Required Sample Size Research Articles

Applying Exercise Capacity and Physical Activity as Single vs Composite Endpoints for Trials of Cardiac Rehabilitation Interventions: Rationale, Use-case, and a Blueprint Method for Sample Size Calculation

ObjectiveTo conceptualize a composite primary endpoint for parallel-group RCTs of exercise-based cardiac rehabilitation (CR) interventions and to explore its application and statistical efficiency. DesignWe conducted a statistical exploration of sample size requirements. We combined exercise capacity and physical activity for the composite endpoint (CE), both being directly related to reduced premature mortality in patients with cardiac diseases. Based on smallest detectable and minimal clinically important changes (change in exercise capacity of 15 W and change in physical activity of 10 min/day), the CE combines 2 dichotomous endpoints (achieved/not achieved). To examine statistical efficiency, we compared sample size requirements based on the CE to single endpoints using data from 2 completed CR trials. SettingCardiac rehabilitation phase III. ParticipantsPatients in cardiac rehabilitation. InterventionsNot applicable. Main Outcome Measure(s)Exercise capacity (Pmax assessed by incremental cycle ergometry) and physical activity (daily minutes of moderate to vigorous physical activity assessed by accelerometry). ResultsExpecting, for example, a 10% between-group difference and improvement in the clinical outcome, the CE would increase sample size by up to 21% or 61%, depending on the dataset. When expecting a 10% difference and designing an intervention with the aim of non-deterioration, the CE would allow to reduce the sample size by up to 55% or 70%. ConclusionsTrialists may consider the utility of the CE for future studies in exercise-based CR to reduce sample size requirements. However, perhaps surprisingly at first, the CE could also lead to an increased sample size needed, depending on the observed baseline proportions in the trial population and the aim of the intervention.

Is clavicular fracture associated with concomitant injuries in multi-trauma patients?

Objective: This study aimed to evaluate the relationship between clavicular fracture and associated injuries in multi-traumatic patients. Methods: In this prospective cohort study, 185 multi-traumatic patients referred to the Emergency Department of Imam Reza (AS) Hospital of Tabriz University of Medical Sciences were selected between August 2019 and September 2021. The census sampling method was used until the required sample size was achieved. The following patient information was recorded and studied: mechanism of trauma, age, gender, Glasgow Coma Scale (GCS) score, systolic blood pressure (SBP), the presence of clavicular fracture, concomitant injuries, hospital outcome, duration of hospitalization, and severity of trauma. The participants were divided into two groups based on the presence of a clavicular fracture. Data were analyzed using SPSS software. Results: Of all 185 patients, 51 had clavicular fractures. The mean age of the patients was 34.72±12.99. Of all patients, 27.6% had clavicular fractures. There was no statistically significant difference in GCS scores between the two groups (P=0.927). The highest percentage of injuries associated with clavicular traumas was traumatic brain injuries, with rates of 22.4% and 19.6% in the study and control groups, respectively. There were statistically significant differences in concomitant injuries between the two groups (P<0.001). There was no statistically significant relationship between clavicular fracture and the patient’s outcome (P=0.10), nor between clavicular fracture and GAP (GCS, age, SBP) (P=0.143) or New Trauma Score (NTS) (P=0.257). Conclusion: The results showed a difference in the associated injuries between the groups; however, there was no statistically significant difference in patient outcomes. Healthcare providers are suggested to consider concomitant injuries in patients with clavicular fractures.

Determining Sample Size Requirements in EFA Solutions: A Simple Empirical Proposal

In unrestricted or exploratory factor analysis (EFA), there is a wide range of recommendations about the size samples should be to attain correct and stable solutions. In general, however, these recommendations are either rules of thumb or based on simulation results. As it is hard to establish the extent to which a particular data set suits the conditions used in a simulation study, the advice produced by simulation studies is not direct enough to be of practical use. Instead of trying to provide general and complex recommendations, in this article, we propose to estimate the sample size that is needed to analyze a data set at hand. The estimation takes into account the specified EFA model. The proposal is based on an intensive simulation process in which the sample correlation matrix is used as a basis for generating data sets from a pseudo-population in which the parent correlation holds exactly, and the criterion for determining the size required is a threshold that quantifies the closeness between the pseudo-population and the sample reproduced correlation matrices. The simulation results suggest that the proposal works well and that the determinants identified agree with those in the literature.

