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The role of neoadjuvant chemoradiotherapy and/or chemotherapy (neoCHT) in patients with pancreatic ductal adenocarcinoma (PDAC) is poorly defined. We hypothesized that patients who underwent neoadjuvant therapy (NAT) would have improved systemic therapy delivery, as well as comparable perioperative complications, compared to patients undergoing upfront resection. This is an IRB-approved retrospective study of potentially resectable PDAC patients treated within an academic quaternary referral center between 2011 and 2018. Data were abstracted from the electronic medical record using an institutional cancer registry and the National Surgical Quality Improvement Program. Three hundred and fourteen patients were eligible for analysis and eighty-one patients received NAT. The median overall survival (OS) was significantly improved in patients who received NAT (28.6 vs. 20.1 months, p = 0.014). Patients receiving neoCHT had an overall increased mean duration of systemic therapy (p < 0.001), and the median OS improved with each month of chemotherapy delivered (HR = 0.81 per month CHT, 95% CI (0.76–0.86), p < 0.001). NAT was not associated with increases in early severe post-operative complications (p = 0.47), late leaks (p = 0.23), or 30–90 day readmissions (p = 0.084). Our results show improved OS in patients who received NAT, driven largely by improved chemotherapy delivery, without an apparent increase in early or late perioperative complications compared to patients undergoing upfront resection.

The Mantel–Haenszel estimator is one of the most popular techniques for measuring differential item functioning (DIF). A generalization of this estimator is applied to the context of DIF to compare items by taking the covariance of odds ratio estimators between dependent items into account. Unlike the Item Response Theory, the method does not rely on the local item independence assumption which is likely to be violated when one item provides clues about the answer of another item. Furthermore, we use these (co)variance estimators to construct a hypothesis test to assess DIF for multiple items simultaneously. A simulation study is presented to assess the performance of several tests. Finally, the use of these DIF tests is illustrated via application to two real data sets.

Background Free Open-Access Medical education (FOAM) use among residents continues to rise. However, it often lacks quality assurance processes and residents receive little guidance on quality assessment. The Academic Life in Emergency Medicine Approved Instructional Resources tool (AAT) was created for FOAM appraisal by and for expert educators and has demonstrated validity in this context. It has yet to be evaluated in other populations. Objectives We assessed the AAT's usability in a diverse population of practicing emergency medicine (EM) physicians, residents, and medical students; solicited feedback; and developed a revised tool. Methods As part of the Medical Education Translational Resources: Impact and Quality (METRIQ) study, we recruited medical students, EM residents, and EM attendings to evaluate five FOAM posts with the AAT and provide quantitative and qualitative feedback via an online survey. Two independent analysts performed a qualitative thematic analysis with discrepancies resolved through discussion and negotiated consensus. This analysis informed development of an initial revised AAT, which was then further refined after pilot testing among the author group. The final tool was reassessed for reliability. Results Of 330 recruited international participants, 309 completed all ratings. The Best Evidence in Emergency Medicine (BEEM) score was the component most frequently reported as difficult to use. Several themes emerged from the qualitative analysis: for ease of use-understandable, logically structured, concise, and aligned with educational value. Limitations include deviation from questionnaire best practices, validity concerns, and challenges assessing evidence-based medicine. Themes supporting its use include evaluative utility and usability. The author group pilot tested the initial revised AAT, revealing a total score average measure intraclass correlation coefficient (ICC) of moderate reliability (ICC = 0.68, 95% confidence interval [CI] = 0 to 0.962). The final AAT's average measure ICC was 0.88 (95% CI = 0.77 to 0.95). Conclusions We developed the final revised AAT from usability feedback. The new score has significantly increased usability, but will need to be reassessed for reliability in a broad population.

By modeling the effects of predictor variables as a multiplicative function of regression parameters being invariant over categories, and category-specific scalar effects, the ordered stereotype logit model is a flexible regression model for ordinal response variables. In this article, we propose a generalized estimating equations (GEE) approach to estimate the ordered stereotype logit model for panel data based on working covariance matrices, which are not required to be correctly specified. A simulation study compares the performance of GEE estimators based on various working correlation matrices and working covariance matrices using local odds ratios. Estimation of the model is illustrated using a real-world dataset. The results from the simulation study suggest that GEE estimation of this model is feasible in medium-sized and large samples and that estimators based on local odds ratios as realized in this study tend to be less efficient compared with estimators based on a working correlation matrix. For low true correlations, the efficiency gains seem to be rather small and if the working covariance structure is too flexible, the corresponding estimator may even be less efficient compared with the GEE estimator assuming independence. Like for GEE estimators more generally, if the true correlations over time are high, then a working covariance structure which is close to the true structure can lead to considerable efficiency gains compared with assuming independence.

