Sample Size Estimation Research Articles

A Situational Analysis and an Untapped Opportunity for Tackling Challenges Associated with Coverage of Tuberculosis Preventive Treatment: A Multi-Centric Study in India.

To assesses the coverage, adherence, reasons for non-initiation and non-completion of tuberculosis preventive treatment (TPT) among household child contacts (HHCC) of pulmonary tuberculosis (TB). This cross-sectional study was conducted across eight sites in India. Estimated sample size was 200 per site. Information was collected through record review and house-to-house visits of HHCCs of notified pulmonary TB cases during January to March 2022. Coverage and adherence was assessed by proportion of eligible HHCC initiated and completed TPT, respectively. Of 2554 HHCCs eligible for TPT, initiation and completion rate was 34% and 22%, respectively. Across the sites the median time to conduct home visit was 14 d (IQR 9, 22) and TPT initiation was 7 d (IQR 1, 21). Reasons for the non-initiation were no information provided by paramedical workers (82%), information provided by paramedical workers but TPT was not given (19%), parents felt it's not important (9%), and fear of side-effects (3%). Reasons for non-completion were: TPT received for less than six months (from healthcare providers) (54%), advised for the lesser duration TPT by the doctors (4%), parents felt completion was not important (32%), parents' fear of side-effects or myth (5%), and HHCC complained of side-effect (0.7%). Inadequate emphasis on home visits leads to TPT initiation in only one-third and completion in less than one-fourth of eligible HHCCs. This poor coverage was primarily due to the health system related issues. Rarely reported TPT side-effects highlighted its safety.

A phase II study of neratinib (Nera) in combination with chemotherapy/trastuzumab/pembrolizumab (C/T/P) in HER2 overexpressing gastroesophageal cancers (GECs).

TPS514 Background: Human epidermal growth factor receptor 2 (HER2) is overexpressed in about 15-20% of GECs and is tested using immunohistochemistry and/or in-situ hybridization. Per 3 rd interim analysis of KN-811 study, addition of P to C/T improved overall response rate (ORR) and overall survival (OS) in patients with HER2 positive tumors. HER2 heterogeneity and activation of other signaling pathways ultimately leading to T resistance limits its potential to have a durable response. Nera is an anilinoqiunoline derivative intracellular oral kinase inhibitor that irreversibly binds to epidermal growth factor receptor (EGFR), HER2 and HER4. In this study, we hypothesize that targeting other oncogenic pathways with Nera may help derive additional therapeutic benefit in HER2 positive GECs when combined with C/T/P. Methods: This is an open-label, single arm, multi-institutional Phase II study of Nera in combination with C/T/P in HER2 overexpressing GECs as 1 st line treatment. The first 6 pts as a lead-in phase will be evaluated for toxicity. If no dose limiting toxicity (DLT), a total of 30 patients (include the first 6pts in the lead-in phase) will be used to evaluate efficacy in the phase II portion. All pts will receive standard dose mFOLFOX/trastuzumab q2w along with pembrolizumab 400 mg q6w. Nera will be dosed at 240 mg daily. In case of DLTs observed with this dose, a dose of 160 mg/day for Nera will be considered. Estimated sample size is 30-36 pts using Simon 2 stage design. Primary endpoint in ORR. The null hypothesis will be rejected if 26 or more responses are observed in 30 patients. Key inclusion criteria include: HER2 positive GECs (as determined by local testing), treatment naïve for stage IV disease, measurable disease, ECOG 0-1, preserved cardiac function. Uncontrolled brain metastases or impaired organ function that poses risk with treatment are key exclusion criteria. Primary endpoint is ORR and secondary endpoints include safety, duration of response, clinical benefit rate and OS. The trial is registered on clinicaltrials.gov under NCT06109467. Clinical trial information: NCT06109467 .