Prevalence and factors associated with suicidal ideation and attempts among mentally ill patients in the psychiatry OPD at St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia

BackgroundEvery year, more than 800,000 people die due to suicide (one person every 40 s), ranking as the second leading cause of death next to traffic accidents in individuals aged 15–29 years. The objective of this study was to assess the prevalence and factors associated with suicidal ideation and attempts among mentally ill patients.MethodAn institutional-based, cross-sectional study was conducted between December 2019 and January 2020. Data were collected using a convenient sampling technique until the required sample size was achieved. The total sample size was 385. The Suicidal Behavior Questionnaire-Revised (SBQ-R), Oslo Social Support Scale, and self-prepared to assess suicide were used. Data collection was carried out by psychiatry professionals through face-to face interviews. Data were analyzed, and bivariate and multivariable logistic regression analyses were conducted using Statistical Package for the Social Sciences (SPSS) version 23.ResultA total of 385 patients participated in the study. Of these, 216 (56%) were men. The prevalence of suicide ideation was 255 (66.2%). The odds of suicidal ideation and attempt was almost three times higher [adjusted odds ratio (AOR) = 2.79, 95% confidence interval (CI) 1.11–6.98]; among patients who felt hopeless, the OR was around two times higher (AOR = 2.44, 95% CI 1.17–5.08); among patients who had a family history of suicide, the OR was almost three times higher (AOR = 2.56, 95% CI 1.00–6.53); among patients who stopped their medication, the OR was approximately two times higher (AOR = 2.25, 95% CI 1.14–4.46); the OR of suicidal ideation and attempts was almost six and four times higher (AOR = 5.86, 95% CI 1.30–26.41; and AOR = 3.61, 95% CI 1.01–12.88) among patients who were educated to primary and secondary level, respectively.ConclusionIn this study, carried out in Ethiopia, suicidal ideation and attempts were more common among men than women. The prevalence of suicidal ideation and attempts showed a significant public health issue among hospital-attending and chronic mentally ill adolescents, which requires a large emphasis. We recommend that suicide prevention needs to involve non-governmental and non-profit organizations, universities, and civil society at different levels.

Trial sequential analysis in anesthesiology and intensive care: a systematic review

Introduction: The reliability of statistical analysis and its results is becoming a focal point in scientific discourse, overshadowing concerns about statistical significance. Systematic reviews and meta-analyses are characterized by a range of serious statistical and methodological limitations, including insufficient statistical power, clinical and statistical heterogeneity, systematic errors, potential low quality of included examinations, limited data availability, and publication bias. In this context, “Trial Sequential Analysis” (TSA) emerges as a promising methodological tool used in combination with meta-analyses to enhance the precision and robustness of scientific conclusions. Objective: The study is aimed to demonstrating potential scenarios for the using TSA and its role in improving the reliability of scientific conclusions through meta-analyses published in Russian journals on anesthesiology-resuscitation, as well as the authors’ own meta-analyses.Methods: This study is a systematic review without a detailed description of the case studies. The search for meta-analyses was conducted using the keywords “meta-analysis”, “mortality”, “delirium”, “sedation”. Two researchers independently reviewed the titles and abstracts of the selected meta-analyses and extracted data. TSA was conducted for all meta-analyses using “TSA software” (Copenhagen, Denmark). Results: 6 studies were considered, and 7 TSA evaluations were performed. Six out of the seven TSA evaluations confirmed the conclusions of the pertinent meta-analyses. In one TSA, the cumulative Z-line did not reach the required sample size, highlighting the need for additional studies to definitively confirm the result. In two TSAs, the sample size in the meta-analysis was insufficient to draw definitive conclusions. The application of TSA in these cases could provide more substantiated and cautious conclusions.Conclusion: This study explores various scenarios for the application of TSA in meta-analyses published in both Russian and international journals in the fields of anesthesiology and resuscitation. The significance of TSA for enhancing the validity and clinical relevance of research findings was substantiated. Received 3 February 2024. Revised 18 March 2024. Accepted 20 March 2024. Funding: The study did not have sponsorship. Conflict of interest: The authors declare no conflict of interest. Contribution of the authorsConception and study design: M.Ya. YadgarovData collection and analysis: M.Ya. Yadgarov, L.B. Berikashvili, P.A. PolyakovStatistical analysis: M.Ya. Yadgarov, P.A. PolyakovDrafting the article: M.Ya. Yadgarov, L.B. Berikashvili, K.K. Kadantseva, A.V. Smirnova, I.V. Kuznetsov, A.A. YakovlevCritical revision of the article: V.V. LikhvantsevFinal approval of the version to be published: M.Ya. Yadgarov, L.B. Berikashvili, P.A. Polyakov, K.K. Kadantseva, A.V. Smirnova, I.V. Kuznetsov, A.A. Yakovlev, V.V. Likhvantsev

