaluate sample size and sample selection techniques for fine-tuning LLMs to support improved named entity recognition (NER) for a custom data set of conflicts of interest disclosure statements. A random sample of 200 disclosure statements was prepared for annotation. All "PERSON" and "ORG" entities were identified by each of the 2 raters, and once appropriate agreement was established, the annotators independently annotated an additional 290 disclosure statements. From the 490 annotated documents, 2500 stratified random samples in different size ranges were drawn. The 2500 training set subsamples were used to fine-tune a selection of language models across 2 model architectures (Bidirectional Encoder Representations from Transformers [BERT] and Generative Pre-trained Transformer [GPT]) for improved NER, and multiple regression was used to assess the relationship between sample size (sentences), entity density (entities per sentence [EPS]), and trained model performance (F1-score). Additionally, single-predictor threshold regression models were used to evaluate the possibility of diminishing marginal returns from increased sample size or entity density. Fine-tuned models ranged in topline NER performance from F1-score=0.79 to F1-score=0.96 across architectures. Two-predictor multiple linear regression models were statistically significant with multiple R2 ranging from 0.6057 to 0.7896 (all P<.001). EPS and the number of sentences were significant predictors of F1-scores in all cases ( P<.001), except for the GPT-2_large model, where EPS was not a significant predictor (P=.184). Model thresholds indicate points of diminishing marginal return from increased training data set sample size measured by the number of sentences, with point estimates ranging from 439 sentences for RoBERTa_large to 527 sentences for GPT-2_large. Likewise, the threshold regression models indicate a diminishing marginal return for EPS with point estimates between 1.36 and 1.38. Relatively modest sample sizes can be used to fine-tune LLMs for NER tasks applied to biomedical text, and training data entity density should representatively approximate entity density in production data. Training data quality and a model architecture's intended use (text generation vs text processing or classification) may be as, or more, important as training data volume and model parameter size.

Efficacy of two brief trauma-focussed writing interventions in comparison to positive experiences writing: A randomized controlled trial

BackgroundThere is emerging literature regarding the efficacy of trauma-focussed writing therapies (TF-WTs) for posttraumatic stress. Such therapies have the potential to reduce posttraumatic stress symptoms (PTSS) in a brief time frame and can be delivered remotely. There remains a need for further research assessing the efficacy of different types of TF-WTs, as well comparing them to alternative control conditions not previously assessed. The present study assessed two TF-WTs that had differing writing instructions in comparison to an intervention that involved writing about positive experiences. MethodsAdult community participants (n = 83) with subthreshold or clinical PTSD symptoms were randomized to one of three conditions (two of which involved trauma-focussed writing, and the other involved writing about positive experiences). All conditions involved three weekly telehealth-delivered writing appointments. Outcomes were measured using the PTSD Checklist (PCL-5) and the Depression, Anxiety and Stress Scales (DASS-21), and were evaluated at baseline, one-week post-intervention, and five-weeks post-intervention. This trial was registered with the Australian and New Zealand Clinical Trials Registry (ANZCTR Protocol 12620001065987). ResultsThere was no evidence that the two TF-WTs were more efficacious in reducing PTSS or producing clinically meaningful change in comparison to positive experiences writing. Instead, a significant reduction from baseline to follow-up in PTSS, depression, anxiety and stress was observed in all three conditions. LimitationsThe results should be interpreted with consideration of the modest sample size and absence of longer-term follow-up. ConclusionsThree-session trauma-focussed writing delivered via telehealth may not be superior to writing about positive experiences.

Methods and reagent-lot comparisons by regression analysis: sample size considerations.

Parametric regression analysis is widely used in methods comparisons and more recently in checking the concordance of test results following receipt of new reagent lots. The greater frequency of reagent-lot evaluations increases pressure to detect bias with smallest possible sample sizes (i.e. smallest consumption of time and resources). This study revisits bias detection using the joint slope, intercept confidence region as an alternative to slope and intercept confidence intervals. Four cases were considered representing constant errors, proportional errors (constant CV) and two more complicated error patterns typical of immunoassays. Maximum:minimum range ratios varied from 2:1 to 2000:1. After setting a maximum tolerable difference a series of slope, intercept combinations, each of which predicted the critical difference, were systematically evaluated in simulations which determined the minimum sample size required to detect the difference, firstly using slope, intercept confidence intervals and secondly using the joint slope, intercept confidence region. At small to moderate range ratios, bias detection by joint confidence region required greatly reduced sample sizes to the extent that it should encourage reagent-lot evaluations or, alternatively, transform those already routinely performed into considerably less costly exercises. While some software is available to calculate joint confidence regions in real-life analyses, shifting this testing method into the mainstream will require a greater number of software developers incorporating the necessary code into their regression programs. The computer program used to conduct this study is freely available and can be used to model any laboratory test.

