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Objectives: Among the provisions within the Affordable Care Act (ACA), expanding Medicaid was arguably the greatest contributor to increasing access to care. For over a decade, researchers have investigated how Medicaid expansion impacted cancer outcomes. Over this same decade, statistical theory illuminated how state-based policy research could be compromised by invalid inference. After reviewing the literature to identify the inference strategies of state-based cancer registry Medicaid expansion research, this study aimed to assess how inference decisions could change the interpretation of Medicaid expansion's impact on staging, treatment, and mortality in cancer patients., Data Sources: Cancer case data (2000-2019) was obtained from the Surveillance, Epidemiology, End Results (SEER) programme. Cases included all cancer sites combined, top 10 cancer sites combined, and three screening amenable cancers (colorectal, female breast, female cervical)., Study Design: A Difference-in-Differences design estimated the association between Medicaid expansion and four binary outcomes: distant stage, initiating treatment >1 month after diagnosis, no surgery recommendation, and death. Three inference techniques were compared: (1) traditional, (2) cluster, and (3) Wild Cluster Bootstrap., Data Collection: Data was accessed via SEER*Stat., Principal Findings: Estimating standard errors via traditional inference would suggest that Medicaid expansion was associated with delayed treatment initiation and surgery recommendations. Traditional and clustered inference also suggested that Medicaid expansion reduced mortality. Inference using Wild Cluster Bootstrap techniques never rejected the null hypotheses., Conclusions: This study reiterates the importance of explicit inference. Future state-based, cancer policy research can be improved by incorporating emerging techniques. These findings warrant caution when interpreting prior SEER research reporting significant effects of Medicaid expansion on cancer outcomes, especially studies that did not explicitly define their inference strategy., (© 2024 The Author(s). Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

Maximum likelihood inference can often become computationally intensive when performing joint modeling of longitudinal and time-to-event data, due to the intractable integrals in the joint likelihood function. The computational challenges escalate further when modeling HIV-1 viral load data, owing to the nonlinear trajectories and the presence of left-censored data resulting from the assay's lower limit of quantification. In this paper, for a joint model comprising a nonlinear mixed-effect model and a Cox Proportional Hazards model, we develop a computationally efficient Stochastic EM (StEM) algorithm for parameter estimation. Furthermore, we propose a novel technique for fast standard error estimation, which directly estimates standard errors from the results of StEM iterations and is broadly applicable to various joint modeling settings, such as those containing generalized linear mixed-effect models, parametric survival models, or joint models with more than two submodels. We evaluate the performance of the proposed methods through simulation studies and apply them to HIV-1 viral load data from six AIDS Clinical Trials Group studies to characterize viral rebound trajectories following the interruption of antiretroviral therapy (ART), accounting for the informative duration of off-ART periods., (© The Author 2024. Published by Oxford University Press. All rights reserved. For Permissions, email: journals.permissions@oup.com.)

Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to be fully addressed. To tackle this issue, the present study proposed an updated supplemented expectation maximization (USEM) method and a bootstrap method for SE estimation. These two methods were compared in terms of SE recovery accuracy. The simulation results demonstrated that the GVEM algorithm with bootstrap and item priors (GVEM-BSP) outperformed the other methods, exhibiting less bias and relative bias for SE estimates under most conditions. Although the GVEM with USEM (GVEM-USEM) was the most computationally efficient method, it yielded an upward bias for SE estimates., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

In Australia, trifluralin is one of the commonly used herbicides to manage annual grasses and some broadleaf weeds. However, it may have some ecosystem impacts such as high toxicity to terrestrial and aquatic life, so it is vital to monitor the degradation of trifluralin for a considerable period for environmental safety. For risk assessment purposes, it is necessary to estimate the half-life of trifluralin, which is often evaluated using derived mathematical dissipation models. In the literature, bi-exponential (BEXP) and gamma models were suggested for modelling the dissipation of trifluralin in soil. Both models provide the half-life estimate without discussing the uncertainty of the estimate, which is a shortcoming in the literature. In this paper, we used simulation to illustrate the importance of estimate's uncertainty (standard error) and demonstrated a method to compute the standard error for the half-life estimate mathematically for kinetic dissipation models. Later, we evaluated the performance of the two suggested models using statistical indices. The computation of the half-life and the standard error of the half-life estimate were discussed. This allows us to describe the inference of the half-life parameter and determine whether the half-life estimates are significantly different against the co-variate (moisture) levels. We demonstrated the method to calculate the standard error of the half-life of trifluralin, which allows us to determine the statistical difference between the estimates. In this study, we found that the half-life of trifluralin in soil tends to increase with increasing moisture levels, and the half-life of trifluralin in soil with 100% moisture level is significantly greater than 40% and 70% moisture levels. Our findings suggest that soil moisture levels should be carefully considered before trifluralin application to minimize the non-target environmental damage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Competing Interests: Declaration of competing interest None.