Isotonic design for single-arm biomarker stratified trials.

In single-arm trials with a predefined subgroup based on baseline biomarkers, it is often assumed that a biomarker defined subgroup, the biomarker positive subgroup, has the same or higher response to treatment compared to its complement, the biomarker negative subgroup. The goal is to determine if the treatment is effective in each of the subgroups or in the biomarker positive subgroup only or not effective at all. We propose the isotonic stratified design for this problem. The design has a joint set of decision rules for biomarker positive and negative subjects and utilizes joint estimation of response probabilities using assumed monotonicity of response between the biomarker negative and positive subgroups. The new design reduces the sample size requirement when compared to running two Simon's designs in each biomarker positive and negative. For example, the new design requires 23%-35% fewer patients than running two Simon's designs for scenarios we considered. Alternatively, the new design allows evaluating the response probability in both biomarker negative and biomarker positive subgroups using only 40% more patients needed for running Simon's design in the biomarker positive subgroup only.

Sample size planning for rank-based multiple contrast tests.

Rank methods are well-established tools for comparing two or multiple (independent) groups. Statistical planning methods for the computing the required sample size(s) to detect a specific alternative with predefined power are lacking. In the present paper, we develop numerical algorithms for sample size planning of pseudo-rank-based multiple contrast tests. We discuss the treatment effects and different ways to approximate variance parameters within the estimation scheme. We further compare pairwise with global rank methods in detail. Extensive simulation studies show that the sample size estimators are accurate. A real data example illustrates the application of themethods.

Fostering Food Security through Farmers’ Intentions to Embrace Climate-Smart Agriculture: Unraveling the Impact of Attitude, Subjective Norms, and Behavioral Control

Food security is a global challenge exacerbated by climate change, especially in lower-income countries. Promoting the implementation of climate-smart agricultural techniques holds significant importance to addressing this issue. This research investigates how farmers’ attitudes, subjective norms, and perceived behavioral control impact a certain aspect/area on their intention to adopt CSA practices, ultimately contributing to increased food security. Using the Theory of Planned Behavior framework, this study investigates how farmers’ attitudes toward CSAs, subjective norms within their social networks, and perceived ability to implement these practices influence their intentions to adopt CSAs. Data was collected through surveys and interviews with farmers in Cohen’s table to determine the required sample size. Our findings show that farmers’ attitudes towards CSA positively and significantly influence their intention to adopt climate-smart agricultural practices. Additionally, subjective norms are critical in shaping these intentions, indicating that social influence and support networks within farming communities are important in driving CSA adoption. Further, perceived behavioural control emerged as an important factor, highlighting the importance of farmers’ confidence in their ability to implement CSA practices. This research underscores the importance of encouraging positive attitudes, strengthening social networks, and building farmers’ confidence in adopting CSA practices to improve food security in lower-income countries. Policymakers, agricultural extension services, and development organizations can leverage these insights to formulate strategies and interventions to encourage the adoption of implementing climate-smart agriculture practices is essential to safeguard food security amidst the challenges posed by climate change.

Effect size guidelines for cross-lagged effects.