A new web application for determining sample size in freedom-from-disease testing with imperfect tests

Veterinary surveillance frequently requires study design for freedom-from-disease testing, specifying a sample size to balance higher statistical power with larger sample sizes against increased research and ethics costs, with the recognition that tests can generate false positive and negative results: i.e., tests exhibit imperfect sensitivity and specificity. In this paper, we revisit the mathematics behind exact calculations of sample size in terms of the binomial and hypergeometric distributions, and present a new algorithm – implemented and available to use in R as a Shiny application with a graphical user interface – to determine sample size for practical situations. Often, sample size calculations are based upon simulations or approximations, but we show here that exact calculations are feasible. In addition, we relax the liberal assumption – which provides conservative sample-size estimates – that sensitivity and specificity are known exactly, and instead assume both are Beta distributed with known hyperparameters. The application presented here was originally designed as a learning tool for students and is now made available for wider use.

Body image victimization and eating distress in young adult females

Abstract Context: Eating distress syndrome (EDS) is characterized by distressing and conflicting thoughts about the body shape and eating behaviors accompanied by self-imposition of strict dietary and/or exercise regimes in the absence of any significant weight loss. EDS has a prevalence of nearly 11% and is associated with psychological distress, internalizing and externalizing problems, lesser well-being, and reduced quality of life. Aims: The study aimed to assess the relationship between eating-related messages and body image victimization with EDS in young adult females. Settings and Design: Purposive sampling was employed to approach an estimated sample size of 151 young adults (18–26 years). The data were collected online using e-consent through social media platforms such as Facebook, Twitter, and Instagram through Google Form (GF). Materials and Methods: There were a total of 403 respondents to GF comprising of Caregiver Eating Messages Scale, Body Image Victimization Experience Scale, and Eating Disorder Examination-Questionnaire Short. A total of 151 participants were recruited from 403 as per defined criteria. Statistical Analysis Used: Descriptive statistics, correlation, and regression analysis were used to tests the findings. Results: Restrictive or critical messages had a positive relationship and accounted 12% chance of occurrence of EDS. The frequency of experiences of body image victimization from peers (6%) had a greater predictive value than that from the parents (3%). Similarly, the impact of body image victimization by peers (9%) contributed more than that from the parents (4%) in the occurrence of EDS. Conclusion: Remarks or comments related to eating and appearance tend to determine how an individual perceived oneself and the course of one’s food habits or choices.

Impact of interobserver variability of echocardiography derived clinical metrics on sample size estimation in clinical trials

Abstract Introduction Clinical metrics obtained from echocardiography usually involve manual analysis, where annotation introduces variability and bias, reducing the reliability of the findings [1]. Traditionally, sample size estimations consider patient variability but often overlook interobserver variability, leading to underpowered and suboptimal studies and an increased risk of clinical study failure [2]. Purpose This study aims to assess the power reduction due to a neglected error sourced in the interobserver variability and its impact on clinical trial size. Methods Interobserver variability was evaluated in an in-house cohort (median age 72, IQR: 52-77; predominantly male, 70%) by recruiting a set of 35 raters, considerably larger than previous studies. Ventricular and atrial endocardium were delineated at end-diastole (ED) and end-systole (ES) in apical 4-chamber (A4C) and 2-chamber (A2C) views, totalling 120 independent annotations per rater. Standard clinical metrics were accordingly derived, and interobserver variability was calculated as the average of the raters’ standard deviations across patients. Population variability was estimated from this cohort and from SHaRe (hypertrophic cardiomyopathy with systolic dysfunction) and Miyazaki (myocardial infarction) registries [3,4]. Sample sizes were estimated using power analysis (two-sided alternative hypothesis assumption) for an independent two-sample t-test, considering a pooled standard deviation combining population and raters’ variability. Results The sample size required for all the evaluated metrics to maintain a given power level after adjusting for the interobserver variability was significantly higher (52.7% average increase). This increase was up to 3-fold for left ventricular ejection fraction. Likewise, the adjusted sample size doubled when considering population variability from the SHaRe and Miyazaki cohorts (Table 1). In the cases where the estimated increase was relatively small (e.g. 5% for left atrium volume), the absolute number of additional patients was still substantial, around several hundred, depending on the expected effect size. The sample size increases exponentially with the inverse of the effect size, and the proportional increase in the adjusted sample size is roughly the same for a wide range of effects (Figure 1). Therefore, reducing the interobserver variability has a major impact on the sample size for any expected effect. Only when the interobserver variability relative to the population one falls below 25% can the effect of the interobserver variability be neglected in the trial size calculations. Conclusion This study evaluates the impact of interobserver variability on trial size estimation. The findings highlight the importance of this source of variability and the elevated risk of underpowered studies when neglected. This ultimately argues for a swift towards automated echocardiography analysis to reduce interobserver variability.