Background and Objective: Mediation analysis is used to gain insight into the mechanisms of exposure-outcome effects by dividing this effect into a direct and an indirect effect. One of the problems of mediation analysis is that in many situations, the standard error of the indirect effect is much lower than the standard errors of the total and direct effect. Because this problem is ignored in the epidemiological literature, the purpose of this paper was to illustrate this problem and to provide an advice regarding the statistical testing of indirect effects in mediation analysis., Methods: To illustrate the problem of the estimation of the standard error of the indirect effect two real life datasets and several simulations are used., Results: The paper shows that the problem of estimating the standard error of the indirect effect was most pronounced when the relationship between exposure and mediator and the relationship between mediator and outcome were equally strong. Furthermore, the magnitude of the estimation problem is different for different strengths of the mediation effect., Conclusion: The indirect effect in mediation analysis should not be tested for statistical significance but the importance of mediation should be evaluated by its clinical relevance., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Prenatal drug exposure is a public health problem, which results in profound behavioral problems during childhood and adolescence, mainly represented by an increase in the risk of cocaine abuse at an early age. In rodents, prenatal and postnatal cocaine exposure enhanced locomotor activity and cocaine- or nicotine-induced locomotor sensitization. Various authors consider that the adverse emotional states (anxiety and depression) that occur during cocaine withdrawal are the main factors that precipitate, relapse, and increase chronic cocaine abuse, which could increase the risk of relapse of cocaine abuse. Therefore, the objective of this study was to characterize anxiety- and depression-like behaviors at different times (30, 60, 90, and 120 days) of cocaine withdrawal in rats born to females exposed prenatally and postnatally to cocaine. A group of pregnant female Wistar rats were administered daily from day GD0 to GD21 with cocaine (cocaine preexposure group), and another group of pregnant female rats was administered daily with saline (saline preexposure group). Of the litters resulting from the cocaine-pre-exposed and saline-pre-exposed pregnant female groups, only the male rats were used for the recording of the anxiety- and depression-like behaviors at different times (30, 60, 90, and 120 days) of cocaine withdrawal The study found that prenatal and postnatal cocaine exposure dose-dependent enhanced anxiety- and depression-like behaviors. This suggests that prenatal and postnatal cocaine exposure can result in enhanced vulnerability to cocaine abuse in young and adult humans., (© 2024 Wiley Periodicals LLC.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Biological assays are essential tools in biomedical and pharmaceutical research. In simplest terms, such an assay is an analytical method used to measure or predict a response in a biological system in the presence of a given stimulus (e.g., drug). The inherent complexity involved in evaluating a biological system requires the use of rigorous and appropriate tools for data analysis. Linear and nonlinear regression models represent critically important statistical analyses used to define the relationships between variables of interest in biological systems. Recent challenges relating to the reproducibility of published data suggest the absence of standardized and routine use of statistics to support experimental results across a wide range of scientific disciplines. The current situation warrants an introductory review of basic regression concepts using current, practical examples, along with references to in-depth resources. The goal is to provide the necessary information to help standardize the analysis of biological assays in academic research and drug discovery and development, elevating their utility and increasing data transparency and reproducibility. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC., (© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.)

The partial correlation coefficient quantifies the relationship between two variables while taking into account the effect of one or multiple control variables. Researchers often want to synthesize partial correlation coefficients in a meta-analysis since these can be readily computed based on the reported results of a linear regression analysis. The default inverse variance weights in standard meta-analysis models require researchers to compute not only the partial correlation coefficients of each study but also its corresponding sampling variance. The existing literature is diffuse on how to estimate this sampling variance, because two estimators exist that are both widely used. We critically reflect on both estimators, study their statistical properties, and provide recommendations for applied researchers. We also compute the sampling variances of studies using both estimators in a meta-analysis on the partial correlation between self-confidence and sports performance., (© 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)

We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of the outcome and other studies report the sample median along with the first and third quartiles and/or minimum and maximum values. To perform meta-analysis in this context, a number of approaches have recently been developed to impute the sample mean and standard deviation from studies reporting medians. Then, standard meta-analytic approaches with inverse-variance weighting are applied based on the (imputed) study-specific sample means and standard deviations. In this article, we illustrate how this common practice can severely underestimate the within-study standard errors, which results in poor coverage for the pooled mean in common effect meta-analyses and overestimation of between-study heterogeneity in random effects meta-analyses. We propose a straightforward bootstrap approach to estimate the standard errors of the imputed sample means. Our simulation study illustrates how the proposed approach can improve the estimation of the within-study standard errors and consequently improve coverage for the pooled mean in common effect meta-analyses and estimation of between-study heterogeneity in random effects meta-analyses. Moreover, we apply the proposed approach in a meta-analysis to identify risk factors of a severe course of COVID-19.

Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Computing confidence intervals around generalizability coefficients has long been a challenging task in generalizability theory. This is a serious practical problem because generalizability coefficients are often computed from designs where some facets have small sample sizes, and researchers have little guide regarding the trustworthiness of the coefficients. As generalizability theory can be framed to a linear mixed-effect model (LMM), bootstrap and simulation techniques from LMM paradigm can be used to construct the confidence intervals. The purpose of this research is to examine four different LMM-based methods for computing the confidence intervals that have been proposed and to determine their accuracy under six simulated conditions based on the type of test scores (normal, dichotomous, and polytomous data) and data measurement design ( p × i × r and p × [ i:r ]). A bootstrap technique called "parametric methods with spherical random effects" consistently produced more accurate confidence intervals than the three other LMM-based methods. Furthermore, the selected technique was compared with model-based approach to investigate the performance at the levels of variance components via the second simulation study, where the numbers of examines, raters, and items were varied. We conclude with the recommendation generalizability coefficients, the confidence interval should accompany the point estimate., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

Statistical analysis of data is an integral part of research projects in all scientific disciplines including the plant pathology. Appropriate design, application and interpretation of statistical analysis are also, therefore, at the center of publishing and properly evaluating studies in plant pathology. A survey of research works published in the Plant Pathology Journal, however, cast doubt on high standard of statistical analysis required for scientific rigor and reproducibility in the journal. Here I first describe, based on the survey of published works, what mistakes are commonly made and what components are often lacking during statistical analysis and interpretation of its results. Next, I provide possible remedies and suggestions to help guide researchers in preparing manuscript and reviewers in evaluating manuscripts submitted to the Plant Pathology Journal. This is not aiming at delineating technical and practical details of particular statistical methods or approaches.

In the last decade, temporal dominance of sensations (TDS) methods have proven to be potent approaches in the field of food sciences. Accordingly, thus far, methods for analyzing TDS curves, which are the major outputs of TDS methods, have been developed. This study proposes a method of bootstrap resampling for TDS tasks. The proposed method enables the production of random TDS curves to estimate the uncertainties, that is, the 95% confidence interval and standard error of the curves. Based on Monte Carlo simulation studies, the estimated uncertainties are considered valid and match those estimated by approximated normal distributions with the number of independent TDS tasks or samples being 50-100 or greater. The proposed resampling method enables researchers to apply statistical analyses and machine-learning approaches that require a large sample size of TDS curves., Competing Interests: The author declares no conflict of interest.

In this study, the delta method was applied to estimate the standard errors of the true score equating when using the characteristic curve methods with the generalized partial credit model in test equating under the context of the common-item nonequivalent groups equating design. Simulation studies were further conducted to compare the performance of the delta method with that of the bootstrap method and the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion empirical standard errors as well as those yielded by the bootstrap method and the multiple imputation method under all the manipulated conditions., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments allow us to estimate model parameters, other basic problems such as estimating the marginal likelihood, a fundamental tool in Bayesian model selection, remain challenging. This is an important scientific limitation because testing psychological hypotheses with hierarchical models has proven difficult with current model selection methods. We propose an efficient method for estimating the marginal likelihood for models where the likelihood is intractable, but can be estimated unbiasedly. It is based on first running a sampling method such as MCMC to obtain samples for the model parameters, and then using these samples to construct the proposal density in an importance sampling (IS) framework with an unbiased estimate of the likelihood. Our method has several attractive properties: it generates an unbiased estimate of the marginal likelihood, it is robust to the quality and target of the sampling method used to form the IS proposals, and it is computationally cheap to estimate the variance of the marginal likelihood estimator. We also obtain the convergence properties of the method and provide guidelines on maximizing computational efficiency. The method is illustrated in two challenging cases involving hierarchical models: identifying the form of individual differences in an applied choice scenario, and evaluating the best parameterization of a cognitive model in a speeded decision making context. Freely available code to implement the methods is provided. Extensions to posterior moment estimation and parallelization are also discussed.

Equilibrium binding constants (K b ) between chemical compounds and target proteins or between interacting proteins provide a quantitative understanding of biological interaction mechanisms. Reported uncertainties of measured experimental parameters are critical for decision-making in many scientific areas, e.g., in lead compound discovery processes and in comparing computational predictions with experimental results. Uncertainties in measured K b values are commonly represented by a symmetric normal distribution, often quoted in terms of the experimental value plus-minus the standard deviation. However, in general, the distributions of measured K b (and equivalent K d ) values and the corresponding free energy change ΔG b are all asymmetric to varying degree. Here, using a simulation approach, we illustrate the effect of asymmetric K b distributions within the realm of isothermal titration calorimetry (ITC) experiments. Further we illustrate the known, but perhaps not widely appreciated, fact that when distributions of any of K b , K d and ΔG b are transformed into each other, their degree of asymmetry is changed. Consequently, we recommend that a more accurate way of expressing the uncertainties of K b , K d , and ΔG b values is to consistently report 95% confidence intervals, in line with other authors' suggestions. The ways to obtain such error ranges are discussed in detail and exemplified for a binding reaction obtained by ITC.