Cross-lagged models are by far the most commonly used method to test the prospective effect of one construct on another, yet there are no guidelines for interpreting the size of cross-lagged effects. This research aims to establish empirical benchmarks for cross-lagged effects, focusing on the cross-lagged panel model (CLPM) and the random intercept cross-lagged panel model (RI-CLPM). We drew a quasirepresentative sample of studies published in four subfields of psychology (i.e., developmental, social-personality, clinical, and industrial-organizational). The dataset included 1,028 effect sizes for the CLPM and 302 effect sizes for the RI-CLPM, based on data from 174 samples. For the CLPM, the 25th, 50th, and 75th percentiles of the distribution corresponded to cross-lagged effect sizes of .03, .07, and .12, respectively. For the RI-CLPM, the corresponding values were .02, .05, and .11. Effect sizes did not differ significantly between the CLPM and RI-CLPM. Moreover, effect sizes did not differ significantly across subfields and were not moderated by design characteristics. However, effect sizes were moderated by the concurrent correlation between the constructs and the stability of the predictor. Based on the findings, we propose to use .03 (small effect), .07 (medium effect), and .12 (large effect) as benchmark values when interpreting the size of cross-lagged effects, for both the CLPM and RI-CLPM. In addition to aiding in the interpretation of results, the present findings will help researchers plan studies by providing information needed to conduct power analyses and estimate minimally required sample sizes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Simulation-Based Power Analyses for the Smallest Effect Size of Interest: A Confidence-Interval Approach for Minimum-Effect and Equivalence Testing

Effect sizes are often used in psychology because they are crucial when determining the required sample size of a study and when interpreting the implications of a result. Recently, researchers have been encouraged to contextualize their effect sizes and determine what the smallest effect size is that yields theoretical or practical implications, also known as the “smallest effect size of interest” (SESOI). Having a SESOI will allow researchers to have more specific hypotheses, such as whether their findings are truly meaningful (i.e., minimum-effect testing) or whether no meaningful effect exists (i.e., equivalence testing). These types of hypotheses should be reflected in power analyses to accurately determine the required sample size. Through a confidence-interval-focused approach and simulations, I show how to conduct power analyses for minimum-effect and equivalence testing. Moreover, I show that conducting a power analysis for the SESOI might result in inconclusive results. This confidence-interval-focused simulation-based power analysis can be easily adopted to different types of research areas and designs. Last, I provide recommendations on how to conduct such simulation-based power analyses.

THE ROLE OF BUSINESS MODEL CANVAS (BMC) IN DIGITAL ENTREPRENEURSHIP ON STUDENTS' INTEREST IN INDONESIA

THE ROLE OF BUSINESS MODEL CANVAS (BMC) IN DIGITAL ENTREPRENEURSHIP ON STUDENTS' INTEREST IN INDONESIA

Barriers Impact the Primary Healthcare Providers When Dealing With Emergency Cases: A Cross-Sectional Study in Al-Ahsa, Saudi Arabia.

Introduction Being the first in-line care providers, primary healthcare (PHC) physicians may encounter all forms of medical emergencies, ranging from minor complaints to major life-threatening events; therefore, this study aims to assess the PHC physicians' knowledge and attitude related to the diagnosis and management of emergency cases as well as their preferences for emergency medicine training courses and their preferred methods of training. Methods A descriptive cross-sectional study was conducted among physicians working at PHC in Al-Ahsa, Saudi Arabia, between November and December 2023, excluding those who do not work at PHC. The minimum required sample size was 192. A self-administered online questionnaire was developed using Google Forms and distributed conveniently through social media platforms. It consists of 22 items categorized into four sections:The first section focused on demographic information; the second section assessed knowledge related to the diagnosis and management of emergency cases commonly encountered in PHC, along with actual management experiences; the third section gauged physicians' attitudes, and the fourth section explored participants' preferences for emergency medicine courses and their preferred methods of training in this field. The Kruskal-Wallis rank sum and Wilcoxon rank sum tests were employed to identify predictors of knowledge and attitude. Results The study involved 193 participants, with 96 (50%) females and a median age of 30 years. The participants included 43 (22%) consultants, 69 (36%) family residents, 30 (16%) general practitioners, and 50 (26%) specialists. Participants reported a median of 4.0 years of experience in PHC in Saudi Arabia. The majority, 69 (36%), reported working in the hospital emergency department, with a reported median duration of three months. Roughly 84% of PHC physicians had a positive attitude toward emergency cases diagnosis and management, while 92% showed fair diagnostic knowledge of emergency cases, and 73% showed fair management knowledge. Higher knowledge in the diagnosis and management of PHC was linked to increasing age, being consultants, being employed in the emergency department, and having higher years of experience in PHC (p < 0.05). A positive attitude towardPHC was found to be associated with working as a consultant and being employed in the emergency department (p < 0.05). Approximately 133 (68.9%)expressed a preference for a course in wound care trauma, followed by central nervous system emergencies (n = 124, 64.2%), coronary artery disease emergencies (n = 116, 60.1%), and obstetrics/gynecologic emergencies (n = 114, 59.1%) with 160 (82.9%) favored training through practice in PHC under supervision of qualified staff. Conclusion While many PHC centers are well equipped and prepared for early stabilization and management of emergency cases, PHC physicians showed low competency in dealing with emergency cases. This indicates a great need for enhancing physician's knowledge and skills regarding emergency situations. Nevertheless, courses like basic life support (BLS) and advanced trauma life support (ATLS) should be the bare minimum requirements for PHC physicians. Mote advance training and lectures should be organized to enhance PHC physician competencies to deal with different emergencies.