TAD-SIE: sample size estimation for clinical randomized controlled trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator

BackgroundPhase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs). However, 30–40% of these trials fail mainly because such studies have inadequate sample sizes, stemming from the inability to obtain accurate initial estimates of average treatment effect parameters.MethodsTo remove this obstacle from the drug development cycle, we present a new algorithm called Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator (TAD-SIE) that powers a parallel-group trial, a standard RCT design, by leveraging a state-of-the-art hypothesis testing strategy and a novel trend-adaptive design (TAD). Specifically, TAD-SIE uses synthetic intervention (SI) to estimate individual treatment effects and thereby simulate a cross-over design, which makes it easier for a trial to reach target power within trial constraints (e.g., sample size limits). To estimate sample sizes, TAD-SIE implements a new TAD tailored to SI given that using it violates assumptions under standard TADs. In addition, our TAD overcomes the ineffectiveness of standard TADs by allowing sample sizes to be increased across iterations without any condition while controlling significance level with futility stopping. Our TAD also introduces a hyperparameter that enables trial designers to trade off between accuracy and efficiency (sample size and number of iterations) of the solution.ResultsOn a real-world Phase-3 clinical RCT (i.e., a two-arm parallel-group superiority trial with an equal number of subjects per arm), TAD-SIE obtains operating points ranging between 63% to 84% power and 3% to 6% significance level in contrast to baseline algorithms that get at best 49% power and 6% significance level.ConclusionTAD-SIE is a superior TAD that can be used to reach typical target operating points but only for trials with rapidly measurable primary outcomes due to its sequential nature. The framework is useful to practitioners interested in leveraging the SI algorithm for their study design.

Associations between CAG repeat size, brain and spinal cord volume loss, and motor symptoms in spinocerebellar ataxia type 3: a cohort study

BackgroundSpinocerebellar ataxia type 3 (SCA3) is a hereditary disease caused by abnormally expanded CAG repeats in the ATXN3 gene. The study aimed to identify potential biomarkers for assessing therapeutic efficacy by investigating the associations between expanded CAG repeat size, brain and spinal cord volume loss, and motor functions in patients with SCA3.MethodsIn this prospective, cross-observational study, we analyzed 3D T1-weighted MRIs from 92 patients with SCA3 and 42 healthy controls using voxel-based morphometry and region of interest approaches. Associations between expanded CAG repeat size, brain and spinal cord volume loss, and International Cooperative Ataxia Rating Scale (ICARS) scores were investigated using partial correlation and mediation analyses. Sample sizes of potential biomarkers were calculated.ResultsCompared with healthy controls, SCA3 patients had lower cerebellar volume and cervical spinal cord area. SCA3 patients evolved along a stage-independent decline that began in the cerebellum, progressed to spinal cord, brainstem, thalami, and basal ganglia, and extensive subcortex. Expanded CAG repeat size was associated with right cerebellar lobule IV volume (r = − 0.423, P < 0.001) and cervical spinal cord area (r = − 0.405, P < 0.001), and higher ICARS (r = 0.416, P < 0.001). Mediation analysis revealed an indirect effect of expanded CAG repeat size on ICARS through spinal cord. Sample sizes estimation revealed that a minimum sample size was achieved with spinal cord measures.ConclusionsOur results indicate the potential of cervical spinal cord area as a biomarker for disease progression and a minimum sample size estimation in future clinical studies of SCA3.

Identifying the Minimal Clinically Important Difference in Emotion Regulation Among Youth Using the JoyPop App: Survey Study.

