Standard Sample Size Research Articles

Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes.

An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.

A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials.

Stepped-wedge cluster randomized trials tend to require fewer clusters than standard parallel-arm designs due to the switches between control and intervention conditions, but there are no recommendations for the minimum number of clusters. Trials randomizing an extremely small number of clusters are not uncommon, but the justification for small numbers of clusters is often unclear and appropriate analysis is often lacking. In addition, stepped-wedge cluster randomized trials are methodologically more complex due to their longitudinal correlation structure, and ignoring the distinct within- and between-period intracluster correlations can underestimate the sample size in small stepped-wedge cluster randomized trials. We conducted a review of published small stepped-wedge cluster randomized trials to understand how and why they are used, and to characterize approaches used in their design and analysis. Electronic searches were used to identify primary reports of full-scale stepped-wedge cluster randomized trials published during the period 2016-2022; the subset that randomized two to six clusters was identified. Two reviewers independently extracted information from each report and any available protocol. Disagreements were resolved through discussion. We identified 61 stepped-wedge cluster randomized trials that randomized two to six clusters: median sample size (Q1-Q3) 1426 (420-7553) participants. Twelve (19.7%) gave some indication that the evaluation was considered a "preliminary" evaluation and 16 (26.2%) recognized the small number of clusters as a limitation. Sixteen (26.2%) provided an explanation for the limited number of clusters: the need to minimize contamination (e.g. by merging adjacent units), limited availability of clusters, and logistical considerations were common explanations. Majority (51, 83.6%) presented sample size or power calculations, but only one assumed distinct within- and between-period intracluster correlations. Few (10, 16.4%) utilized restricted randomization methods; more than half (34, 55.7%) identified baseline imbalances. The most common statistical method for analysis was the generalized linear mixed model (44, 72.1%). Only four trials (6.6%) reported statistical analyses considering small numbers of clusters: one used generalized estimating equations with small-sample correction, two used generalized linear mixed model with small-sample correction, and one used Bayesian analysis. Another eight (13.1%) used fixed-effects regression, the performance of which requires further evaluation under stepped-wedge cluster randomized trials with small numbers of clusters. None used permutation tests or cluster-period level analysis. Methods appropriate for the design and analysis of small stepped-wedge cluster randomized trials have not been widely adopted in practice. Greater awareness is required that the use of standard sample size calculation methods can provide spuriously low numbers of required clusters. Methods such as generalized estimating equations or generalized linear mixed models with small-sample corrections, Bayesian approaches, and permutation tests may be more appropriate for the analysis of small stepped-wedge cluster randomized trials. Future research is needed to establish best practices for stepped-wedge cluster randomized trials with a small number of clusters.

The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data

This research aims to explore and compare several nonparametric regression techniques, including smoothing splines, natural cubic splines, B-splines, and penalized spline methods. The focus is on estimating parameters and determining the optimal number of knots to forecast cyclic and nonlinear patterns, applying these methods to simulated and real-world datasets, such as Thailand’s coal import data. Cross-validation techniques are used to control and specify the number of knots, ensuring the curve fits the data points accurately. The study applies nonparametric regression to forecast time series data with cyclic patterns and nonlinear forms in the dependent variable, treating the independent variable as sequential data. Simulated data featuring cyclical patterns resembling economic cycles and nonlinear data with complex equations to capture variable interactions are used for experimentation. These simulations include variations in standard deviations and sample sizes. The evaluation criterion for the simulated data is the minimum average mean square error (MSE), which indicates the most efficient parameter estimation. For the real data, monthly coal import data from Thailand is used to estimate the parameters of the nonparametric regression model, with the MSE as the evaluation metric. The performance of these techniques is also assessed in forecasting future values, where the mean absolute percentage error (MAPE) is calculated. Among the methods, the natural cubic spline consistently yields the lowest average mean square error across all standard deviations and sample sizes in the simulated data. While the natural cubic spline excels in parameter estimation, B-splines show strong performance in forecasting future values.