Education Experiments in Latin America: Empirical Evidence to Guide Evaluation Design.

Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also helpful to anticipate the benefits, in terms of statistical power, of collecting household data, testing students at baseline, or relying on administrative data on previous cohorts from the same school. We use data from multiple cluster-randomized trials in four Latin American countries to provide information on the intraclass correlations in early grade literacy outcomes. We also describe the proportion of variance explained by different types of covariates. These parameters will help future researchers conduct statistical power analysis, estimate the required sample size, and determine the necessity of collecting different types of baseline data such as child assessments, administrative data at the school level, or household surveys.

Applying the Robust Chi-Square Goodness-of-Fit Test to Multilevel Multitrait-Multimethod Models: A Monte Carlo Simulation Study on Statistical Performance

As the robust maximum likelihood χ2 goodness-of-fit test had been found to yield inflated type-I error rates for certain two-level confirmatory factor analysis (CFA) models, a new correction for the test was implemented in Mplus version 8.7. In this simulation study, we inspected whether the corrected test statistics follow the expected χ2 distributions when applying more complex two-level models for multitrait-multimethod data with varying sample sizes and correlations within trait factors. Investigating rejection rates and probability-probability plots, we found that the new correction markedly and sufficiently reduced previously inflated rejection rates in conditions with within-trait correlations equal to 1, 100 between-level units, and 10 or 20 within-level units. In other conditions, rejection rates were hardly affected or not sufficiently reduced by the new correction. While in most conditions, 2 within-level units did not suffice, 5 within-level units and 250 between-level units were enough to yield correct rejection rates given within-trait correlations did not exceed 0.80. Correlations above 0.80 required larger sample sizes. In planning studies with multilevel CFA models, researchers should be aware that sample size requirements for likelihood-based model fit evaluations can depend on several different factors and might consider conducting Monte Carlo simulations tailored to their specific modeling conditions.

Efficient testing of the biomarker positive and negative subgroups in a biomarker-stratified trial.

In many randomized placebo-controlled trials with a biomarker defined subgroup, it is believed that this subgroup has the same or higher treatment effect compared with its complement. These subgroups are often referred to as the biomarker positive and negative subgroups. Most biomarker-stratified pivotal trials are aimed at demonstrating a significant treatment effect either in the biomarker positive subgroup or in the overall population. A major shortcoming of this approach is that the treatment can be declared effective in the overall population even though it has no effect in the biomarker negative subgroup. We use the isotonic assumption about the treatment effects in the two subgroups to construct an efficient way to test for a treatment effect in both the biomarker positive and negative subgroups. A substantial reduction in the required sample size for such a trial compared with existing methods makes evaluating the treatment effect in both the biomarker positive and negative subgroups feasible in pivotal trials especially when the prevalence of the biomarker positive subgroup is less than 0.5.