This work was supported by the Marsden Fund (Award Num-ber E2987-3648) administrated by the Royal Society of NewZealand and also supported by grant RTI2018-100927-J-I00 administrated by Ministerio de Ciencia e Innovación (MCI,Spain), by the Agencia Estatal de Investigación (AEI, Spain),and by the European Regional Development Fund FEDER(FEDER, UE). We are grateful to Dr Louise McMillan for helpful comments.

For a two-way contingency table, odds ratios are commonly used to describe the relationships between the row and column variables. In the ordinary case cells are mutually exclusive, that is each subject must fit into one and only one cell. However, in many surveys respondents may select more than one outcome category, commonly referred to as multiple responses. We discuss model-based and Mantel–Haenszel estimators of an assumed common odds ratio for several $$2\times c$$ tables, where the two rows refer to independent groups and the c columns to multiple responses, treating the multiple responses as an extension of the multinomial sampling model. We derive new dually consistent (co)variance estimators for the Mantel–Haenszel odds ratio estimators and show their performance in a simulation study and illustrate the estimators on a linguistic data set.

Many of the methods which deal with clustering in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. In general, it is not possible to use statistical inferences or select the appropriateness of a model via information criteria with these techniques because there is no underlying probability model. This article summarizes some recent model-based methodologies for matrices of binary, count, and ordinal data, which are modelled under a unified statistical framework using finite mixtures to group the rows and/or columns. The model parameter can be constructed from a linear predictor of parameters and covariates through link functions. This likelihood-based one-mode and two-mode fuzzy clustering provides maximum likelihood estimation of parameters and the options of using likelihood information criteria for model comparison. Additionally, a Bayesian approach is presented in which the parameters and the number of clusters are estimated simultaneously from their joint posterior distribution. Visualization tools focused on ordinal data, the fuzziness of the clustering structures, and analogies of various standard plots used in the multivariate analysis are presented. Finally, a set of future extensions is enumerated.

Leadership by senior public managers in national departments is increasingly called for in response to many policy challenges and limitations of appointee and political leadership. This study defin...

Objective : The collection and use of ordinal variables are common in many psychological and psychiatric studies. Although the models for continuous variables have similarities to those for ordinal variables, there are advantages when a model developed for modeling ordinal data is used such as avoiding “floor” and “ceiling” effects and avoiding to assign scores, as it happens in continuous models, which can produce results sensitive to the score assigned. This paper introduces and focuses on the application of the ordered stereotype model, which was developed for modeling ordinal outcomes and is not so popular as other models such as linear regression and proportional odds models. This paper aims to compare the performance of the ordered stereotype model with other more commonly used models among researchers and practitioners. Methods : This article compares the performance of the stereotype model against the proportional odd and linear regression models, with three, four, and five levels of ordinal categories and sample sizes 100, 500, and 1000. This paper also discusses the problem of treating ordinal responses as continuous using a simulation study. The trend odds model is also presented in the application. Results : Three types of models were fitted in one real-life example, including ordered stereotype, proportional odds, and trend odds models. They reached similar conclusions in terms of the significance of covariates. The simulation study evaluated the performance of the ordered stereotype model under four cases. The performance varies depending on the scenarios. Conclusions : The method presented can be applied to several areas of psychiatry dealing with ordinal outcomes. One of the main advantages of this model is that it breaks with the assumption of levels of the ordinal response are equally spaced, which might be not true.

Mammalian host cell lines are the preferred expression systems for the manufacture of complex therapeutics and recombinant proteins. However, the most utilized mammalian host systems, namely Chinese hamster ovary (CHO), Sp2/0 and NS0 mouse myeloma cells, can produce glycoproteins with non-human glycans that may potentially illicit immunogenic responses. Hence, we developed a fully human expression system based on HEK293 cells for the stable and high titer production of recombinant proteins by first knocking out GLUL (encoding glutamine synthetase) using CRISPR-Cas9 system. Expression vectors using human GLUL as selection marker were then generated, with recombinant human erythropoietin (EPO) as our model protein. Selection was performed using methionine sulfoximine (MSX) to select for high EPO expression cells. EPO production of up to 92700 U/mL of EPO as analyzed by ELISA or 696 mg/L by densitometry was demonstrated in a 2 L stirred-tank fed batch bioreactor. Mass spectrometry analysis revealed that N-glycosylation of the produced EPO was similar to endogenous human proteins and non-human glycan epitopes were not detected. Collectively, our results highlight the use of a human cellular expression system for the high titer and xenogeneic-free production of EPO and possibly other complex recombinant proteins. NRF (Natl Research Foundation, S’pore) ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

This paper presents two new model-based goodness-of-fit tests for the ordered stereotype model applied to an ordinal response variable. The proposed tests are based on the Lipsitz test, which partitions the subjects into G groups following the popular Hosmer–Lemeshow test for binary data. The tests construct an alternative model where group effects are added into the null model. If the model fits the data well then the null model is correct, and there should be no group effects. One of the main advantages of the ordered stereotype model is that it allows us to determine a new uneven spacing of the ordinal response categories, dictated by the data. The two proposed tests use this new adjusted spacing. One test uses the form of the original ordered stereotype model, and the other uses an ordinary linear model. We demonstrate the performance of both tests under a variety of scenarios. Finally, the results of the application in three examples are presented.