Stress is the most important proximal precipitant of depression, yet most large genome-wide association studies (GWAS) do not include stress as a variable. Here, we review how gene × environment (G × E) interaction might impede the discovery of genetic factors, discuss two examples of G × E interaction in depression and addiction, studies incorporating high-stress environments, as well as upcoming waves of genome-wide environment interaction studies (GWEIS). We discuss recent studies which have shown that genetic distributions can be affected by social factors such as migrations and socioeconomic background. These distinctions are not just academic but have practical consequences. Owing to interaction with the environment, genetic predispositions to depression should not be viewed as unmodifiable destiny. Patients may genetically differ not just in their response to drugs, as in the now well-recognised field of pharmacogenetics, but also in how they react to stressful environments and how they are affected by behavioural therapies.

Objective: This study attempted to clarify the applicability of standard error (SE) terms in clinical research when examining the impact of short-term practice effects on cognitive performance via reliable change methodology., Method: This study compared McSweeney's SE of the estimate (SEest) to Crawford and Howell's SE for prediction of the regression (SEpred) using a developmental sample of 167 participants with either normal cognition or mild cognitive impairment (MCI) assessed twice over 1 week. One-week practice effects in older adults: Tools for assessing cognitive change. Using these SEs, previously published standardized regression-based (SRB) reliable change prediction equations were then applied to an independent sample of 143 participants with MCI., Results: This clinical developmental sample yielded nearly identical SE values (e.g., 3.697 vs. 3.719 for HVLT-R Total Recall SEest and SEpred, respectively), and the resultant SRB-based discrepancy z scores were comparable and strongly correlated (r = 1.0, p < .001). Consequently, observed follow-up scores for our sample with MCI were consistently below expectation compared to predictions based on Duff's SRB algorithms., Conclusions: These results appear to replicate and extend previous work showing that the calculation of the SEest and SEpred from a clinical sample of cognitively intact and MCI participants yields similar values and can be incorporated into SRB reliable change statistics with comparable results. As a result, neuropsychologists utilizing reliable change methods in research investigation (or clinical practice) should carefully balance mathematical accuracy and ease of use, among other factors, when determining which SE metric to use., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

This reflection presents a discussion of some common measures of variability and how they are appropriately used in descriptive and inferential statistical analyses. We argue that confidence intervals (CIs), which incorporate these measures, serve as tools to assess both clinical and statistical significance.

This study revisits the parameter estimation issues in multidimensional item response theory more thoroughly and investigates some computation details that have seldom been addressed previously when implementing the expectation-maximization (EM) algorithm for finite mixtures (EM-FM). Two research questions are: Should we rescale after each EM cycle or after the final EM cycle? How to adapt the supplemented EM algorithm to the EM-FM framework to estimate standard errors (SEs) of all unknown parameters? Analytic details of the methods are provided, and a comprehensive simulation study is conducted to provide supporting evidence. Results reveal that rescaling after each EM cycle accelerates convergence without affecting the calibration accuracy. Moreover, the SEs of all model parameters, including item parameters and population mixing proportions, recover well when the sample size is relatively large (e.g., 2000).

Concerns have been expressed over standards of statistical interpretation. Results with P <0.05 are often referred to as 'significant' which, in plain English, implies important. This leads some people directly into the misconception that this provides proof that associations are clinically relevant. There are calls for statistics educators to respond to these concerns. This article provides novel plain English interpretations that are designed to deepen understanding. Experience teaching postgraduates at Imperial College is discussed. A key issue with focusing on 'significance' is the common inappropriate practice of implying no association exists, simply because P >0.05. Referring to strengths of association in 'study participants' gives them gravitas, which may help to avoid this. This contrasts with the common practice of focusing on imprecision, by referring to the 'sample' and to 'point estimates'. Unlike formal statistical definitions, interpretations developed and presented here are rooted in the application of statistics. They are based on one set of study participants (not many random samples). Precision of strengths of association are based on using strengths in study participants to estimate strengths of association in the population (from which participants were selected by probability random sampling). Reference to 'compatibility with study data, dependent on statistical modelling assumptions' reminds us of the importance of data quality and modelling assumptions. A straightforward graph shows the relationship between P-values and test statistics. This figure and associated interpretations were developed to illuminate the continuous nature of P-values. This is designed to discourage focus on whether P <0.05, and encourage interpretation of exact P-values., (© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Many authors are unsure of whether to present the mean along with the standard deviation (SD) or along with the standard error of the mean (SEM). The SD is a descriptive statistic that estimates the scatter of values around the sample mean; hence, the SD describes the sample. In contrast, the SEM is an estimate of how close the sample mean is to the population mean; it is an intermediate term in the calculation of the 95% confidence interval around the mean, and (where applicable) statistical significance; the SEM does not describe the sample. Therefore, the mean should always be accompanied by the SD when describing the sample. There are many reasons why the SEM continues to be reported, and it is argued that none of these is justifiable. In fact, presentation of SEMs may mislead readers into believing that the sample data are more precise than they actually are. Given that the standard error is not presented for other parameters, such as difference between means or proportions, and difference between proportions, it is suggested that presentation of SEM values can be done away with, altogether., (© 2020 Indian Psychiatric Society - South Zonal Branch.)