Evaluating the impact of internet communication quality in human resource management on the productivity of construction projects

This research endeavors to examine the intricate relationship between human resource management and communication management in Iraqi construction projects, exploring their impact on project productivity. Beyond its primary objectives, the study aims to identify key challenges and limitations in implementing effective communication and human resource management practices in the Iraqi construction sector. Understanding these challenges provides valuable insights for practitioners and policymakers, informing strategies to overcome obstacles and enhance project productivity. The research methodology involves an initial survey using a main questionnaire, focusing on three key areas: internet communication in human resource management, the influence of internet quality on construction project management, and factors affecting work productivity. The sample size, determined using Morgan's Table, requires a minimum of 248 participants. The questionnaire is distributed to engineers, managers, and technicians, with the number of respondents meeting the required sample size. Data analysis utilizes the Statistical Package for Social Sciences (SPSS), conducting Exploratory Factor Analysis (EFA) for each variable to identify underlying factors. A first-order confirmatory factor analysis is performed using the Analysis of Moment Structures (AMOS) program, creating a structural model incorporating three variables and their associated factors. Out of the initial 64 factors, 21 are included in the final model, refined based on modification indices (M.I) output to improve fit. Results indicate a significant positive effect (0.92) of internet communication quality on labor productivity, while the impact of labor resource management is relatively negligible (−0.07). The size effect of variables on human resource management is 0.05, suggesting a small but non-zero effect. Overall, the findings underscore the importance of internet communication quality in enhancing labor productivity in construction projects and emphasize the need for effective human resource management practices in the industry. This study contributes valuable insights for project managers and practitioners in construction sector.

COMPLICATIONS ASSOCIATED WITH INTERNAL ILIAC LIGATION IN THE CONTEXT OF SEVERE POST-PERIPARTUM HYSTERECTOMY BLEEDING

Severe post-peripartum hysterectomy bleeding poses a critical threat to maternal health, necessitating swift and effective interventions to ensure favorable outcomes. The main objective of the study is to find the potential complications associated with internal iliac ligation in the context of severe post-peripartum hysterectomy bleeding. This prospective observational study was conducted in the gynecology and obstetrics department of Lady Reading Hospital Peshawar from June 2022 til the required sample size is obtained. The study included women who had significant and profuse post-peripartum hysterectomy bleeding. All women with post-peripartum hysterectomy hemorrhage were identified, focusing specifically on cases where internal iliac ligation was employed as an intervention. The demographic information, including patient age, parity, medical history, and obstetric details, was collected on predesigned proforma. Data was collected from 196 patients. The mean age of the patients was 31.5±4.2 years. About 97% of patients were multiparas, and all patients presented with postpartum hemorrhage (100%). The mode of delivery was vaginal in 89% of cases. It is concluded that internal iliac ligation, while effective in controlling severe post-peripartum hysterectomy hemorrhage, presents a nuanced landscape marked by a low incidence of vascular (1.5%) and ureteric (0.5%) injuries. Consequently, we believe that procedure is worth try in the face of catastrophic uncontrolled haemorrhage, especially in low-income countries.

Detecting microvascular invasion in hepatocellular carcinoma using the impeded diffusion fraction technique to sense macromolecular coordinated water.

Impeded diffusion fraction (IDF) is a novel and promising diffusion-weighted imaging (DWI) technique that allows for the detection of various diffusion compartments, including macromolecular coordinated water, free diffusion, perfusion, and cellular free water. This study aims to investigate the clinical potential of IDF-DWI in detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). 66 patients were prospectively included. Metrics derived from IDF-DWI and the apparent diffusion coefficient (ADC) were calculated. Multivariate logistic regression was employed to identify clinical risk factors. Diagnostic performance was evaluated using the area under the receiver operating characteristics curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and the calibration error (cal-error). Additionally, a power analysis was conducted to determine the required sample size. The results suggested a significantly higher fraction of impeded diffusion (FID) originating from IDF-DWI in MVI-positive HCCs (p < 0.001). Moreover, the ADC was found to be significantly lower in MVI-positive HCCs (p = 0.019). Independent risk factors of MVI included larger tumor size and elevated alpha-fetoprotein (AFP) levels. The nomogram model incorporating ADC, FID, tumor size, and AFP level yielded the highest diagnostic accuracy for MVI (AUC-PR = 0.804, AUC-ROC = 0.783, cal-error = 0.044), followed by FID (AUC-PR = 0.693, AUC-ROC = 0.760, cal-error = 0.060) and ADC (AUC-PR = 0.570, AUC-ROC = 0.651, cal-error = 0.164). IDF-DWI shows great potential in noninvasively, accurately, and preoperatively detecting MVI in HCC and may offer clinical benefits for prognostic prediction and determination of treatment strategy.