Empirical Review on Determinants of Willingness to Pay for Natural and Environmental Resource Valuation: Meta-Analysis

Even if natural and environmental resource plays detrimental role prosperity of nations, measuring their value is highly problematic. Lack of realistic markets data to estimate its value for the goods and services necessitates the use of nonmarket valuation techniques. The meta-analysis was carried out to see the mean size effect of certain demographic, socioeconomic and institutional factors on willingness to pay of natural resources conservation and improvement. The empirical analysis review was done on about 57 published articles for total of 116 observations. The data synthesis was done from published articles include coefficients of explanatory variable and standard error, model used, value measured, study region, sample size and publication from the year of 2002 up to 2019 year. The data analyzed in excel sheet and SPSS software. The study countries were Ethiopia, Uganda, Kenya, Guinea, Nigeria, Malaysia, USA, Iraq, China and India. The result confirmed that measurement errors and heterogeneity of case studies, interviewed resulted difference in willingness to pay estimate. The result implied inverse relationship between willingness estimate and environmental resource valued and study destination [Eat Africa]. The meta-analysis indicated mean size effect of willingness to pay defined as function of income, age, bid value, occupation, sex, knowledge, and education level of respondents. The empirical analysis result showed that increasing awareness, enabling to expand income earning believed to increase the willingness to pay value.

Three-Dimensional Optical Study on the Effects of Microwave Glazing on Surface Roughness of Zirconia-Reinforced Glass Ceramic.

BACKGROUND This 3-dimensional (3D) optical study aimed to evaluate the effects of microwave glazing on the surface roughness of zirconia-reinforced glass. Glazed surfaces of ceramic provide a smooth and esthetically superior restoration. There are many methods of glazing. However, this study aims to evaluate the effect of microwave glazing on ceramic restorations over conventional oven and hand polishing. MATERIAL AND METHODS A sample size of 90 ceramic material tiles was derived according to the standard sample size formula. The 3 dental ceramics used were IPS e.max CAD (lithium disilicate ceramic; IvoclarVivadent), Suprinity (zirconia-reinforced lithium silicate; VITA Zahnfabrik), and Celtra Duo zirconia-reinforced lithium silicate; Dentsply Sirona). Each group was further divided equally to undergo conventional oven glazing, hand polishing, and microwave glazing. The final glazed surfaces were then evaluated for surface roughness with the Ra parameter, using a Contour GT 3D Optical Microscope (Bruker) and 3D non-contact surface metrology with interferometry. RESULTS The ANOVA test for intergroup comparison showed microwave glazing was a significantly better glazing method than conventional oven and hand polishing (P<0.05). A statistically significant difference was shown between conventional and microwave glazing; however, the difference was greater between conventionally glazed and hand-polished specimens. Furthermore, a highly significant difference between microwave-glazed and hand-polished specimens was observed. CONCLUSIONS Results showed that irrespective of the ceramic, microwave-glazed ceramics were better than traditional oven-glazed ceramics, and hand-polishing resulted in a rougher surface than glazing. Irrespective of the surface treatment methods, IPS e.max CAD ceramic showed a relatively smoother surface than did Suprinity and Celtra Duo.

Comparison of peer-assisted learning with expert-led learning in medical school ultrasound education: a systematic review and meta-analysis.

