Effect Of Sample Size Research Articles

Quantifying the effects of sample size and species distribution on the precision and accuracy of abundance estimates from bottom-trawl surveys in the Gulf of Alaska

Quantifying the effects of sample size and species distribution on the precision and accuracy of abundance estimates from bottom-trawl surveys in the Gulf of Alaska

From Maps to Models: A Survey on the Reliability of Small Studies of Task-Based fMRI.

Task-based functional magnetic resonance imaging is a powerful tool for studying brain function, but neuroimaging research produces ongoing concerns regarding small-sample studies and how to interpret them. Although it is well understood that larger samples are preferable, many situations require researchers to make judgments from small studies, including reviewing the existing literature, analyzing pilot data, or assessing subsamples. Quantitative guidance on how to make these judgments remains scarce. To address this, we leverage the Human Connectome Project's Young Adult dataset to survey various analyses-from regional activation maps to predictive models. We find that, for some classic analyses such as detecting regional activation or cluster peak location, studies with as few as 40 subjects are adequate, although this depends crucially on effect sizes. For predictive modeling, similar sizes can be adequate for detecting whether features are predictable, but at least an order of magnitude more (at least hundreds) may be required for developing consistent predictions. These results offer valuable insights for designing and interpreting fMRI studies, emphasizing the importance of considering effect size, sample size, and analysis approach when assessing the reliability of findings. We hope that this survey serves as a reference for identifying which kinds of research questions can be reliably answered with small-scale studies.

Application of Deep Convolutional Generative Adversarial Network to Identification of Bridge Structural Damage

In recent years, the increase in the number of vehicles on the road has impacted the service life of bridges, and thus, their performance. In China, bridge inspections must be performed and documented at least once per year using labor-intensive and subjective approaches. In this study, a computationally efficient damage identification methodology for bridges was developed based on a deep convolutional generative adversarial network (DCGAN). To predict the damage to bridges, the vibration data of the structural damage are extracted from sensor measurements on the bridges and applied to the DCGAN model. The proposed damage identification method was validated on a simple supported beam bridge and a continuous girder bridge. The effects of sample size and noise on the identification accuracy were also evaluated. The performance of the DCGAN in the damage identification process is robust and reliable. This method for structural health monitoring offers a more objective evaluation of the overall performance and condition of a bridge, thus enabling bridge owners to efficiently allocate finite maintenance resources.

Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

Minimizing confounding in comparative observational studies with time-to-event outcomes: An extensive comparison of covariate balancing methods using Monte Carlo simulation.

Observational studies are frequently used in clinical research to estimate the effects of treatments or exposures on outcomes. To reduce the effects of confounding when estimating treatment effects, covariate balancing methods are frequently implemented. This study evaluated, using extensive Monte Carlo simulation, several methods of covariate balancing, and two methods for propensity score estimation, for estimating the average treatment effect on the treated using a hazard ratio from a Cox proportional hazards model. With respect to minimizing bias and maximizing accuracy (as measured by the mean square error) of the treatment effect, the average treatment effect on the treated weighting, fine stratification, and optimal full matching with a conventional logistic regression model for the propensity score performed best across all simulated conditions. Other methods performed well in specific circumstances, such as pair matching when sample sizes were large (n = 5000) and the proportion treated was < 0.25. Statistical power was generally higher for weighting methods than matching methods, and Type I error rates were at or below the nominal level for balancing methods with unbiased treatment effect estimates. There was also a decreasing effective sample size with an increasing number of strata, therefore for stratification-based weighting methods, it may be important to consider fewer strata. Generally, we recommend methods that performed well in our simulations, although the identification of methods that performed well is necessarily limited by the specific features of our simulation. The methods are illustrated using a real-world example comparing beta blockers and angiotensin-converting enzyme inhibitors among hypertensive patients at risk for incident stroke.

Effect of spatial scale, color infrared and sample size on learning poverty from aerial images

There is a growing amount of literature that focuses on using machine learning algorithms to predict poverty from satellite and aerial images on a low regional level, particularly for countries without a high-quality official statistical system. The data used for annotating images and training an algorithm are generally based on sample surveys. In The Netherlands, statistics on income and poverty are derived from tax registers resulting in a complete enumeration of the Dutch population. In this paper, we use this complete enumeration to simulate to which extent satellite or aerial images can predict poverty on low regional levels. After geocoding these households, aerial images are annotated and a deep learning algorithm is trained to predict poverty. The precision of the predictions is evaluated by comparing it with the true poverty fractions known from tax registers. The effect of different spatial scales (1-ha vs. 25-ha images), spectral bands (RGB vs. CIR), and sample sizes for the training set are compared. It is discussed how this information can be used in the production of low regional statistics on poverty in countries where high-quality official statistical systems are lacking.