Abstract 2174: Sample size estimations based on human microbiome temporal stability over six months: A shallow shotgun metagenome sequencing analysis

Abstract Introduction: Many biological factors can influence the human microbiome; thus, it is necessary to reasonably estimate sample size to have adequate power for future population-based studies. Methods: We evaluated the temporal stability of fecal microbial diversity, species level composition, and genes and functional pathways based on shallow shotgun metagenome sequencing from a population-based study in Costa Rica with two fecal samples separated by an interval of six months. We quantified the biological variability over six months with intraclass correlation coefficients (ICC). We then estimated the numbers of cases, assuming 1:1 or 1:3 matched case-control study, required to detect an association at significance levels of 0.05 and 0.001 with 80% power, based on the number of fecal specimens collected per participant. Results: For most alpha and beta diversity metrics, the temporal stability of fecal microbiome in samples collected at six months of interval was low to moderate, with ICCs of 0.6 or less. We observed heterogeneity in temporal stability for the proportions of species, genes and pathways, with ICCs varying between 0.0 and 0.9. For most microbiome measures from a single fecal collection, assuming an equal number of cases and controls at significance level of 0.05 (for alpha and beta diversity) or 0.001 (for species, genes and pathways), detecting an odds ratio of 1.5 per standard deviation of microbiome metric would require hundreds to thousands of cases. Specifically, detecting an association between alpha/beta diversity metrics and an outcome would require between 1,000-5,000 cases. In addition, for low prevalent species (5% - 10%, median ICC = 0.09), 15,102 cases would be required; in contrast, for species with high prevalence (&amp;gt;75%, median ICC = 0.41) 3,527 cases would be necessary; the same applies to genes and pathways. For an odds ratio of 1.5, assuming a 1:3 matched case-control study based on one fecal specimen per subject, 10,068 cases would be required for low prevalent species and 2,351 cases for species with high prevalence. Using the same study setting with multiple specimens per subject over time, the required sample size would be lower. Indeed, detecting an odds ratio of 1.5 for low prevalent species would require 15,102 cases with one specimen, 8,267 cases with two specimens, and 5,989 cases with three specimens. Conclusion: Our calculations suggest that to detect modest disease associations we would need a substantial number of cases. Repeated prediagnostic samples could decrease the number of subjects required to detect these associations as well as matching cases to a great number of controls. Citation Format: Semi Zouiouich, Smriti Karwa, Yunhu Wan, Emily Vogtmann, Carolina Porras, Christian C. Abnet, Jianxin Shi, Rashmi Sinha. Sample size estimations based on human microbiome temporal stability over six months: A shallow shotgun metagenome sequencing analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2174.

Detecting changes in generation and serial intervals under varying pathogen biology, contact patterns and outbreak response.

The epidemiological characteristics of SARS-CoV-2 transmission have changed over the pandemic due to emergence of new variants. A decrease in the generation or serial intervals would imply a shortened transmission timescale and, hence, outbreak response measures would need to expand at a faster rate. However, there are challenges in measuring these intervals. Alongside epidemiological changes, factors like varying delays in outbreak response, social contact patterns, dependence on the growth phase of an outbreak, and effects of exposure to multiple infectors can also influence measured generation or serial intervals. To guide real-time interpretation of variant data, we simulated concurrent changes in the aforementioned factors and estimated the statistical power to detect a change in the generation and serial interval. We compared our findings to the reported decrease or lack thereof in the generation and serial intervals of different SARS-CoV-2 variants. Our study helps to clarify contradictory outbreak observations and informs the required sample sizes under certain outbreak conditions to ensure that future studies of generation and serial intervals are adequately powered.