The minimal clinically important difference (MCID) is an important threshold to consider when evaluating the meaningfulness of improvement following an intervention. The JoyPop app is an evidence-based smartphone app designed to improve resilience and emotion regulation. Information is needed regarding the JoyPop app's MCID among culturally diverse youth. This study aims to calculate the MCID for youth using the JoyPop app and to explore how the MCID may differ for a subset of Indigenous youth. Youth (N=36; aged 12-18 years) were recruited to use the JoyPop app for up to 4 weeks as part of a larger pilot evaluation. Results were based on measures completed after 2 weeks of app use. The MCID was calculated using emotion regulation change scores (Difficulties in Emotion Regulation-Short Form [DERS-SF]) and subjective ratings on the Global Rating of Change Scale (GRCS). This MCID calculation was completed for youth overall and separately for Indigenous youth only. A significant correlation between GRCS scores and change scores on the DERS-SF supported face validity (r=-0.37; P=.04). The MCID in emotion regulation following the use of the JoyPop app for youth overall was 2.80 on the DERS-SF. The MCID for Indigenous youth was 4.29 on the DERS-SF. In addition, most youth reported improved emotion regulation after using the JoyPop app. These MCID findings provide a meaningful threshold for improvement in emotion regulation for the JoyPop app. They provide potential effect sizes and can aid in sample size estimations for future research with the JoyPop app or e-mental health technologies in general. The difference between overall youth and Indigenous youth MCID values also highlights the importance of patient-oriented ratings of symptom improvement as well as cultural considerations when conducting intervention research and monitoring new interventions in clinical practice.

P0238 Reliability of magnetic resonance imaging indices for evaluating perianal fistulizing disease activity in paediatric Crohn’s disease

Abstract Background Progress in clinical trials and drug development for paediatric perianal fistulising CD (pfCD) depends upon accurate, reproducible imaging to assess disease activity and measure treatment response. We evaluated the inter- and intra-rater reliability of existing radiologic disease activity indices and anatomical classification systems previously identified as appropriate for assessment of paediatric pfCD.1 Methods A retrospective cohort (N=50) of magnetic resonance imaging (MRI) exams representing a full spectrum of paediatric (&amp;lt;18 years) perianal CD severity was selected by a single expert paediatric radiologist. Methods described by Zou2 were used to estimate sample size. Three separate radiologists (2 paediatric) blinded to clinical information assessed pfCD in the exams using the Magnetic Resonance Novel Index for Fistula Imaging in CD (MAGNIFI-CD), Van Assche Index (VAI), modified VAI (mVAI), Paediatric MRI based Perianal Crohn’s disease (PEMPAC) index, a visual analogue scale (VAS) of overall pfCD disease severity (range, 0-100 mm), as well as with the Park’s and St. James University Hospital anatomic classification systems. Two radiologists read all exams twice, separated by 2 weeks, and 1 radiologist read the exams once (250 total reads). Intra- and inter-rater reliability was evaluated using intraclass correlation coefficients (ICC; equivalent to weighted kappa). Precision of the ICC estimates was quantified with 95% confidence intervals (CIs) obtained using clustered bootstrapping. Degree of reliability was interpreted with benchmarks proposed by Landis and Koch3. Results Median age of children was 13.5 years (IQR 11.4-16.1), and 64% were male. Median Paediatric Crohn’s Disease Activity Index score at time of imaging was 17.5 (IQR 10-32.5). Substantial (ICC&amp;gt;0.61) inter-rater reliability was observed for MAGNIFI-CD, VAI, and mVAI (Table 1), with the highest ICCs observed for the mVAI (0.72 [0.6 to 0.79]). Moderate (ICC&amp;gt;0.41) inter-rater reliability was observed for PEMPAC, St. James University Hospital classification, and the VAS, and fair (ICC&amp;gt;0.21) inter-rater reliability was observed for Parks classification. Intra-rater reliability was nearly perfect (ICC&amp;gt;0.81) for the VAS, and substantial for all indices except for Parks classification, which had moderate (0.60 [0.44 to 0.75]) intra-rater reliability (Table 1). Conclusion Most existing multi-item MRI-based disease activity indices and the St. James Hospital classification were reliable for the assessment of paediatric pfCD in our cohort. Our preliminary results suggest that the disease activity indices may have utility to measure treatment effect in paediatric pfCD clinical trials.