Traditional cluster analysis methods used in ordinal data, for instance k-means and hierarchical clustering, are mostly heuristic and lack statistical inference tools to compare among competing models. To address this we propose a latent transitional model, a finite mixture model that includes both observed and latent covariates and apply it for the first time to the case of longitudinal ordinal data. This model-based clustering model is an extension of the proportional odds model and includes a first-order transitional term, occasion effects and interactions which provide flexible ways to capture different time patterns by cluster as well as time-heterogeneous transitions. We estimate model parameters within a Bayesian setting using a Markov chain Monte Carlo scheme and block-wise Metropolis–Hastings sampling. We illustrate the model using 2001–2011 self-reported health status (SRHS) from the Household, Income and Labour Dynamics in Australia survey. SRHS is recorded as an ordinal variable with five levels: poor, fair, good, very good and excellent. Using the Widely Applicable Information Criterion for model comparison, we find evidence for six latent groups. Transitions in the original data and the estimated groups are visualized using heatmaps.

Deciding on the best statistical method to apply when the response variable is ordinal is essential because the way the categories are ordered in the data is relevant as it could change the results of the analysis. Although the models for continuous variables have similarities to those for ordinal variables, this paper presents the advantages of the use of the ordering information on the outcomes with methods developed for modeling ordinal data such as the ordered stereotype model. The novelty of this article lies in showing the dangers of assigning equally spaced scores to ordered response categories in statistical analysis, which are illustrated with a simulation study and a case study. We propose a new way to use the score parameters, which incorporates the fitted spacing dictated by the data. Additionally, this article uses score parameter estimates in the ordered stereotype model to propose a new measure to calculate continuous medians in the raw data: the adjusted c-median. It benefits the general audience who can easily understand the median as a summary statistic. Supplementary materials for this article are available online.

The clustered regularly interspaced short palindromic repeats (CRISPR) system functions naturally in bacterial adaptive immunity, but has been successfully repurposed for genome engineering in many different living organisms. Most commonly, the wildtype CRISPR associated 9 (Cas9) or Cas12a endonuclease is used to cleave specific sites in the genome, after which the DNA double-stranded break is repaired via the non-homologous end joining (NHEJ) pathway or the homology-directed repair (HDR) pathway depending on whether a donor template is absent or present respectively. To date, CRISPR systems from different bacterial species have been shown to be capable of performing genome editing in mammalian cells. However, despite the apparent simplicity of the technology, multiple design parameters need to be considered, which often leave users perplexed about how best to carry out their genome editing experiments. Here, we describe a complete workflow from experimental design to identification of cell clones that carry desired DNA modifications, with the goal of facilitating successful execution of genome editing experiments in mammalian cell lines. We highlight key considerations for users to take note of, including the choice of CRISPR system, the spacer length, and the design of a single-stranded oligodeoxynucleotide (ssODN) donor template. We envision that this workflow will be useful for gene knockout studies, disease modeling efforts, or the generation of reporter cell lines. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Nanyang Technological University National Medical Research Council (NMRC) National Research Foundation (NRF) M.H.T. is supported by an Agency for Science Technology and Research’s Joint Council Office grant (1431AFG103), a National Medical Research Council grant (OFIRG/0017/2016), National Research Foundation grants (NRF2013-THE001-046 and NRF2013-THE001-093), a Ministry of Education Tier 1 grant (RG50/17 (S)), a startup grant from Nanyang Technological University, and funds for the International Genetically Engineering Machine (iGEM) competition from Nanyang Technological University.

A modified version of Cas9 with a fusion of the hormone-binding domain of the estrogen receptor allows reversible control of Cas9 activity with high efficiency at multiple loci with 4-hydroxytamoxifen treatment. CRISPR–Cas9 has emerged as a powerful technology that enables ready modification of the mammalian genome. The ability to modulate Cas9 activity can reduce off-target cleavage and facilitate precise genome engineering. Here we report the development of a Cas9 variant whose activity can be switched on and off in human cells with 4-hydroxytamoxifen (4-HT) by fusing the Cas9 enzyme with the hormone-binding domain of the estrogen receptor (ERT2). The final optimized variant, termed iCas, showed low endonuclease activity without 4-HT but high editing efficiency at multiple loci with the chemical. We also tuned the duration and concentration of 4-HT treatment to reduce off-target genome modification. Additionally, we benchmarked iCas against other chemical-inducible methods and found that it had the fastest on rate and that its activity could be toggled on and off repeatedly. Collectively, these results highlight the utility of iCas for rapid and reversible control of genome-editing function.