Data-driven prediction of fracture toughness size effect in ductile-to-brittle transition using Two-Step-Scaling procedure

The fracture properties of ferritic steels in transition temperature region are usually statistically treated by models that presuppose the existence of various forms of quenched disorder (critical cleavage triggers). In this study, the fracture toughness from the Euro fracture toughness dataset (stress intensity factor used in the master curve KJc) for reactor steel 22NiMoCr37 is used with the aim of outlining a methodology for using the recently proposed two-step scaling (2SS) method. Three widely different temperatures (−154 °C, −91 °C, and 0 °C), which cover the entire range from lower shelf to upper shelf fracture, are selected to demonstrate the accuracy of extrapolation and interpolation of the fracture toughness CDF (cumulative distribution function) and the pertinent issues related to the procedure application. The obtained predictions at the two lower temperatures are in good agreement with the experimental results and well within the inherent experimental data scatter, while 2SS method is shown not to be applicable in upper-shelf transition region. Special attention is devoted to the effect of statistical sample size on prediction accuracy/reliability along with the minimum sample size requirement. Moreover, the reduction in random sample size affects the Weibull parameters, which in turn impacts the accuracy of predictions, introducing a degree of uncertainty and highlighting limitations in the method.

The Effects of the Training Sample Size, Ground Truth Reliability, and NLP Method on Language-Based Automatic Interview Scores’ Psychometric Properties

The Effects of the Training Sample Size, Ground Truth Reliability, and NLP Method on Language-Based Automatic Interview Scores’ Psychometric Properties

Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression

ABSTRACT Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research. Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes. Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect. Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains. Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.

Limited evidence of test-retest reliability in infant-directed speech preference in a large preregistered infant experiment.

Test-retest reliability-establishing that measurements remain consistent across multiple testing sessions-is critical to measuring, understanding, and predicting individual differences in infant language development. However, previous attempts to establish measurement reliability in infant speech perception tasks are limited, and reliability of frequently used infant measures is largely unknown. The current study investigated the test-retest reliability of infants' preference for infant-directed speech over adult-directed speech in a large sample (N=158) in the context of the ManyBabies1 collaborative research project. Labs were asked to bring in participating infants for a second appointment retesting infants on their preference for infant-directed speech. This approach allowed us to estimate test-retest reliability across three different methods used to investigate preferential listening in infancy: the head-turn preference procedure, central fixation, and eye-tracking. Overall, we found no consistent evidence of test-retest reliability in measures of infants' speech preference (overall r=0.09, 95% CI [-0.06,0.25]). While increasing the number of trials that infants needed to contribute for inclusion in the analysis revealed a numeric growth in test-retest reliability, it also considerably reduced the study's effective sample size. Therefore, future research on infant development should take into account that not all experimental measures may be appropriate for assessing individual differences between infants. RESEARCH HIGHLIGHTS: We assessed test-retest reliability of infants' preference for infant-directed over adult-directed speech in a large pre-registered sample (N=158). There was no consistent evidence of test-retest reliability in measures of infants' speech preference. Applying stricter criteria for the inclusion of participants may lead to higher test-retest reliability, but at the cost of substantial decreases in sample size. Developmental research relying on stable individual differences should consider the underlying reliability of its measures.

Relationship between serum iron, zinc, calcium, and HIF-1a-comparative analysis of 2 regions and 4 ethnic groups in China.