Abstract PO4-19-03: FES-PET/MRI for Tailored treatment of luminal A and lobular breast cancer: a prospective cohort study

Abstract Background: Breast cancer (BC) staging is essential to planning the most appropriate treatment pathway. Currently, BC management has become more tailored to the tumor’s and patient’s characteristics. However, BC imaging shows varying performance according to tumor subtypes. In particular, routine imaging may pose some challenges when evaluating luminal A BC (LumA) and lobular BC (Lob). LumA, due to low grade and proliferation index (&amp;lt; 20%) shows lower sensitivity on axillary US, reduced MRI enhancement, and low FDG-avidity on PET. Similarly, Lob, due to its peculiar single-cell growth pattern, exhibits inferior sensitivity on axillary MRI and FDG-avidity. LumA and Lob account for &amp;gt;50% of all BCs. Despite their favourable prognosis, metastases and recurrences still occur; this translates into a higher absolute number of events than in other subtypes. As a result, a concrete risk of disease underestimation and under-treatment exists. Two ongoing studies on FDG-PET/MRI imaging in our institution have already demonstrated that its sensitivity also decreases in LumA and Lob. Based on these premises, our hypothesis states that by combining the advantages of hybrid PET/MRI with the high accuracy of 16α -18F-fluoro-17-beta-estradiol(FES), a radiolabeled form of estrogen binding to functionally active ER, we could obtain a reliable, non-invasive, operator-independent, one-stage imaging method for staging LumA and ER-positive Lob. Trial design: This is a prospective cohort study where patients with LumA and ER-positive Lob will be enrolled in four cohorts undergoing: A) primary surgery; B) induction endocrine therapy; C) neoadjuvant chemotherapy, and D) systemic therapy for metastatic disease. FES PET/MRI examinations will be performed at baseline for local and systemic staging in all cohorts and a second exam after systemic therapy in cohorts C-D. Correlations between the FES PET/MRI parameters and pathology, gene expression, and FDG PET parameters, when available, will be investigated. Study aims will evaluate: 1) the performance of the FES PET/MRI in axillary staging compared with axillary surgery; 2) potential correlations between changes in FES uptake and changes in proliferation index after three weeks of endocrine therapy (ET); 3) the performance of the FES PET/MRI in systemic staging of patients undergoing systemic therapy in comparison with standard imaging; 4) biological determinants of tumor heterogeneity on pathological, imaging, and genomic levels. Statistics: The primary analysis in cohort A will test the sensitivity of the FES PET/MRI in detecting macrometastatic axillary nodes. Assuming that the probability that a patient has positive nodes is 33%, 119 patients (10% drop-out included) will provide 80% power to reject the null hypothesis about sensitivity at a 5% significance level if the true sensitivity is at least 50%. Results from the FES PET/MRI will be also compared with standard imaging in terms of sensitivity and number of lesions detected using the Mc-Nemar test for paired proportions and the Wilcoxon signed-rank test for paired data. The primary analysis in Cohort B will test the association between the FES-SUV change from pre-ET to post-ET and the Ki-67 change from core biopsy to post-ET in ER positive BC patients with a Ki-67 &amp;gt;10%. The association between these changes will be measured by the Pearson correlation index ρ. Α total of 52 patients (10% drop-out included) from Cohort B will provide 80% power to reject the null hypothesis about ρ at a 5% significance level if the true ρ is at least 0.4. No formal sample size was calculated for cohort C and D: results will be considered descriptive in nature, informing sample size considerations for future trials. We intent to recruit 20 and 30 patients, respectively. Accrual has not started yet. Project funded by AIRC (Associazione Italiana Ricerca sul Cancro) Next Gen Clinician Scientist ID 28378 Contact: Dr. Rosa Di Micco, dimicco.rosa@hsr.it Citation Format: Rosa Di Micco, Carla Canevari, Francesca Gallivanone, Nicole Rotmensz, Veronica Zuber, Sara Baleri, Giovanni Cisternino, Mario Rampa, Stefania Zambelli, Giulia Viale, Patrizia Zucchinelli, Manuela Morgante, Paola Scifo, Elena Venturini, Paola Magnani, Isabella Sassi, Vincenzo Bagnardi, Pietro Panizza, Giampaolo Bianchini, Arturo Chiti, Oreste Davide Gentilini. FES-PET/MRI for Tailored treatment of luminal A and lobular breast cancer: a prospective cohort study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-19-03.

Computing Power and Sample Size for the False Discovery Rate in Multiple Applications.

The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a p-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package FDRsamplesize2, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The FDRsamplesize2 package is available on CRAN.