Biomarkers of disease progression in progressive supranuclear palsy for use in clinical trials

Abstract Progressive supranuclear palsy is a rare neurodegenerative disease with no current disease-modifying treatments approved. Longitudinal research and clinical trials for progressive supranuclear palsy are ongoing and require reliable measures which are sensitive to disease progression. Despite susceptibility to subjective limitations clinical and cognitive assessments are the most used instruments in therapeutic trials in progressive supranuclear palsy. The objective of this review is to identify measures which have been studied longitudinally as measures of progression and which are suitable for use as clinical trial endpoints. We reviewed the measures currently used as trial endpoints, identifying the clinical, cognitive, fluid and imaging measures which have previously been studied longitudinally, and discussing current diagnostic and emerging measures which are yet to be studied longitudinally but which may be sensitive to disease progression. We found that many fluid and imaging measures require further research to validate their use as longitudinal measures of change, including emerging measures which have not yet been studied specifically in progressive supranuclear palsy. We also summarise the sample size estimates required to detect changes in a 2-arm, 52-week therapeutic trial and found that specific MRI volumes require the smallest sample sizes to detect change.

Assessing Clinical Improvement of Infants Hospitalized for Respiratory Syncytial Virus-Related Critical Illness.

Pediatric respiratory syncytial virus (RSV)-related acute lower respiratory tract infection (LRTI) commonly requires hospitalization. The Clinical Progression Scale Pediatrics (CPS-Ped) measures level of respiratory support and degree of hypoxia across a range of disease severity, but it has not been applied in infants hospitalized with severe RSV-LRTI. We analyzed data from a prospective surveillance registry of infants hospitalized for RSV-related complications across 39 U.S. PICUs from October through December 2022. We assigned CPS-Ped (0=discharged home at respiratory baseline to 8=death) at admission, days 2-7,10, and 14. We identified predictors of clinical improvement (CPS-Ped≤2 or 3-point decrease) by day 7 using multivariable log-binomial regression models and estimated the sample size (80% power) to detect 15% between-group clinical improvement with CPS-Ped versus hospital length of stay (LOS). Of 585 hospitalized infants, 138 (23.6%) received invasive mechanical ventilation (IMV). Of the 49 (8.4%) infants whose CPS-Ped score worsened by 2 points after admission, one died. Failure to clinically improve by day 7 occurred in 205 (35%) infants and was associated with age <3 months, prematurity, underlying respiratory condition, and IMV in the first 24 hours in the multivariable analysis. The estimated sample size per arm required for detecting a 15% clinical improvement in a potential study was 584 using CPS-Ped clinical improvement versus 2,031 for hospital LOS. CPS-Ped can be used to capture a range of disease severity and track clinical improvement in infants who develop RSV-related critical illness and could be useful for evaluating therapeutic interventions for RSV.

Longitudinal FDG-PET Metabolic Change Along the Lewy Body Continuum

Although 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established cross-sectional biomarker of brain metabolism in dementia with Lewy bodies (DLB), the longitudinal change in FDG-PET has not been characterized. To investigate longitudinal FDG-PET in prodromal DLB and DLB, including a subsample with autopsy data, and report estimated sample sizes for a hypothetical clinical trial in DLB. Longitudinal case-control study with mean (SD) follow-up of 3.8 (2.3) years. Cases were recruited consecutively between 2007 and 2022 at a referral center and among the population. Patients with probable DLB or mild cognitive impairment with Lewy bodies (MCI-LB) were included. Individuals without cognitive impairment were included from a population-based cohort balanced on age and sex for comparison. All participants completed at least 1 follow-up assessment by design. Patients with MCI-LB and DLB. Rate of change in FDG-PET was assessed as standardized uptake value ratios (SUVr). Clinical progression was assessed with the Clinical Dementia Rating Sum of Boxes (CDR-SB) score. Thirty-five patients with probable DLB, 37 patients with MCI-LB, and 100 individuals without cognitive impairment were included. The mean (SD) age of the DLB and MCI-LB groups combined (n = 72) was 69.6 (8.2) years; 66 patients (92%) were men and 6 (8%) were women. At follow-up, 18 participants (49%) with MCI-LB had progressed to probable DLB. Patients with MCI-LB had a faster decline in FDG-SUVr, compared with that of participants without cognitive impairment, in the posterior cingulate, occipital, parietal, temporal, and lateral frontal cortices. The same regions showed greater metabolic decline in patients with DLB than in participants without cognitive impairment, with the addition of anterior-middle cingulate, insula, and medial frontal orbital cortices. Rates of change in FDG-PET in these brain regions were combined into a region of interest (ROI) labeled longitudinal FDG-PET LB meta-ROI. The rate of change in FDG-SUVr in the meta-ROI correlated with the rate of change in CDR-SB, and sample size estimates were reported for potential clinical trials in DLB. Findings were confirmed in the subsample with neuropathologic confirmation (n = 20). This study found that brain hypometabolism begins to evolve during the prodromal stages of DLB with changes paralleling symptomatic progression. These data may inform clinical practice and trials planning to use FDG-PET for biologic staging, monitoring disease progression, and potentially assessing treatment response.