Teaching point-of-care ultrasonography (PoCUS) to medical students is resource intensive. Peer-assisted learning, where the teacher can be a medical student, may be a feasible alternative to expert-led learning. The objective of this systematic review and meta-analysis was to compare the PoCUS performance assessments of medical students receiving peer-assisted vs expert-led learning. This study was submitted to PROSPERO (CRD42023383915) and reported with PRISMA guidelines. MEDLINE, Embase, ERIC, Education Source, Scopus, and Web of Science were searched from inception to November 2022. Inclusion criteria were studies comparing peer-assisted vs expert-led PoCUS teaching for undergraduate medical students. The primary outcome was performance assessment of PoCUS skills. Two reviewers independently screened citations and extracted data. The Cochrane risk-of-bias tool for randomized trials was used to assess study quality. Studies were included in the meta-analysis if mean performance assessment scores with standard deviations and sample sizes were available. A random-effects meta-analysis was conducted to estimate the accuracy score of practical knowledge test for each group. A meta-regression evaluated difference in mean scores. The search yielded 2890 citations; 1417 unique citations remained after removing duplicates. Nine randomized-controlled studies conducted in Germany, USA, and Israel, with 593 participants, were included in the meta-analysis. The included studies assessed teaching of abdominal, cardiac, thoracic, musculoskeletal, and ocular PoCUS skills. Most studies had some risk-of-bias concerns. The estimate accuracy score after weighting is 0.56 (95% CI [0.47, 0.65]) for peer-assisted learning and 0.59 (95% CI [0.49, 0.69]) for expert-led learning. The regression coefficient estimate is 0.0281 (95% CI [-0.1121, 0.1683]); P value is 0.69. This meta-analysis found that peer-assisted learning was a reasonable alternative to expert-led learning for teaching PoCUS skills to medical students.

The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves.

Often in the planning phase of a clinical trial, a researcher will need to choose between a standard versus weighted log-rank test (LRT) for investigating right-censored survival data. While a standard LRT is optimal for analyzing evenly distributed but distinct survival events (proportional hazards), an appropriately weighted LRT test may be better suited for handling non-proportional, delayed treatment effects. The "a priori" misspecification of this alternative may result in a substantial loss of power when determining the effectiveness of an experimental drug. In this paper, the standard unweighted and inverse log-rank tests (iLRTs) are compared with the multiple weight, default Max-Combo procedure for analyzing differential late survival outcomes. Unlike combination LRTs that depend on the arbitrary selection of weights, the iLRT by definition is a single weight test and does not require implicit multiplicity correction. Empirically, both weighted methods have reasonable flexibility for assessing continuous survival curve differences from the onset of a study. However, the iLRT may be preferable for accommodating delayed separating survival curves, especially when one arm finishes first. Using standard large-sample methods, the power and sample size for the iLRT are easily estimated without resorting to complex and timely simulations.

Uncertainty quantification of a spent fuel refrigeration pool utilizing a [formula omitted] turbulence reduced order model

In this article, the uncertainty of a three-dimensional turbulent natural convection transient is quantified in a geometry representing an idealized spent fuel pool. The assessment is carried out through the creation of a surrogate fast model. This is build utilizing Proper Orthogonal Decomposition and Galerkin projection applied to a K-ϵ turbulent Navier–Stokes formulation. We discuss the uncertainties created by a uncertain heat release of the spent fuel elements. We consider the hypothesis of deviations that follow the normal distribution around a certain nominal value, with different standard deviations and sample size. The expected results and its uncertainty are henceforth computed by Monte–Carlo method, calculating hundreds of solutions of the Initial Value Problem with the surrogate model.

Optimal Weighting of Preclinical Alzheimer's Cognitive Composite (PACC) Scales to Improve their Performance as Outcome Measures for Alzheimer's Disease Clinical Trials.

Cognitive composite scales constructed by combining existing neuropsychometric tests are seeing wide application as endpoints for clinical trials and cohort studies of Alzheimer's disease (AD) predementia conditions. Preclinical Alzheimer's Cognitive Composite (PACC) scales are composite scores calculated as the sum of the component test scores weighted by the reciprocal of their standard deviations at the baseline visit. Reciprocal standard deviation is an arbitrary weighting in this context, and may be an inefficient utilization of the data contained in the component measures. Mathematically derived optimal composite weighting is a promising alternative. Sample size projections using standard power calculation formulas were used to describe the relative performance of component measures and their composites when used as endpoints for clinical trials. Power calculations were informed by (n=1,333) amnestic mild cognitive impaired participants in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set. A composite constructed using PACC reciprocal standard deviation weighting was both less sensitive to change than one of its component measures and less sensitive to change than its optimally weighted counterpart. In standard sample size calculations informed by NACC data, a clinical trial using the PACC weighting would require 38% more subjects than a composite calculated using optimal weighting. These findings illustrate how reciprocal standard deviation weighting can result in inefficient cognitive composites, and underscore the importance of component weights to the performance of composite scales. In the future, optimal weighting parameters informed by accumulating clinical trial data may improve the efficiency of clinical trials in AD.