This note highlights and illustrates the links between item response theory and classical test theory in the context of polytomous items. An item response modeling procedure is discussed that can be used for point and interval estimation of the individual true score on any item in a measuring instrument or item set following the popular and widely applicable graded response model. The method contributes to the body of research on the relationships between classical test theory and item response theory and is illustrated on empirical data., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2017.)

Principal components analysis (PCA) is a common method to summarize a larger set of correlated variables into a smaller and more easily interpretable axes of variation. However, the different components need to be distinct from each other to be interpretable otherwise they only represent random directions. This is a fundamental assumption of PCA and, thus, needs to be tested every time. Sample correlation matrices will always result in a pattern of decreasing eigenvalues even if there is no structure. Tests are, therefore, needed to discern real patterns from illusionary ones. Furthermore, the loadings of the vectors need to be larger than expected by random data to be useful in the calculation of PC-scores. PC-scores calculated from nondistinct PC's have very large standard errors and cannot be used for biological interpretations. I give a number of examples to illustrate the potential problems with PCA. Robustness of the PC's increases with increasing sample size but not with the number of traits. I review a few simple test statistics appropriate for testing PC's and use a real-world example to illustrate how this can be done using randomization tests. PCA can be very useful but great care is needed to avoid spurious results., (© 2019 The Author(s). Evolution © 2019 The Society for the Study of Evolution.)

Assessing publication bias is a critical procedure in meta-analyses for rating the synthesized overall evidence. Because statistical tests for publication bias are usually not powerful and only give P values that inform either the presence or absence of the bias, examining the asymmetry of funnel plots has been popular to investigate potentially missing studies and the direction of the bias. Most funnel plots present treatment effects against their standard errors, and the contours depicting studies' significance levels have been used in the plots to distinguish publication bias from other factors (such as heterogeneity and subgroup effects) that may cause the plots' asymmetry. However, treatment effects and their standard errors are frequently associated even if no publication bias exists (eg, both variables depend on the four data cells in a 2 × 2 table for the odds ratio), so standard-error-based funnel plots may lead to false positive conclusions when such association may not be negligible. In addition, the missingness of studies may relate to their sample sizes besides P values (which are partly determined by standard errors); studies with more samples are more likely published. Therefore, funnel plots based on sample sizes can be an alternative tool. However, the contours for standard-error-based funnel plots cannot be directly applied to sample-size-based ones. This article introduces contours for sample-size-based funnel plots of various effect sizes, which may help meta-analysts properly interpret such plots' asymmetry. We provide five examples to illustrate the use of the proposed contours., (© 2019 John Wiley & Sons, Ltd.)

In computerized adaptive testing (CAT), a variable-length stopping rule refers to ending item administration after a pre-specified measurement precision standard has been satisfied. The goal is to provide equal measurement precision for all examinees regardless of their true latent trait level. Several stopping rules have been proposed in unidimensional CAT, such as the minimum information rule or the maximum standard error rule. These rules have also been extended to multidimensional CAT and cognitive diagnostic CAT, and they all share the same idea of monitoring measurement error. Recently, Babcock and Weiss (J Comput Adapt Test 2012. https://doi.org/10.7333/1212-0101001) proposed an "absolute change in theta" (CT) rule, which is useful when an item bank is exhaustive of good items for one or more ranges of the trait continuum. Choi, Grady and Dodd (Educ Psychol Meas 70:1-17, 2010) also argued that a CAT should stop when the standard error does not change, implying that the item bank is likely exhausted. Although these stopping rules have been evaluated and compared in different simulation studies, the relationships among the various rules remain unclear, and therefore there lacks a clear guideline regarding when to use which rule. This paper presents analytic results to show the connections among various stopping rules within both unidimensional and multidimensional CAT. In particular, it is argued that the CT-rule alone can be unstable and it can end the test prematurely. However, the CT-rule can be a useful secondary rule to monitor the point of diminished returns. To further provide empirical evidence, three simulation studies are reported using both the 2PL model and the multidimensional graded response model.

Building on prior research on the relationships between key concepts in item response theory and classical test theory, this note contributes to highlighting their important and useful links. A readily and widely applicable latent variable modeling procedure is discussed that can be used for point and interval estimation of the individual person true score on any item in a unidimensional multicomponent measuring instrument or item set under consideration. The method adds to the body of research on the connections between classical test theory and item response theory. The outlined estimation approach is illustrated on empirical data., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2017.)

Background: Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability., Results: In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy., Conclusions: The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.