Altitude illness has serious effects on individuals who are not adequately acclimatized to high-altitude areas and may even lead to death. However, the individualized mechanisms of onset and preventive measures are not fully elucidated at present, especially the relationship between altitude illness and elements, which requires further in-depth research. Fresh serum samples were collected from individuals who underwent health examinations at the two hospitals in Xining and Sanya between November 2021 and December 2021. The blood zinc (Zn), iron (Fe), and calcium (Ca) concentrations, as well as hypoxia-inducible factor 1-alpha (HIF-1α) concentrations, were measured. This study conducted effective sample size estimation, repeated experiments, and used GraphPad Prism 9.0 and IBM SPSS version 19.0 software for comparative analysis of differences in the expression of elements and HIF-1α among different ethnic groups, altitudes, and concentration groups. Linear regression and multiple linear regression were employed to explore the relationships among elements and their correlation with HIF-1α. This study included a total of 400 participants. The results from the repeated measurements indicated that the consistency of the laboratory test results was satisfactory. In terms of altitude differences, except for Fe (p = 0.767), which did not show significant variance between low and high altitude regions, Zn, Ca, and HIF-1α elements all exhibited notable differences between these areas (p < 0.0001, p = 0.004, and p < 0.0001). When grouping by the concentrations of elements and HIF-1α, the results revealed significant variations in the distribution of zinc among different levels of iron and HIF-1α (p < 0.05). The outcomes of the linear regression analysis demonstrated that calcium and zinc, iron and HIF-1α, calcium and HIF-1α, and zinc and HIF-1α displayed substantial overall explanatory power across different subgroups (p < 0.05). Finally, the results of the multiple linear regression analysis indicated that within the high-altitude population, the Li ethnic group in Sanya, and the Han ethnic group in Sanya, the multiple linear regression model with HIF-1αas the dependent variable and elements as the independent variables exhibited noteworthy overall explanatory power (p < 0.05). The levels of typical elements and HIF-1α in the blood differ among various altitudes and ethnic groups, and these distinctions may be linked to the occurrence and progression of high-altitude illness.

Merging Data with Modeling: An Example from Fatigue.

It is well known that errors are inevitable in experimental observations, but it is equally unavoidable to eliminate errors in modeling the process leading to the experimental observations. If estimation and prediction are to be done with reasonable accuracy, the accumulated errors must be adequately managed. Research in fatigue is challenging because modeling can be quite complex. Furthermore, experimentation is time-consuming, which frequently yields limited data. Both of these exacerbate the magnitude of the potential error. The purpose of this paper is to demonstrate a procedure that combines modeling with independent experimental data to improve the estimation of the cumulative distribution function (cdf) for fatigue life. Subsequently, the effect of intrinsic error will be minimized. Herein, a simplified fatigue crack growth modeling is used. The data considered are a well-known collection of fatigue lives for an aluminum alloy. For lower applied stresses, the fatigue lives can range over an order of magnitude and up to 107 cycles. For larger applied stresses, the scatter in the lives is considerably reduced. Consequently, modeling must encompass a variety of conditions. The primary conclusion of the effort is that merging independent experimental data with a reasonably acceptable model vastly improves the accuracy of the calibrated cdfs for fatigue life, given the loading conditions. This allows for improved life estimation and prediction. For the aluminum data, the calibrated cdfs are shown to be quite good by using statistical goodness-of-fit tests, stress-life (S-N) analysis, and confidence bounds estimated using the mean square error (MSE) method. A short investigation into the effect of sample size is also included. Thus, the proposed methodology is warranted.

Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics

Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead–spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions.

Morphological simulation tests the limits on phenotype discovery in 3D image analysis.

In the past few decades, advances in 3D imaging have created new opportunities for reverse genetic screens. Rapidly growing datasets of 3D images of genetic knockouts require high-throughput, automated computational approaches for identifying and characterizing new phenotypes. However, exploratory, discovery-oriented image analysis pipelines used to discover these phenotypes can be difficult to validate because, by their nature, the expected outcome is not known a priori . Introducing known morphological variation through simulation can help distinguish between real phenotypic differences and random variation; elucidate the effects of sample size; and test the sensitivity and reproducibility of morphometric analyses. Here we present a novel approach for 3D morphological simulation that uses open-source, open-access tools available in 3D Slicer, SlicerMorph, and Advanced Normalization Tools in R (ANTsR). While we focus on diffusible-iodine contrast-enhanced micro-CT (diceCT) images, this approach can be used on any volumetric image. We then use our simulated datasets to test whether tensor-based morphometry (TBM) can recover our introduced differences; to test how effect size and sample size affect detectability; and to determine the reproducibility of our results. In our approach to morphological simulation, we first generate a simulated deformation based on a reference image and then propagate this deformation to subjects using inverse transforms obtained from the registration of subjects to the reference. This produces a new dataset with a shifted population mean while retaining individual variability because each sample deforms more or less based on how different or similar it is from the reference. TBM is a widely-used technique that statistically compares local volume differences associated with local deformations. Our results showed that TBM recovered our introduced morphological differences, but that detectability was dependent on the effect size, the sample size, and the region of interest (ROI) included in the analysis. Detectability of subtle phenotypes can be improved both by increasing the sample size and by limiting analyses to specific body regions. However, it is not always feasible to increase sample sizes in screens of essential genes. Therefore, methodical use of ROIs is a promising way to increase the power of TBM to detect subtle phenotypes. Generating known morphological variation through simulation has broad applicability in developmental, evolutionary, and biomedical morphometrics and is a useful way to distinguish between a failure to detect morphological difference and a true lack of morphological difference. Morphological simulation can also be applied to AI-based supervised learning to augment datasets and overcome dataset limitations.