A novel pulse-decay-balance method for smooth rock joint aperture measurement

A novel pulse-decay-balance (PDB) method was proposed to simultaneously measure hydraulic and mechanical apertures (e and E) under stress. The YOKO2 system was modified into a closed system to measure the e and E of saw-cut and foliated slate samples under a confining (normal) stress of up to 60 MPa. The hydraulic aperture was determined based on analysis of the pressure decay curve, and the mechanical aperture was determined based on the joint volume measurement (JV method) by determining the balance pressure. The hydraulic aperture estimated using the proposed method was comparable to that measured in steady-state tests. The hydraulic apertures of the saw-cut and foliated slate samples estimated using the simplified and general analytical solutions were in good agreement, especially for smooth joints under high normal stress. However, the error in the hydraulic aperture estimation, induced by neglecting the specific storage of the rock joint, is non-negligible for rough rock joints under low normal stress. A regression function based on the stress-dependent E and e fitted well with the experimental data to describe the E-e relationship. Our testing results, which show a decreasing trend in the E/e ratio with decreasing e, contradict the existing (E/e)-e relation, at least for smooth joints. The measurable e via the closed YOKO2 system was several tens of microns. Although the measurable E is below 1 μm, the best measurement range of E is roughly 1000 to 100,000 μm. A standard sample size for the conventional triaxial test was suggested to harmonize the optimum measurement ranges of the hydraulic and mechanical apertures. In this case, an analytical solution for the pulsed decay that considers the specific storage of the rock joint should be used to reduce the error in estimating hydraulic apertures.

PFCE2: A versatile parameter-free calibration enhancement framework for near-infrared spectroscopy

Near-infrared (NIR) spectroscopy is a widely used technique for chemical analysis, but it has faced challenges of calibration transfer, maintenance, and enhancement among different instruments and conditions. The parameter-free calibration enhancement (PFCE) framework was developed to address these challenges with non-supervised (NS), semi-supervised (SS), and full-supervised (FS) methods. This study presented PFCE2, an updated version of the PFCE framework that incorporates two new constraints and a new method to improve the robustness and efficiency of calibration enhancement. First, normalized L2 and L1 constraints were introduced to replace the correlation coefficient (Corr) constraint used in the original PFCE. These constraints preserve the parameter-free feature of PFCE and impose smoothness or sparsity on the model coefficients. Second, multitask PFCE (MT-PFCE) was proposed within the framework to address the calibration enhancement among multiple instruments, enabling the framework to be versatile for all possible calibration transfer situations. Demonstrations conducted on three NIR datasets of tablets, plant leaves, and corn showed that the PFCE methods with the new L2 and L1 constraints can result in more accurate and robust predictions than the Corr constraint, especially when the standard sample size is small. Moreover, MT-PFCE could refine all models in the involved scenarios at once, leading to significant enhancement in model performance, compared to the original PFCE method with the same data requirements. Finally, the applicable situations of the PFCE framework and other analogous calibration transfer methods were summarized, facilitating users to choose suitable methods for their application. The source codes written in both MATLAB and Python are available at https://github.com/JinZhangLab/PFCE and https://pypi.org/project/pynir/, respectively.

Wear Analysis and Experimentation of Sintered Al-SiCp and Al-B&lt;sub&gt;4&lt;/sub&gt;Cp Composites with Deformation Predictions

Complex geometries and porous components are very difficult to manufacture using traditional methods. Powder metallurgy is a metal forming process that allows complex shapes to be easily formed without loss of material. Metal matrix composites are used for high thermal conductivity and high wear resistance. In Al-B4CP, the composition of boron carbide is changed by 5%, 10%, 15%, and 20%. Al-SiCp composites maintain similar weight fractions. The blended mixture is compacted and sintered to the required standard sample size. After heat treatment, samples are tested for wear rate using a pin-on-disc wear tester, which is analyzed with analysis software.