Sample size estimation for task-related functional MRI studies using Bayesian updating.

Task-related functional MRI (fMRI) studies need to be properly powered with an adequate sample size to reliably detect effects of interest. But for most fMRI studies, it is not straightforward to determine a proper sample size using power calculations based on published effect sizes. Here, we present an alternative approach of sample size estimation with empirical Bayesian updating. First, this method provides an estimate of the required sample size using existing data from a similar task and similar region of interest. Using this estimate researchers can plan their research project, and report empirically determined sample size estimations in their research proposal or pre-registration. Second, researchers can expand the sample size estimations with new data. We illustrate this approach using four existing fMRI data sets where Cohen's d is the effect size of interest for the hemodynamic response in the task condition of interest versus a control condition, and where a Pearson correlation between task effect and age is the covariate of interest. We show that sample sizes to reliably detect effects differ between various tasks and regions of interest. We provide an R package to allow researchers to use Bayesian updating with other task-related fMRI studies.

Statistical analysis of clinical trial data in cancer research

Statistical methods are fundamental to designing and executing cancer clinical trials, which present unique challenges such as patient diversity, incomplete data, and ethical considerations. This article underscores the pivotal role of advanced statistical techniques in ensuring reliable outcomes, with a focus on sample size estimation, randomization, and endpoint selection. Key methodologies discussed include Cox regression, Bayesian modeling, and machine learning applications for predictive analytics and real-time data processing. Practical solutions to challenges like treatment effect assessment, bias reduction, and ethical compliance are highlighted through real-world examples. The paper concludes by envisioning future innovations that enhance accuracy, efficiency, and accessibility in cancer research. By integrating rigorous statistical methods with clinical relevance, this work aims to propel oncology into a new era of precision and patient-centered care.

Prevalence and Patterns of Non-Doctor Prescription Practices in Urban Field Practice Area of Dharwad

Background: The dearth of quality of care in many tribal and remote rural areas of the country has led to non-doctor prescription practices. Non-qualified personnel prescribing can result in drug resistance and wrong use of drugs. Objective: To estimate the prevalence and patterns of non-doctor prescription practices in Urban field practice area of SDMCMSH, Dharwad Methods: A cross sectional study was conducted among adults of urban field practice area, SDMCMSH, Dharwad. An estimated sample size of 335 was selected using systematic random sampling. All eligible subjects were interviewed through pre-tested questionnaire. Results: The prevalence of non-doctor prescription practices was 42 (12.5%). Herbal practitioners 16 (38.09%) and nurses 12 (28.57%) were major non-doctors prescribing in community. Faith 13 (30.95%) and easy accessibility 7 (16.66%) were the reasons for consulting non-doctor. Conclusion: Around one in ten adults seek non-doctor for consultation. Herbal remedies were also prescribed along with allopathic medicine. Non-accessibility and non-availability of qualified doctors can increase this practice in near future.

Thromboembolic Events in the Hemodialysis Setting: Understanding Risk Profiles and Cumulative Incidences to Inform Clinical Trial Design.