Optimization-Based Stable Balancing Weights Versus Propensity Score Weighting for Samples With High Covariate Imbalance.

To compare the performance (covariate balance, effective sample size [ESS]) of stable balancing weights (SBW) versus propensity score weighting (PSW). Two applied cases were used to compare performance: (Case 1) extreme imbalance in baseline covariates between groups and (Case 2) substantial discrepancy in sample size between groups. Using the Premier Healthcare Database, we selected patients who (Case 1) underwent a surgical procedure with one of two different bipolar forceps between January 2000 and June 2020, or (Case 2) a neurological procedure using one of two different nonabsorbable surgical sutures between January 2000 and March 2020. Average treatment effects on the treated (ATT) weights were generated based on selected covariates. SBW was implemented using two techniques: (1) "grid search" to find weights of minimum variance at the lowest target absolute standardized mean difference (SMD); (2) finding weights of minimum variance at prespecified SMD tolerance. PSW and SBW methods were compared on postweighting SMDs, the number of imbalanced covariates, and ESS of the ATT-weighted control group. In both studies, improved covariate balance was achieved with both SBW techniques. All methods suffered from postweighting ESS that was lower than the unweighted control group's original sample size; however, SBW methods achieved higher ESS for the control groups. Sensitivity analyses using SBW to apply variable-specific SMD thresholds increased ESS, outperforming PSW. In this applied example, the optimization-based SBW method provided ample flexibility with respect to prespecification of covariate balance goals and resulted in better postweighting covariate balance and larger ESS as compared with PSW.

The Effect of Inhaled Oxygen on Postpartum Hemorrhage: A Systematic Review and Meta-Analysis.

Postpartum Hemorrhage (PPH) is a significant problem that can increase the risk of maternal mortality. Previous studies investigated the effect of Inhaled Oxygen (IO) on PPH. They found some conflicting results. Thus, the current systematic review and meta-analysis aimed to determine the effect of IO on PPH. Randomized trials were searched according to the PRISMA framework until the end of November 2022 in Web of Science, PubMed, Scopus, and Cochrane Library databases. Statistical analyses were performed in the STATA v. 14 software. I2 statistic was applied to assess heterogeneity between studies. The random effect model, sample size, and mean and standard deviation of each group were applied to report the pooled effect size. Version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2) was used to evaluate the risk of bias in the included studies. Finally, five articles were included in the meta-analysis. Two and three studies reported the mean of bleeding after vaginal delivery during one- and two-hours oxygen therapy, respectively. Results showed that IO significantly reduced bleeding by 38.91 mL in the intervention group compared to routine care (WMD: -38.91, 95%CI: -60.18 to -17.64) after vaginal delivery. In addition, IO during one (WMD: -38.42, 95%CI: -71.62 to -5.22) and two (WMD: -41.93, 95%CI: -60.15 to - 23.71) hours significantly decreased bleeding in the intervention. According to the present study, IO can significantly reduce PPH in the intervention group compared to routine care. However, more rigorously randomized clinical trials are required to decide better about this issue.

Causal meta-analysis by integrating multiple observational studies with multivariate outcomes.

