CFA with binary variables in small samples: a comparison of two methods.
Ontology highlight
ABSTRACT:
SUBMITTER: Savalei V
PROVIDER: S-EPMC4285741 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
CFA with binary variables in small samples: a comparison of two methods.
Savalei Victoria V Bonett Douglas G DG Bentler Peter M PM
Frontiers in psychology 20150107
Asymptotically optimal correlation structure methods with binary data can break down in small samples. A new correlation structure methodology based on a recently developed odds-ratio (OR) approximation to the tetrachoric correlation coefficient is proposed as an alternative to the LPB approach proposed by Lee et al. (1995). Unweighted least squares (ULS) estimation with robust standard errors and generalized least squares (GLS) estimation methods were compared. Confidence intervals and tests fo ...[more]
Similar Datasets
Project description:We report performance of six different protocols for small RNAseq library preparation and of a method utilizing sequencing of probes targeting microRNAs (HTG EdgeSeq). Recently, small RNA sequencing (small RNA-seq) has been introduced as a method for quantifying circulating microRNAs (miRNAs) and enabling their global profiling without prior knowledge of target sequences. Despite its great promise, small RNA-seq has not delivered the expected outcomes, particularly due to ligation and PCR bias introduced within the workflow. In this study, we assessed the performance of all existing approaches to the small RNA-seq of miRNAs in plasma samples: original two adapter ligation approach; single adapter ligation with subsequent circularization; polyadenylation; use of randomized adapters; and use of unique molecular identifiers (UMI). Using comprehensive set of metrics, we evaluated each protocol in terms of yield, precision, accuracy, sensitivity, and ability to detect isomiRs. Moreover, we assessed performance of targeted RNA-seq method utilizing hybridization probes across relevant metrics and together with RT-qPCR we used it as a reference for accuracy evaluation. The best results were delivered by targeted RNA-seq outperforming other methods in all relevant parameters. The protocols using randomized adapters or UMIs showed consistent good performance across all of the assessed metrics. In contrast, the polyadenylation approach generated a high percentage of discarded reads and impeded the analysis of isomiRs. The single adapter ligation with subsequent circularization failed to prevent ligation bias and the traditional two adapter ligation approach achieved the worse scores in the majority of tested metrics. To sum, we provide a comprehensive comparison that can serve as a guide for new users interested in analysis of circulating miRNAs and as a reference for further comparative studies.
2020-12-31 | GSE149513 | GEO
Project description:Developed and validated analytical methods for the determination of a wide spectrum of pesticide residues in honey and honeybee samples after the modification of QuEChERS extraction in combination with gas chromatography?tandem quadrupole mass spectrometry (GC-MS/MS) and liquid chromatography?tandem quadrupole mass spectrometry (LC-MS/MS) were discussed and compared. The developed methods were evaluated regarding the utilized equipment and reagents using Eco-Scale and compared in terms of extraction time, accuracy, precision, sensitivity and versatility, with similar procedures. The results proved that the QuEChERS protocol in combination with LC and GC techniques fulfills the requirements of green analytical chemistry, so it can be used as a tool in environmental monitoring. The recovery was 85?116% for honey and 85.5?103.5% for honeybee samples. The developed methods were successfully applied in monitoring real samples collected from three districts of Pomerania in Poland. Analysis of real samples revealed the presence of the following pesticides: bifenthrin, fenpyroximate, methidathione, spinosad, thiamethoxam, triazophos, metconazole and cypermethrin at levels higher than the MRLs established by the EU.