Optimal sampling regimes for estimating predator-prey dynamics

Predator-prey models are often used in ecology to represent realistic encounters between species. Lotka-Volterra differential equations are famously used by ecologists to model the relationship between a predator and prey. Given ecological data collection is known to be time consuming and difficult, the purpose of this paper is to optimize the process and improve sampling efficiency. The method of sequential optimality is applied to a more complex population model with multiple basins of attraction, the Lotka-Volterra differential equations. In this application, various theoretical scenarios are simulated using varying sample sizes and timeframes to determine optimal designs. The first scenario uses a standard sample size and design window to represent a realistic budget for ecological data collection. The second scenario decreases the budget and extends the design window as a representation of a limited budget. The third scenario represents ample resources available for sampling. The three scenarios are used to then compare the optimal designs and help ecologists evaluate the method of sequential optimality. I, A and D optimal designs are compared in the simulation results. The I-optimality criterion tends to outperform the A and D criteria and is recommended as a primary selection when using the sequential optimality algorithm. This simulation study has further developed sequential optimality by predicting optimal sampling regimes according to the dynamics associated with the Lotka-Volterra differential equations.

Investigation of the fatigue life relationship among different geometry combinations of the 3-point bending cylinder (3PBC) fatigue test for asphalt concrete

ABSTRACT Laboratory testing is widely used to characterise fatigue properties of asphalt mixtures for the design of asphalt pavements. The recently introduced Three-Point Bending Cylinder (3PBC) test has proven to be an advantageous laboratory testing alternative. However, the standard sample size for the 3PBC test may hamper its use for testing as-built thin lifts of asphalt pavement. In this study, the potential to alter the sample size for 3PBC test was investigated. Various numerical techniques were explored to relate the fatigue performance in terms of the number of cycles to failure (Nf) among varying specimen diameters. In addition to Nf of the tested 3PBC sample diameter Nf (Di), strain level; 3PBC test sample diameter Di; and dynamic modulus |E*|; were selected as input variables to correlate with the Nf for the reference geometry Nf(D.R.). Initially, multi-linear regression (MLR) analysis was applied to inspect if any cogent relation could be achieved among the Nf values for different 3PBC sample diameters. In addition, multigene genetic programming (MGGP) and artificial neural network (ANN) analyses were performed to check if a better fit could be achieved. Both these techniques, however, revealed similar statistical results to the MLR model, with the ANN model achieving higher statistics.

Apparent Diffusion Coefficient (ADC) as an Early Biomarker for Tumor Response to Radiation Therapy, a Meta-Analysis

<h3>Purpose/Objective(s)</h3> MRI equipped linear accelerators are increasing the routine ability to acquire daily diffusion weight imaging (DWI) during a course of radiation therapy (RT). Multiple small series have examined DWI and apparent diffusion coefficient (ADC) as an early biomarker of tumor response to RT. We sought to comprehensively evaluate ADC change (ΔADC) by conducting a meta-analysis of published data. We hypothesized that quantifiable changes in ADC occur early after initiation of treatment and that a certain magnitude in ΔADC changes would be associated with response to RT. <h3>Materials/Methods</h3> A search was conducted for studies with ADC values acquired during and following RT. Studies were included that published mean ADC values of tumors prior to treatment (baseline), 1 and/or 2 weeks into treatment, and included therapeutic response data or progression reported in follow up. The mean percentage changes between studies were combined using the inverse variance weighted method, where variances were estimated from the study reported standard deviations, using a random effects model, to accommodate potential between study heterogeneity. A single estimate of percentage change in ADC, along with the standard deviation and sample size corresponding to estimate was used as the summary statistic of interest from each study, for the response and non-response groups separately. These were combined using the inverse variance weighting method to obtain an overall estimate of percentage ADC change for the two groups of interest, where the square of the reported standard deviation served as the variance estimate. The overall estimate was computed using the random-effects meta-analysis technique. <h3>Results</h3> Data from eight published studies were analyzed totaling 207 patients undergoing RT for brain metastases (n=66), esophageal (n=50), head-and-neck (n=34), cervical (n=33), and rectal (n=24) cancer. One week after the induction of RT, there was no significant change in ADC from baseline in non-responding lesions, +3.32%, 95% CI [-2.25, +8.89], or responding lesions, +6.61%, 95% CI [-0.27, +13.49]. Two weeks after induction, however, the ADC in responding lesions increased by an average of 16.60%, 95% CI [4.86, 28.34], while non-responding lesions remained unchanged from baseline with a mean ΔADC of +4.36%, 95% CI [-8.38, +17.10]. ADC was significantly increased from baseline at two weeks, but not at one week, and lesions with less than 4.86% increase in ADC from baseline at two weeks were unlikely to be responding to the current therapeutic regimen. <h3>Conclusion</h3> From this relatively small meta-analysis it was demonstrated that significant increases in mean ADC for responding lesions were observed as early as two weeks after the initiation of RT. No significant change from baseline ADC was observed in tumors that did not respond to therapy. These data suggest a strong potential role for ΔADC as an early universal biomarker should be investigated in prospective clinical trials as a method to personalize RT dose.