Objective: Interviews for residency are important for resident selection, yet how to best perform the interviews remains uncertain. Multiple approaches have been described with a variety of results. Our purpose of this study was to evaluate our Obstetrics-Gynecology residency program's interview structure to determine which interview components have association with performance evaluations completed by physicians and nurses during the residents' years of training at our program., Design: Using only data from our matched residents, existing interview scores from our standard interview process were compared to performance evaluation scores completed by faculty and nursing throughout the training years. Our standard interview process consisted of 4 interviews with individual faculty, 1 structured behavioral-based interview by a blinded faculty member, and 1 interview with a pair of current residents., Setting: A single, multisite, Obstetrics-Gynecology residency program., Participants: Interviewees of the Obstetrics-Gynecology residency program that ultimately matched with our program., Results: Interview scores from 44 residents were compared to their performance evaluation scores. Positive associations were seen between performance evaluation scores and both resident teams' interviews and unstructured faculty interviews, with the resident teams' score showing a stronger degree of association. The behavioral-based interview total score did not have association with the performance evaluation scores, nor did any of the individual questions., Conclusions: Resident teams' interview scores of applicants show the strongest association with eventual performance evaluations completed by faculty and nursing during residency. This demonstrates that current residents should have a role in the resident selection process during interviews. This does not provide data to abandon behavior-based interview techniques, but rather encourage each program to carefully apply their use during the resident selection process., (Copyright © 2018 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

The relations among alternative parameterizations of the binary factor analysis (FA) model and two-parameter logistic (2PL) item response theory (IRT) model have been thoroughly discussed in literature. However, the conversion formulas widely available are mainly for transforming parameter estimates from one parameterization to another. There is a lack of discussion about the standard error (SE) conversion among different parameterizations, when SEs of IRT model parameters are often of immediate interest to practitioners. This article provides general formulas for computing the SEs of transformed parameter values, when these parameters are transformed from FA to IRT models. These formulas are suitable for unidimensional 2PL, multidimensional 2PL, and bi-factor 2PL models. A simulation study is conducted to verify the formula by providing empirical evidence. A real data example is given in the end for an illustration.

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed. Specifically, we define four COSAN-CCA models to correspond with four possible combinations of the data to be analyzed and the unique parameters to be estimated. In terms of the data, one can analyze either the unstandardized or standardized variables. In terms of the unique parameters, one can estimate either the weights or loadings. Besides the unique parameters of each COSAN-CCA model, all four COSAN-CCA models also estimate the canonical correlations as their common parameters. Taken together, the four COSAN-CCA models provide the correct point estimates and standard error estimates for all commonly used CCA parameters. Two numeric examples are used to compare the standard error estimates obtained from the MIMIC approach and the COSAN modeling approach. Moreover, the standard error estimates from the COSAN modeling approach are validated by a simulation study and the asymptotic theory. Finally, software implementation and future extensions are discussed.

We discuss different methods of sample size calculation for two independent means, aiming to provide insight into the calculation of sample size at the design stage of a parallel two-arm randomised controlled trial (RCT). We compare different methods for sample size calculation, using published results from a previous RCT. We use variances and correlation coefficients to compare sample sizes using different methods, including 1. The choice of the primary outcome measure: post-intervention score vs. change from baseline score. 2. The choice of statistical methods: t -test without using correlation coefficients vs. analysis of covariance (ANCOVA). We show that the required sample size will depend on whether the outcome measure is the post-intervention score, or the change from baseline score, with or without baseline score included as a covariate. We show that certain assumptions have to be met when using simplified sample size equations, and discuss their implications in sample size calculation when planning an RCT. We strongly recommend publishing the crucial result "mean change (SE, standard error)" in a study paper, because it allows (i) the calculation of the variance of the change score in each arm, and (ii) to pool the variances from both arms. It also enables us to calculate the correlation coefficient in each arm. This subsequently allows us to calculate sample size using change score as the outcome measure. We use simulation to demonstrate how sample sizes by different methods are influenced by the strength of the correlation.

A frequent topic of psychological research is the estimation of the correlation between two variables from a sample that underwent a selection process based on a third variable. Due to indirect range restriction, the sample correlation is a biased estimator of the population correlation, and a correction formula is used. In the past, bootstrap standard error and confidence intervals for the corrected correlations were examined with normal data. The present study proposes a large-sample estimate (an analytic method) for the standard error, and a corresponding confidence interval for the corrected correlation. Monte Carlo simulation studies involving both normal and non-normal data were conducted to examine the empirical performance of the bootstrap and analytic methods. Results indicated that with both normal and non-normal data, the bootstrap standard error and confidence interval were generally accurate across simulation conditions (restricted sample size, selection ratio, and population correlations) and outperformed estimates of the analytic method. However, with certain combinations of distribution type and model conditions, the analytic method has an advantage, offering reasonable estimates of the standard error and confidence interval without resorting to the bootstrap procedure's computer-intensive approach. We provide SAS code for the simulation studies., (© 2017 The British Psychological Society.)