| S-EPMC6222677 | biostudies-literature
Project description:In intervention studies having multiple outcomes, researchers often use a series of univariate tests (e.g., ANOVAs) to assess group mean differences. Previous research found that this approach properly controls Type I error and generally provides greater power compared to MANOVA, especially under realistic effect size and correlation combinations. However, when group differences are assessed for a specific outcome, these procedures are strictly univariate and do not consider the outcome correlations, which may be problematic with missing outcome data. Linear mixed or multivariate multilevel models (MVMMs), implemented with maximum likelihood estimation, present an alternative analysis option where outcome correlations are taken into account when specific group mean differences are estimated. In this study, we use simulation methods to compare the performance of separate independent samples t tests estimated with ordinary least squares and analogous t tests from MVMMs to assess two-group mean differences with multiple outcomes under small sample and missingness conditions. Study results indicated that a MVMM implemented with restricted maximum likelihood estimation combined with the Kenward-Roger correction had the best performance. Therefore, for intervention studies with small N and normally distributed multivariate outcomes, the Kenward-Roger procedure is recommended over traditional methods and conventional MVMM analyses, particularly with incomplete data.
| S-EPMC7093229 | biostudies-literature
Project description:BackgroundMultiple imputation (MI) was developed as a method to enable valid inferences to be obtained in the presence of missing data rather than to re-create the missing values. Within the applied setting, it remains unclear how important it is that imputed values should be plausible for individual observations. One variable type for which MI may lead to implausible values is a limited-range variable, where imputed values may fall outside the observable range. The aim of this work was to compare methods for imputing limited-range variables, with a focus on those that restrict the range of the imputed values.MethodsUsing data from a study of adolescent health, we consider three variables based on responses to the General Health Questionnaire (GHQ), a tool for detecting minor psychiatric illness. These variables, based on different scoring methods for the GHQ, resulted in three continuous distributions with mild, moderate and severe positive skewness. In an otherwise complete dataset, we set 33% of the GHQ observations to missing completely at random or missing at random; repeating this process to create 1000 datasets with incomplete data for each scenario.For each dataset, we imputed values on the raw scale and following a zero-skewness log transformation using: univariate regression with no rounding; post-imputation rounding; truncated normal regression; and predictive mean matching. We estimated the marginal mean of the GHQ and the association between the GHQ and a fully observed binary outcome, comparing the results with complete data statistics.ResultsImputation with no rounding performed well when applied to data on the raw scale. Post-imputation rounding and imputation using truncated normal regression produced higher marginal means than the complete data estimate when data had a moderate or severe skew, and this was associated with under-coverage of the complete data estimate. Predictive mean matching also produced under-coverage of the complete data estimate. For the estimate of association, all methods produced similar estimates to the complete data.ConclusionsFor data with a limited range, multiple imputation using techniques that restrict the range of imputed values can result in biased estimates for the marginal mean when data are highly skewed.
| S-EPMC4021274 | biostudies-literature
Project description:BackgroundRapid diagnosis of COVID-19 is essential in order to restrict the spread of the pandemic, and different approaches for SARS-CoV-2 testing have been proposed as cost-effective and less time-consuming alternatives. For virus detection, the real-time reverse transcriptase-polymerase chain reaction (RT-PCR) technique is still the "gold standard" for accuracy and reliability, but its performance is affected by the efficiency of nucleic acid extraction methods.ObjectiveIn order to improve the SARS-CoV-2 diagnostic workflow, we compared a "standard" commercially available kit, based on viral RNA extraction from human swab samples by magnetic beads, with its technological evolution. The two methods differ mainly in their time consumption (9 vs. 35 min).MethodsWe adopted the MAGABIO PLUS VIRUS DNA/RNA PURIFICATION KIT II (BIOER), defined as "standard", with the automatic extractor BIOER (GenePure Pro fully automatic nucleic acid purification system) to isolate RNA from nasopharyngeal swabs for the detection of SARS-CoV-2 by RT-PCR. We tested this kit with a new faster version of the first one, defined as "rapid" (MAGABIO PLUS VIRUS RNA PURIFICATION KIT II).Results and conclusionThe two evaluated procedures provided similar analytical results, but the faster method proved to be a more suitable tool for the detection of SARS-CoV-2 from nasopharyngeal swabs, due to a more rapid availability of results, which may contribute to improving both clinical decision making and patient safety.