Patient and Operational Factors Do Not Substantively Affect the Annual Departmental Quality of Anesthesiologists' Clinical Supervision and Nurse Anesthetists' Work Habits.

Although safety climate, teamwork, and other non-technical skills in operating rooms probably influence clinical outcomes, direct associations have not been shown, at least partially due to sample size considerations. We report data from a retrospective cohort of anesthesia evaluations that can simplify the design of prospective observational studies in this area.Associations between non-technical skills in anesthesia, specifically anesthesiologists' quality of clinical supervision and nurse anesthetists' work habits, and patient and operational factors were examined. Eight fiscal years of evaluations and surgical cases from one hospital were included. Clinical supervision by anesthesiologists was evaluated daily using a nine-item scale. Work habits of nurse anesthetists were evaluated daily using a six-item scale. The dependent variables for both groups of staff were binary, whether all items were given the maximum score or not. Associations were tested with patient and operational variables for the entire day. There were 40,718 evaluations of faculty anesthesiologists by trainees, 53,772 evaluations of nurse anesthetists by anesthesiologists, and 296,449 cases that raters and ratees started together. Cohen's d values were small (≤0.10) for all independent variables, suggesting a lack of any clinically meaningful association between patient and operational factors and evaluations given the maximum scores. For supervision quality, the day's count of orthopedic cases was a significant predictor of scores (P = 0.0011). However, the resulting absolute marginal change in the percentage of supervision scores equal to the maximum was only 0.8% (99% confidence interval: 0.2% to 1.4%), i.e., too small to be of clinical or managerial importance. Neurosurgical cases may have been a significant predictor of work habits (P = 0.0054). However, the resulting marginal change in the percentage of work habits scores equal to the maximum, an increase of 0.8% (99% confidence interval: 0.1% to 1.6%), which was again too small to be important. When evaluating the effect of assigning anesthesiologists and nurse anesthetists with different clinical performance quality on clinical outcomes, supervision qualityand work habits scores may be included as independent variables without concern that their effects are confounded by association with the patient or case characteristics. Clinical supervision and work habits are measures of non-technical skills. Hence, these findings suggest that non-technical performance can be judged by observing the typical small sample size of cases. Then, associations can be tested with administrative data for a far greater number of patients because there is unlikely to be a confounding association between patient and case characteristics and the clinicians' non-technical performance.

A Hybrid Pragmatic and Factorial Cluster Randomized Controlled Trial for an Anti-racist, Multilevel Intervention to Improve Mental Health Equity in High Schools

Systemic racism is pervasive in US society and disproportionately limits opportunities for education, work, and health for historically marginalized and minoritized racial and ethnic groups, making it an urgent issue of social justice. Because systemic racism is a social determinant of health prevalent across multiple social and institutional structures, it requires multilevel intervention approaches using effective designs and analytic methods to measure and evaluate outcomes. Racism is a fundamental cause of poor health outcomes, including mental health outcomes; thus, mental health services and programs that address racism and discrimination are key to promoting positive mental health of racial and ethnic minority youth. While multilevel interventions are well-suited for improving outcomes like youth mental health disparities, their evaluation poses unique methodological challenges, requiring specialized design and analytic approaches. There has been limited methodological guidance provided to researchers on how to test multilevel interventions using approaches that balance methodological rigor, practicality, and acceptability across stakeholder groups, especially within communities most affected by systemic racism. This paper addresses this gap by providing an example of how to rigorously evaluate a hypothetical, theoretically based, multilevel intervention promoting mental health equity in three US school systems using an anti-racist approach intervening at the macro- (i.e., school system), meso- (i.e., school), and micro- (i.e., family and student) levels to improve mental health in adolescents. We describe the design, sample size considerations, and analytic methods to comprehensively evaluate its effectiveness while exploring the extent to which the components interact synergistically to improve outcomes. The methodological approach proposed can be adapted to other multilevel interventions that include strategies addressing macro-, meso-, and micro-levels of influence.

Cost-Aware Generalized α-Investing for Multiple Hypothesis Testing

We consider the problem of sequential multiple hypothesis testing with nontrivial data collection costs. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes of a disease process. This work builds on the generalized α-investing framework which enables control of the marginal false discovery rate in a sequential testing setting. We make a theoretical analysis of the long term asymptotic behavior of α-wealth which motivates a consideration of sample size in the α-investing decision rule. Posing the testing process as a game with nature, we construct a decision rule that optimizes the expected α-wealth reward (ERO) and provides an optimal sample size for each test. Empirical results show that a cost-aware ERO decision rule correctly rejects more false null hypotheses than other methods for $n=1$ where n is the sample size. When the sample size is not fixed cost-aware ERO uses a prior on the null hypothesis to adaptively allocate of the sample budget to each test. We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner. Finally, empirical tests on real data sets from biological experiments show that cost-aware ERO balances the allocation of samples to an individual test against the allocation of samples across multiple tests.