People with kidney failure have a high risk of cardiovascular morbidity/death, including thromboembolic events. Factor XIa inhibitors are a new class of anticoagulants in development that may offer antithrombotic benefits with a lower risk of incremental bleeding events than traditional therapies. We investigated major adverse vascular events (MAVEs), a relevant composite outcome for testing novel antithrombotic agents, in a large cohort of patients on hemodialysis, to better understand the key requirements to adequately design a phase 3 trial. We included 25 211 patients on hemodialysis for >90 days in phases 4 to 7 (2009-2021) of the DOPPS (Dialysis Outcomes and Practice Patterns Study). Atherosclerotic cardiovascular disease (ASCVD) was defined as history/presence of coronary, peripheral, or cerebrovascular disease. We estimated MAVE rates and cumulative incidence, overall and by ASCVD. Over half (52%) of the cohort met the ASCVD criteria. The MAVE hospitalization/death composite rate (per 100 patient-years) was 6.0 in the overall cohort and 8.7 in the ASCVD subset. Three-year cumulative incidence of MAVE was 13% in the overall cohort and 18% in the ASCVD subset. The estimated sample size to be randomized in a hypothetical trial in the ASCVD population was ≈7000 patients. Even in the enriched ASCVD group, the observed MAVE incidence combined with a high competing risk, regulatory requirements (α=0.01), and limited recruitment pool makes feasibility of a potential randomized trial targeting MAVE reduction challenging. These results highlight key considerations and challenges for developers of novel therapies targeting systemic thromboembolic events in patients on hemodialysis.

EXAMINING THE IMPACT OF ARTIFICIAL INTELLIGENCE IN DENTISTRY: A COMPREHENSIVE SYSTEMATIC REVIEW

Background: Artificial Intelligence (AI) in dentistry has the potential to revolutionize oral healthcare by solving its inherent shortcomings. Aim: To review and evaluate the body of research on artificial intelligence's use in dentistry, with a focus on how it affects treatment planning, diagnosis, and patient care in a range of dental specialties. Methodology: 30 papers encompassing oral diagnosis, surgery, endodontics, prosthodontics, orthodontics, forensic dentistry, radiography, and periodontics are thoroughly examined in this review using PRISMA guidelines. The Cochrane Handbook principles were followed in the evaluation of important variables such as randomization, blinding, withdrawal/dropout rates, sample size estimation, clarity of inclusion/exclusion criteria, examiner reliability testing, pre-specification of outcomes, and bias risk. The Newcastle-Ottawa Scale (NOS) was used in quality assessment to measure bias risk and star ratings. Results: The research highlight improvements in diagnosis, treatment planning, and procedural accuracy, illustrating the revolutionary effects of AI in dentistry. Applications of AI demonstrate its versatility and include automated designs, risk prediction, lesion recognition, and precision in dental operations. There is little chance of bias in randomization, intervention variations, and outcome assessments, according to the methodological evaluation, which shows excellent scientific rigor. Even though a few studies had minor issues including uneven blinding and missing data, these had no appreciable impact on the dependability of the results. Overall, the studies' consistent methodological quality highlights how AI may be relied upon to advance dental research and practice.

A survey of knowledge and attitudes towards antibiotic use and resistance among teachers in the Republic of Kenya: Implications for using teachers in raising public awareness of rational antibiotic use in school communities

Background Antimicrobial resistance is a significant public health threat. In Kenya, schools are targeted for public education to promote knowledge and attitudes towards the proper use of antimicrobials. However, there are limited studies that have investigated teachers’ knowledge and attitudes on antibiotic use. Methods We conducted an online survey from December 2023 to January 2024 which included 608 primary and secondary school teachers, representing a response rate of 93% of the estimated sample size of 653 teachers. Data on the respondents’ antibiotic knowledge score (seventeen questions) and attitude score (eleven questions) were analysed using IBM SPSS (Version 27). A binary logistic regression model was applied to analyze predicators of adequate knowledge and attitude on antibiotics. Results The average knowledge score for antibiotic use was 9.2 out of 17. Among the sampled teachers, 82% had not received public awareness information on proper antibiotic use. More than 86% of the respondents incorrectly answered that antibiotics are effective against colds. The average attitude score on antibiotic use was 6.1 out of 11. A quarter of the interviewees agreed that they gave family members antibiotics wherever they fell sick. Respondents aged 40–49 years and teaching in secondary school had higher odds for adequate knowledge in antibiotic use. Conclusions This study has identified significant knowledge and attitude gaps that need to be addressed by policy makers to ensure rational antibiotics use among teachers and in ensuring effective use of teachers in raising awareness in school communities for rational antibiotic use.