| S-EPMC9317615 | biostudies-literature
Project description:Several methods have been proposed for partially or point identifying the average treatment effect (ATE) using instrumental variable (IV) type assumptions. The descriptions of these methods are widespread across the statistical, economic, epidemiologic, and computer science literature, and the connections between the methods have not been readily apparent. In the setting of a binary instrument, treatment, and outcome, we review proposed methods for partial and point identification of the ATE under IV assumptions, express the identification results in a common notation and terminology, and propose a taxonomy that is based on sets of identifying assumptions. We further demonstrate and provide software for the application of these methods to estimate bounds. Supplementary materials for this article are available online.
| S-EPMC6752717 | biostudies-literature
Project description:This study aimed at finding out the most effective clinical samples and methods in chronic cutaneous leishmaniasis (CCL). Smear, aspiration fluid, and filter paper samples were taken from 104 skin lesions of suspected cases with CCL, and they were compared using microscopic examination, culture, and molecular methods. We characterized four different forms of CCL and identified the causative agents in CCL forms using high-resolution melting curve real-time polymerase chain reaction assay. We observed that smear was detected to be the most sensitive (63.5%) among clinical samples, and real-time polymerase chain reaction method was the most sensitive (96.8%) among the methods used in diagnosis of CCL. We identified 68.8% Leishmania tropica and 31.2% L. infantum in papular lesions, 69.2% L. infantum and 30.8% L. tropica in nodular lesions, 57.9% L. tropica and 42.1% L. major in ulcerating plaque lesions, and 55.5% L. tropica and 44.5% L. major in noduloulcerative lesions in CCL patients.
| S-EPMC4228882 | biostudies-literature
Project description:Snake venom is an extremely interesting natural mixture of proteins and peptides, characterized by both high diversity and high pharmacological potential. Much attention has been paid to the study of venom composition of different species and also detailed analysis of the properties of individual components. Since proteins and peptides are the active ingredients in venom, rapidly developing proteomic techniques are used to analyze them. During such analyses, one of the routine operations is to measure the protein concentration in the sample. The aim of this study was to compare five methods used to measure protein content in venoms of two snake species: the Viperids representative, Agkistrodon contortrix, and the Elapids representative, Naja ashei. The study showed that for A. contortrix venom, the concentration of venom protein measured by four methods is very similar and only the NanoDrop method clearly stands out from the rest. However, in the case of N. ashei venom, each technique yields significantly different results. We hope that this report will help to draw attention to the problem of measuring protein concentration, especially in such a complex mixture as animal venoms.
| S-EPMC7142616 | biostudies-literature
Project description:Misclassification is a long-standing statistical problem in epidemiology. In many real studies, either an exposure or a response variable or both may be misclassified. As such, potential threats to the validity of the analytic results (e.g., estimates of odds ratios) that stem from misclassification are widely discussed in the literature. Much of the discussion has been restricted to the nondifferential case, in which misclassification rates for a particular variable are assumed not to depend on other variables. However, complex differential misclassification patterns are common in practice, as we illustrate here using bacterial vaginosis and Trichomoniasis data from the HIV Epidemiology Research Study (HERS). Therefore, clear illustrations of valid and accessible methods that deal with complex misclassification are still in high demand. We formulate a maximum likelihood (ML) framework that allows flexible modeling of misclassification in both the response and a key binary exposure variable, while adjusting for other covariates via logistic regression. The approach emphasizes the use of internal validation data in order to evaluate the underlying misclassification mechanisms. Data-driven simulations show that the proposed ML analysis outperforms less flexible approaches that fail to appropriately account for complex misclassification patterns. The value and validity of the method are further demonstrated through a comprehensive analysis of the HERS example data.
| S-EPMC4418038 | biostudies-literature
Project description:Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.
| S-EPMC4626630 | biostudies-literature