ROC analysis involving two large datasets is an important method for analyzing statistics of interest for decision making of a classifier in many disciplines. And data dependency due to multiple use of the same subjects exists ubiquitously in order to generate more samples because of limited resources. Hence, a two-layer data structure is constructed and the nonparametric two-sample two-layer bootstrap is employed to estimate standard errors of statistics of interest derived from two sets of data, such as a weighted sum of two probabilities. In this article, to reduce the bootstrap variance and ensure the accuracy of computation, Monte Carlo studies of bootstrap variability were carried out to determine the appropriate number of bootstrap replications in ROC analysis with data dependency. It is suggested that with a tolerance 0.02 of the coefficient of variation, 2,000 bootstrap replications be appropriate under such circumstances.

Frequentist standard errors are a measure of uncertainty of an estimator, and the basis for statistical inferences. Frequestist standard errors can also be derived for Bayes estimators. However, except in special cases, the computation of the standard error of Bayesian estimators requires bootstrapping, which in combination with Markov chain Monte Carlo (MCMC) can be highly time consuming. We discuss an alternative approach for computing frequentist standard errors of Bayesian estimators, including importance sampling. Through several numerical examples we show that our approach can be much more computationally efficient than the standard bootstrap.

The linear ballistic accumulator (LBA) model (Brown & Heathcote, , Cogn. Psychol., 57, 153) is increasingly popular in modelling response times from experimental data. An R package, glba, has been developed to fit the LBA model using maximum likelihood estimation which is validated by means of a parameter recovery study. At sufficient sample sizes parameter recovery is good, whereas at smaller sample sizes there can be large bias in parameters. In a second simulation study, two methods for computing parameter standard errors are compared. The Hessian-based method is found to be adequate and is (much) faster than the alternative bootstrap method. The use of parameter standard errors in model selection and inference is illustrated in an example using data from an implicit learning experiment (Visser et al., , Mem. Cogn., 35, 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model., (© 2017 The British Psychological Society.)

Background: Cell image segmentation (CIS) is an essential part of quantitative imaging of biological cells. Designing a performance measure and conducting significance testing are critical for evaluating and comparing the CIS algorithms for image-based cell assays in cytometry. Many measures and methods have been proposed and implemented to evaluate segmentation methods. However, computing the standard errors (SE) of the measures and their correlation coefficient is not described, and thus the statistical significance of performance differences between CIS algorithms cannot be assessed., Results: We propose the total error rate (TER), a novel performance measure for segmenting all cells in the supervised evaluation. The TER statistically aggregates all misclassification error rates (MER) by taking cell sizes as weights. The MERs are for segmenting each single cell in the population. The TER is fully supported by the pairwise comparisons of MERs using 106 manually segmented ground-truth cells with different sizes and seven CIS algorithms taken from ImageJ. Further, the SE and 95% confidence interval (CI) of TER are computed based on the SE of MER that is calculated using the bootstrap method. An algorithm for computing the correlation coefficient of TERs between two CIS algorithms is also provided. Hence, the 95% CI error bars can be used to classify CIS algorithms. The SEs of TERs and their correlation coefficient can be employed to conduct the hypothesis testing, while the CIs overlap, to determine the statistical significance of the performance differences between CIS algorithms., Conclusions: A novel measure TER of CIS is proposed. The TER's SEs and correlation coefficient are computed. Thereafter, CIS algorithms can be evaluated and compared statistically by conducting the significance testing.

The data dependency due to multiple use of the same subjects has impact on the standard error (SE) of the detection cost function (DCF) in speaker recognition evaluation. The DCF is defined as a weighted sum of the probabilities of type I and type II errors at a given threshold. A two-layer data structure is constructed: target scores are grouped into target sets based on the dependency, and likewise for non-target scores. On account of the needed equal probabilities for scores being selected when resampling, target sets must contain the same number of target scores, and so must non-target sets. In addition to the bootstrap method with i.i.d. assumption, the nonparametric two-sample one-layer and two-layer bootstrap methods are carried out based on whether the resampling takes place only on sets, or subsequently on scores within the sets. Due to the stochastic nature of the bootstrap, the distributions of the SEs of the DCF estimated using the three different bootstrap methods are created and compared. After performing hypothesis testing, it is found that data dependency increases not only the SE but also the variation of the SE, and the two-layer bootstrap is more conservative than the one-layer bootstrap. The rationale regarding the different impacts of the three bootstrap methods on the estimated SEs is investigated.

For two-arm randomized phase II clinical trials, previous literature proposed an optimal design that minimizes the total sample sizes subject to multiple constraints on the standard errors of the estimated event rates and their difference. The original design is limited to trials with dichotomous endpoints. This paper extends the original approach to be applicable to phase II clinical trials with endpoints from the exponential dispersion family distributions. The proposed optimal design minimizes the total sample sizes needed to provide estimates of population means of both arms and their difference with pre-specified precision. Its applications on data from specific distribution families are discussed under multiple design considerations. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I error rate, and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, mean square error, power, and type I error rate., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Assessing library diversity is an important control step in a directed evolution experiment. To do this, a limited amount of colonies from a test library are sequenced and tested. In the case of an error-prone PCR library, the spectrum of the identified mutations - the proportions of mutations of a specific nucleobase to another- is calculated enabling the user to make more informed predictions on library diversity and coverage. However, the calculations of the mutational spectrum are severely affected by the limited sample sizes., Results: Here an online program, called Mutanalyst, is presented, which not only automates the calculations, but also estimates errors involved. Specifically, the errors are calculated thanks to the complementarity of DNA, which means that a mutation has a complementary mutation on the other sequence. Additionally, in the case of determining the mean number of mutations per sequence it does so by fitting to a Poisson distribution, which is more robust than calculating the average in light of the small sampling size., Conclusion: As a result of the added measures to keep into account of small sample size the user can better assess whether the library is satisfactory or whether error-prone PCR conditions should be adjusted. The program is available at www.mutanalyst.com .