How complete does the fossil record have to be?

No science is based on complete knowledge, so the incompleteness of the fossil record is not necessarily a serious limitation in using palaeontological data to test geological or biological hypotheses. Completeness and adequacy of data are measured against sorne prior requirement which, in turn, should determine sampling strategy. Adequacy is considered for single samples, for detecting patterns by comparing samples, and for comparisons with neontology. Adequacy of single samples can be determined by sample size or by "growth of knowledge" curves. When comparing samples, standard sample sizes have serious disadvantages. Pattern detection requires appropriate density of samples. In stratigraphy using standard intervals has disadvantages. Bed by bed sampling is often better, but one sample per bed is usually inadequate. In comparing morphological data from the fossil record with more extensive neontolog&iacute;cai data, what matters is not that extra data are avaiiabie but whether or not they are necessary. Much neontological data is based on taxa identified morphologically anyway. Commonly problems with palaeontological data result as much from inadequate collection and recording as from inadequacies of the fossil record. We must be clear about our aims, and therefore what data are necessary, before data gathering begins.

The batched stepped wedge design: A design robust to delays in cluster recruitment.

Stepped wedge designs are an increasingly popular variant of longitudinal cluster randomized trial designs, and roll out interventions across clusters in a randomized, but step‐wise fashion. In the standard stepped wedge design, assumptions regarding the effect of time on outcomes may require that all clusters start and end trial participation at the same time. This would require ethics approvals and data collection procedures to be in place in all clusters before a stepped wedge trial can start in any cluster. Hence, although stepped wedge designs are useful for testing the impacts of many cluster‐based interventions on outcomes, there can be lengthy delays before a trial can commence. In this article, we introduce “batched” stepped wedge designs. Batched stepped wedge designs allow clusters to commence the study in batches, instead of all at once, allowing for staggered cluster recruitment. Like the stepped wedge, the batched stepped wedge rolls out the intervention to all clusters in a randomized and step‐wise fashion: a series of self‐contained stepped wedge designs. Provided that separate period effects are included for each batch, software for standard stepped wedge sample size calculations can be used. With this time parameterization, in many situations including when linear models are assumed, sample size calculations reduce to the setting of a single stepped wedge design with multiple clusters per sequence. In these situations, sample size calculations will not depend on the delays between the commencement of batches. Hence, the power of batched stepped wedge designs is robust to unexpected delays between batches.