Integrating multiple observational studies to make unconfounded causal or descriptive comparisons of group potential outcomes in a large natural population is challenging. Moreover, retrospective cohorts, being convenience samples, are usually unrepresentative of the natural population of interest and have groups with unbalanced covariates. We propose a general covariate-balancing framework based on pseudo-populations that extends established weighting methods to the meta-analysis of multiple retrospective cohorts with multiple groups. Additionally, by maximizing the effective sample sizes of the cohorts, we propose a FLEXible, Optimized, and Realistic (FLEXOR) weighting method appropriate for integrative analyses. We develop new weighted estimators for unconfounded inferences on wide-ranging population-level features and estimands relevant to group comparisons of quantitative, categorical, or multivariate outcomes. Asymptotic properties of these estimators are examined. Through simulation studies and meta-analyses of TCGA datasets, we demonstrate the versatility and reliability of the proposed weighting strategy, especially for the FLEXOR pseudo-population.

Prevention of Anthracyclines and HER2 Inhibitor-Induced Cardiotoxicity: A Systematic Review and Meta-Analysis.

This meta-analysis and systematic review aim to consolidate evidence on cardiotoxicity prevention and treatment strategies in patients receiving anthracyclines or HER2 receptor inhibitors, vital treatments for breast cancer and hematologic malignancies. By synthesizing existing research, the goal is to provide impactful insights that enhance patient care and outcomes. Comprehensive research across PubMed, Scopus, EMBASE, and the Cochrane Central Register for Controlled Trials was conducted, selecting clinical trials focusing on cardioprotection in anthracyclines or HER2 inhibitor-treated individuals. Effect sizes were computed using OpenMeta (Analyst), with leave-out meta-analysis to assess potential small study effects. Meta-regression explored treatment duration and sample size effects. Evidence quality for primary outcomes was evaluated using ROB, Robins 2, and Newcastle-Ottawa tools. Twenty -three studies involving a total of 14,652 patients (13,221 adults and 1431 kids) were included in the current systematic review and meta-analysis. The risk of bias and methodological quality of the included studies suggested good and moderate quality. Patients prescribed β-blockers demonstrated a 74% lower likelihood of exhibiting cardiotoxicity symptoms (OR 1.736). Similarly, the use of dexrazoxane was linked to a threefold decrease in cardiac abnormalities risk (OR 2.989), and ACE inhibitor administration showed half the risk compared with the control group (OR 1.956). Through this systematic review and meta-analysis, it was shown that there is a reduction in cardiotoxicity from either anthracyclines or HER2 inhibitors in patients receiving pharmacoprophylaxis.

Differential Item Functioning of the region-based national examination equipment

This research aims to detect Differential Item Functioning (DIF) in the 2014/2015 National Examination Questions in mathematics of junior high schools and equivalent-level schools in the Yogyakarta region as a reference group and the South Kalimantan region as a focus group using the Likelihood Ratio Test (LRT) method, Area Measure Raju, and Lord. A sensitivity analysis was carried out to determine which method was the most sensitive. The data consisted of 5,465 National Examination papers of the students from the two regions, who worked on type A questions. A sample of 1,000 exam papers for each region was established using the simple random sampling (SRS) technique, which was conducted to avoid the effect of sample size. The results of the research showed that by using the LRT method the researchers found 36 items had significant DIF detection, 32 items were significant for Raju Area, and all items had significant DIF detection using Lord. Lord Method is the most sensitive because this method can detect the most DIF items.

Studi Skala Efek Pada Uji Kuat Tekan Uniaksial Terhadap Batulempung Formasi Pulau Balang dan Formasi Balikpapan Di Samarinda

Rock mass is a volume of rock consisting of rock material in the form of minerals, texture, composition and also consisting of discontinuous planes, forming a material and interconnected with all elements as a unit. The rock mass itself is composed of several intact rocks which basically have isotropic, continuous and homogeneous properties. However, the conditions found in the field are different, namely anisotropic, discontinuous and heterogeneous. These properties will certainly influence the test results in the uniaxial test. There are several factors that influence the results of uniaxial rock tests, one of which is the scale effect. The purpose of this test is to determine and analyze the effect of rock sample size on the uniaxial compressive strength value of claystone. This rock testing was carried out at the Mineral and Coal Technology Laboratory and the rock sampling locations were in Palaran District, Samarinda City and in North Samarinda District, Samarinda City. In this uniaxial compressive strength test, 3 side widths with different lengths will be used. After carrying out the uniaxial compressive strength test, the average uniaxial compressive strength test value was obtained in each formation, such as the Balang Island formation, the average rock compressive strength test value was 1.68 Mpa and the Balang formation, the average rock compressive strength test value was 3.10. Mpa. Based on these results, it can be concluded that the larger the sample size, the smaller the rock compressive strength test value tends to be.