This commentary is the second of a series outlining one specific concept in interpreting biomarkers data. In the first, an observational method was presented for assessing the distribution of measurements before making parametric calculations. Here, the discussion revolves around the next step, the choice of using standard error of the mean or the calculated standard deviation to compare or predict measurement results.

Bootstrapping has enormous potential in statistics education and practice, but there are subtle issues and ways to go wrong. For example, the common combination of nonparametric bootstrapping and bootstrap percentile confidence intervals is less accurate than using t -intervals for small samples, though more accurate for larger samples. My goals in this article are to provide a deeper understanding of bootstrap methods-how they work, when they work or not, and which methods work better-and to highlight pedagogical issues. Supplementary materials for this article are available online. [Received December 2014. Revised August 2015].

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided., (© 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.)

In item response theory, the classical estimators of ability are highly sensitive to response disturbances and can return strongly biased estimates of the true underlying ability level. Robust methods were introduced to lessen the impact of such aberrant responses on the estimation process. The computation of asymptotic (i.e., large-sample) standard errors (ASE) for these robust estimators, however, has not yet been fully considered. This paper focuses on a broad class of robust ability estimators, defined by an appropriate selection of the weight function and the residual measure, for which the ASE is derived from the theory of estimating equations. The maximum likelihood (ML) and the robust estimators, together with their estimated ASEs, are then compared in a simulation study by generating random guessing disturbances. It is concluded that both the estimators and their ASE perform similarly in the absence of random guessing, while the robust estimator and its estimated ASE are less biased and outperform their ML counterparts in the presence of random guessing with large impact on the item response process., (© 2013 The British Psychological Society.)

Freshwater fishes in India are poorly known and plagued by many unresolved cryptic species complexes that masks some latent and endemic species. Limitations in traditional taxonomy have resulted in this crypticism. Hence, molecular approaches like DNA barcoding, are needed to diagnose these latent species. We have analyzed 1383 barcode sequences of 175 Indian freshwater fish species available in the databases, of which 172 sequences of 70 species were generated. The congeneric and conspecific genetic divergences were calculated using Kimura's 2 parameter distance model followed by the construction of a Neighbor Joining tree using the MEGA 5.1. DNA barcoding principle at its first hand approach, led to the straightforward identification of 82% of the studied species with 2.9% (S.E=0.2) divergence between the nearest congeners. However, after validating some cases of synonymy and mislabeled sequences, 5% more species were found to be valid. Sequences submitted to the database under different names were found to represent single species. On the other hand, some sequences of the species like Barilius barna, Barilius bendelisis and Labeo bata were submitted to the database under a single name but were found to represent either some unexplored species or latent species. Overall, 87% of the available Indian freshwater fish barcodes were diagnosed as true species in parity with the existing checklist and can act as reference barcode for the particular taxa. For the remaining 13% (21 species) the correct species name was difficult to assign as they depicted some erroneous identification and cryptic species complex. Thus, these barcodes will need further assay and inclusion of barcodes of more specimens from same and sister species., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Insect chemosensory proteins (CSPs) are proposed to capture and transport hydrophobic chemicals across the sensillum lymph to olfactory receptors (ORs), but this has not been clarified in moths. In this study, we built on our previously reported segment sequence work and cloned the full length CSP19 gene (SinfCSP19) from the antennae of Sesamia inferens by using rapid amplification of cDNA ends. Quantitative real time-PCR (qPCR) assays indicated that the gene was expressed in a unique profile, i.e. predominant in antennae and significantly higher in male than in female. To explore the function, recombinant SinfCSP19 was expressed in Escherichia coli cells and purified by Ni-ion affinity chromatography. Binding affinities of the recombinant SinfCSP19 with 39 plant volatiles, 3 sex pheromone components and 10 pheromone analogs were measured using fluorescent competitive binding assays. The results showed that 6 plant volatiles displayed high binding affinities to SinfCSP19 (Ki = 2.12-8.75 μM), and more interesting, the 3 sex pheromone components and analogs showed even higher binding to SinfCSP19 (Ki = 0.49-1.78 μM). Those results suggest that SinfCSP19 plays a role in reception of female sex pheromones of S. inferens and host plant volatiles., (Copyright © 2013 Elsevier B.V. All rights reserved.)