The use of sequential multiple assignment randomized trials (SMARTs) in physical activity interventions: a systematic review

BackgroundPhysical activity (PA) is often the cornerstone in risk-reduction interventions for the prevention and treatment of many chronic health conditions. PA interventions are inherently multi-dimensional and complex in nature. Thus, study designs used in the evaluation of PA interventions must be adaptive to intervention components and individual capacities. A Sequential Multiple Assignment Randomised Trial (SMART) is a factorial design in a sequential setting used to build effective adaptive interventions. SMARTs represent a relatively new design for PA intervention research. This systematic review aims to examine the state-of-the-art of SMARTs used to develop PA interventions, with a focus on study characteristics, design, and analyses.MethodsPubMed, Embase, PsychINFO, CENTRAL, and CinAHL were systematically searched through May 2023 for studies wherein PA SMARTs were conducted. Methodological quality was assessed using the Cochrane Risk of Bias 2 Tool.ResultsTwenty studies across a variety of populations – e.g., obesity, chronic pain, and cardiovascular conditions, were included. All PA SMARTs involved two decision stages, with the majority including two initial treatment options. PA interventions most commonly consisted of individual aerobic exercise with strategies such as goal setting, wearable technology, and motivational interviewing also used to promote PA. Variation was observed across tailoring variables and timing of tailoring variables. Non-response strategies primarily involved augmenting and switching treatment options, and for responders to continue with initial treatment options. For analyses, most sample size estimations and outcome analyses accounted for the SMART aims specified. Techniques such as linear mixed models, weighted regressions, and Q-learning regression were frequently used. Risk of bias was high across the majority of included studies.ConclusionsIndividual-based aerobic exercise interventions supported by behaviour change techniques and wearable sensing technology may play a key role in the future development of SMARTs addressing PA intervention development. Clearer rationale for the selection of tailoring variables, timing of tailoring variables, and included measures is essential to advance PA SMART designs. Collaborative efforts from researchers, clinicians, and patients are needed in order to bridge the gap between adaptive research designs and personalised treatment pathways observed in clinical practice.

Power analysis for personal light exposure measurements and interventions.

Light exposure regulates the human circadian system and more widely affects health, well-being, and performance. With the rise in field studies on light exposure's effects, the amount of data collected through wearable loggers and dosimeters has also grown. These data are more complex than stationary laboratory measurements. Determining sample sizes in field studies is challenging, as the literature shows a wide range of sample sizes (between 2 and 1,887 from a recent review of the field and approaching 105 participants in first studies using large-scale 'biobank' databases). Current decisions on sample size for light exposure data collection lack a specific basis rooted in power analysis. Therefore, there is a need for clear guidance on selecting sample sizes. Here, we introduce a novel procedure based on hierarchical bootstrapping for calculating statistical power and required sample size for wearable light and optical radiation logging data and derived summary metrics, taking into account the hierarchical data structure (mixed-effects model) through stepwise resampling. Alongside this method, we publish a dataset that serves as one possible basis to perform these calculations: one week of continuous data in winter and summer, respectively, for 13 early-day shift-work participants (collected in Dortmund, Germany; lat. 51.514° N, lon. 7.468° E). Applying our method on the dataset for twelve different summary metrics (luminous exposure, geometric mean, and standard deviation, timing/time above/below threshold, mean/midpoint of darkest/brightest hours, intradaily variability) with a target comparison across winter and summer, reveals required sample sizes ranging from as few as 3 to more than 50. About half of the metrics-those that focus on the bright time of day-showed sufficient power already with the smallest sample. In contrast, metrics centered around the dark time of the day and daily patterns required higher sample sizes: mean timing of light below mel EDI of 10 lux (5), intradaily variability (17), mean of darkest 5 hours (24), and mean timing of light above mel EDI of 250 lux (45). The geometric standard deviation and the midpoint of the darkest 5 hours lacked sufficient power within the tested sample size. Our novel method provides an effective technique for estimating sample size in light exposure studies. It is specific to the used light exposure or dosimetry metric and the effect size inherent in the light exposure data at the basis of the bootstrap. Notably, the method goes beyond typical implementations of bootstrapping to appropriately address the structure of the data. It can be applied to other datasets, enabling comparisons across scenarios beyond seasonal differences and activity patterns. With an ever-growing pool of data from the emerging literature, the utility of this method will increase and provide a solid statistical basis for the selection of sample sizes.