Databases of Iodine Content of Foods and Dietary Supplements–Availability of New and Updated Resources

The iodine status of the U.S. population is adequate overall, but some people might have insufficient or overly high iodine intakes and would benefit from certain dietary changes. Identifying these individuals can be challenging, however, due to a lack of data on the iodine content of foods and dietary supplements. To fill this gap, a new online database--the USDA, FDA, and ODS-NIH Database for the Iodine Content of Common Foods--was released in 2020. It contains about 430 foods and beverages, such as dairy products and eggs, baby foods, vegetables, meats, mixed dishes, juices, seafood, various types of salt, restaurant foods, and baked goods, including breads made with and without iodate-containing dough conditioners. Data are presented as mean iodine content for both standard serving sizes and per 100 g of food, along with standard deviations, value ranges, and sample sizes. The new database, combined with the existing Dietary Supplement Ingredient Database, which contains chemically analyzed values for the iodine content of dietary supplements, and the newly updated Dietary Supplement Label Database, which contains label information from thousands of dietary supplements on the market, provides comprehensive data to help assess the iodine intakes of individuals and populations.

CPNCoverageAnalysis: An R package for parameter estimation in conceptual properties norming studies.

In conceptual properties norming studies (CPNs), participants list properties that describe a set of concepts. From CPNs, many different parameters are calculated, such as semantic richness. A generally overlooked issue is that those values are only point estimates of the true unknown population parameters. In the present work, we present an R package that allows us to treat those values as population parameter estimates. Relatedly, a general practice in CPNs is using an equal number of participants who list properties for each concept (i.e., standardizing sample size). As we illustrate through examples, this procedure has negative effects on data's statistical analyses. Here, we argue that a better method is to standardize coverage (i.e., the proportion of sampled properties to the total number of properties that describe a concept), such that a similar coverage is achieved across concepts. When standardizing coverage rather than sample size, it is more likely that the set of concepts in a CPN all exhibit a similar representativeness. Moreover, by computing coverage the researcher can decide whether the CPN reached a sufficiently high coverage, so that its results might be generalizable to other studies. The R package we make available in the current work allows one to compute coverage and to estimate the necessary number of participants to reach a target coverage. We show this sampling procedure by using the R package on real and simulated CPN data.

A Comparative Evaluation of Remineralizing Potential of Commonly Used Fluoridated Toothpaste, Herbal Toothpaste, Toothpaste with Zinc Hydroxyapatite, and Toothpaste with Calcium Sucrose Phosphate in Children: A Scanning Electronic Microscopic Study.

AimThe aim of this in vitro study is to evaluate the remineralizing potential of commonly used fluoridated toothpaste, toothpaste with calcium sucrose phosphate, toothpaste with zinc hydroxyapatite, herbal toothpaste, and to compare them.BackgroundThe process of demineralization and remineralization is balanced and occurs concurrently in the oral cavity, but even a mild disruption in this mechanism could lead to dental caries. Dental caries is a threat in the Third World countries and more common entity in childhood. The caries prevalence is reported to be 41% for children within the age of 2-11 years.Materials and methodAccording to previous studies and standard sample size calculating formula the sample size of 48 was calculated, the teeth were subjected to demineralization - remineralization process and were observed under scanning electron microscope.ResultsThe outcomes of this study suggest that the remineralizing potential of toothpaste with zinc hydroxyapatite (p = 0.0001*) was highest as compared to toothpaste with fluoride (p = 0.0087*), herbal toothpaste (p = 0.9034), and toothpaste with calcium sucrose phosphate (p = 0.0002*).ConclusionAll the four toothpastes showed an adequate amount of remineralization but the highest amount of remineralization potential was seen with toothpaste containing zinc hydroxyapatite.Clinical significanceThe clinical significance of our study is to give clear and evidence-based idea to the dentist and the parents about the remineralizing toothpaste available in the market for children. Treating demineralization at an initial stage would help to remineralize the tooth structure and thereby limit further destruction of the tooth structure which will preserve the beautiful smile of the child.How to cite this articleSoneta SP, Hugar SM, Hallikerimath S, et al. A Comparative Evaluation of Remineralizing Potential of Commonly Used Fluoridated Toothpaste, Herbal Toothpaste, Toothpaste with Zinc Hydroxyapatite, and Toothpaste with Calcium Sucrose Phosphate in Children: A Scanning Electronic Microscopic Study. Int J Clin Pediatr Dent 2022;15(S-2):S158-S163.