Sample Size Considerations in the Design of Orthopaedic Risk-Factor Studies

Abstract Objective Sample size calculations play a central role in risk-factor study design because sample size affects study interpretability, costs, hospital resources and staff time. We demonstrate the consequences of using misclassified control groups on the power of risk association tests, with the intent of showing that control groups with even small misclassification rates can reduce the power of association tests. So, sample size calculations that ignore misclassifications may underpower studies. Study Design This was a simulation study using study designs from published orthopaedic risk-factor studies. The approach was to use their designs but simulate the data to include known proportions of misclassified affected subjects in the control group. The simulated data were used to calculate the power of a risk-association test. We calculated powers for several study designs and misclassification rates and compared them to a reference model. Results Treating unlabelled data as disease-negative only always reduced statistical power compared with the reference power, and power loss increased with increasing misclassification rate. For this study, power could be improved back to 80% by increasing the sample size by a factor of 1.1 to 1.4. Conclusion Researchers should use caution in calculating sample sizes for risk-factor studies and consider adjustments for estimated misclassification rates.

Employment of Fresh Graduates from Higher Vocational Schools (Specialty) vs. General Higher Schools (Undergraduate) in 2022 Shanghai

This study aims to explore the employment situation of fresh graduates of higher vocational schools (specialty) and general higher schools (undergraduate) in Shanghai in 2022, and to compare them from the perspectives of employment rate and average salary. In this study, the contextual factor is the epidemics impact on the economy and the labor market. By doing an online survey, the study concluded that the employment rate of fresh graduates from higher vocational schools was relatively high, while the average salary of fresh graduates from general higher schools was higher. In addition, most of the participants were satisfied with the correlation between the job they were currently engaged in and the specialty they studied in their undergraduate/specialized degree. The studys findings point to a correlation between educational background and actual salary, job specialty correlation, and job satisfaction. As a result, its possible that the studys results arent as reliable as they may be. Finally, this research sheds light on the job landscape for new Shanghai grads from both higher vocational and general higher schools in 2022, as well as the correlation between academic credentials and professional success. A deeper understanding of the problem will need more study with a bigger sample size and other considerations.

Longitudinal resting‐state EEG along the Alzheimer’s disease continuum: Sample size considerations for clinical trials.

AbstractBackgroundTo enable successful inclusion of resting‐state electroencephalography (EEG) outcome measures in Alzheimer’s disease (AD) clinical trials, we (retrospectively) mapped the progression of EEG markers over time in amyloid‐positive subjects.MethodsResting‐state 21‐channel EEG was recorded in 173 subjects positive for amyloid deposition (subjective cognitive decline, n = 16; mild cognitive impairment, n = 94; dementia due to AD, n = 63). Two or more EEG recordings were available for all subjects. We computed whole‐brain and regional relative power (i.e., theta (4‐8Hz), alpha1 (8‐10Hz), alpha2 (10‐13Hz), beta (13‐30Hz)), peak frequency, signal variability (i.e., theta Permutation Entropy) and functional connectivity values (i.e., alpha and beta corrected Amplitude Envelope Correlation, theta Phase Lag Index, weighted Symbolic Mutual Information, inverted Joint Permutation Entropy). Whole‐group linear mixed effects models were used to model the development of EEG markers over time. Group‐wise analysis was performed to investigate potential differences in change trajectories between diagnostic groups. The longitudinal relation between EEG and cognition was evaluated. Finally, we estimated the minimum sample size required to detect a stabilizing treatment effect using EEG markers, in hypothetical clinical trials of 1‐ or 2‐year duration.ResultsWhole‐group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased alpha2 and beta power), with strongest effects for temporal and parieto‐occipital regions. Signal variability showed stronger decline on a global scale. We report no significant longitudinal changes in functional connectivity strength. Disease severity at baseline influenced EEG markers’ rates of change, with fastest deterioration in MCI subjects. A decrease of the parieto‐occipital peak frequency, temporal relative alpha2 and beta power, as well as an increase in temporal relative theta power, were significantly associated with a decline in Mini Mental State Examination score. We estimate that 1‐year trials, focusing on amyloid‐positive MCI subjects, require 148 or 438 subjects per arm to detect a stabilizing treatment effect on temporal relative theta‐ or beta power, respectively.ConclusionResting‐state EEG markers can facilitate early detection of target engagement in an objective, non‐invasive, low‐cost manner. Spectral measures, particularly recorded from temporal regions, present robust secondary endpoints for AD interventions.