This paper presents a new goodness-of-fit test for an ordered stereotype model used for an ordinal response variable. The proposed test is based on the well-known Hosmer-Lemeshow test and its version for the proportional odds regression model. The latter test statistic is calculated from a grouping scheme assuming that the levels of the ordinal response are equally spaced which might be not true. One of the main advantages of the ordered stereotype model is that it allows us to determine a new uneven spacing of the ordinal response categories, dictated by the data. The proposed test takes the use of this new adjusted spacing to partition data. A simulation study shows good performance of the proposed test under a variety of scenarios. Finally, the results of the application in two examples are presented. Copyright © 2016 John Wiley & Sons, Ltd.

Medical research frequently focuses on the relationship between quality of life (QoL) and survival time of subjects. QoL may be one of the most important factors that could be used to predict survival, making it worth identifying factors that jointly affect survival and QoL. We propose a semiparametric joint model that consists of item response and survival components, where these two components are linked through latent variables. Several popular ordinal models are considered and compared in the item response component, while the Cox proportional hazards model is used in the survival component. We estimate the baseline hazard function and model parameters simultaneously, through a profile likelihood approach. We illustrate the method using an example from a clinical study.

In classification problems, a large number of features are typically used to describe the problem's instances. However, not all of these features are useful for classification. Feature selection is usually an important pre-processing step to overcome the problem of "curse of dimensionality". Feature selection aims to choose a small number of features to achieve similar or better classification performance than using all features. This paper presents a particle swarm Optimization (PSO)-based multi-objective feature selection approach to evolving a set of non-dominated feature subsets which achieve high classification performance. The proposed algorithm uses local search techniques to improve a Pareto front and is compared with a pure multi-objective PSO algorithm, three well-known evolutionary multi-objective algorithms and a current state-of-the-art PSO-based multi-objective feature selection approach. Their performances are examined on 12 benchmark datasets. The experimental results show that in most cases, the proposed multi-objective algorithm generates better Pareto fronts than all other methods.

Genome editing using programmable DNA endonucleases enables the engineering of eukaryotic cells and living organisms with desirable properties or traits. Among the various molecular scissors that have been developed to date, the most versatile and easy-to-use family of nucleases derives from CRISPR-Cas, which exists naturally as an adaptive immune system in bacteria. Recent advances in the CRISPR-Cas technology have expanded our ability to manipulate complex genomes for myriad biomedical and biotechnological applications. Some of these applications are time-sensitive or demand high spatial precision. Here, we describe the use of an inducible CRISPR-Cas9 system, termed iCas, which we have developed to enable rapid and tight control of genome editing in mammalian cells. The iCas system can be switched on or off as desired through the introduction or removal of the small molecule tamoxifen or its related analogs such as 4-hydroxytamoxifen (4-HT).

Study objective Online educational resources such as blogs are increasingly used for education by emergency medicine clinicians. The Social Media Index was developed to quantify their relative impact. The Medical Education Translational Resources: Indicators of Quality (METRIQ) study was conducted in part to determine the association between the Social Media Index score and quality as measured by gestalt and previously derived quality instruments. Methods Ten blogs were randomly selected from a list of emergency medicine and critical care Web sites. The 2 most recent clinically oriented blog posts published on these blogs were evaluated with gestalt, the Academic Life in Emergency Medicine Approved Instructional Resources (ALiEM AIR) score, and the METRIQ-8 score. Volunteer raters (including medical students, emergency medicine residents, and emergency medicine attending physicians) were identified with a multimodal recruitment methodology. The Social Media Index was calculated in February 2016, November 2016, April 2017, and December 2017. Pearson's correlations were calculated between the Social Media Index and the average rater gestalt, ALiEM AIR score, and METRIQ-8 score. Results A total of 309 of 330 raters completed all ratings (93.6%). The Social Media Index correlated moderately to strongly with the mean rater gestalt ratings (range 0.69 to 0.76) and moderately with the mean rater ALiEM AIR score (range 0.55 to 0.61) and METRIQ-8 score (range 0.53 to 0.57) during the month of the blog post's selection and for 2 years after. Conclusion The Social Media Index's correlation with multiple quality evaluation instruments over time supports the hypothesis that it is associated with overall Web site quality. It can play a role in guiding individuals to high-quality resources that can be reviewed with critical appraisal techniques.

Genome editing using programmable DNA endonucleases enables the engineering of eukaryotic cells and living organisms with desirable properties or traits. Among the various molecular scissors that have been developed to date, the most versatile and easy-to-use family of nucleases derives from CRISPR-Cas, which exists naturally as an adaptive immune system in bacteria. Recent advances in the CRISPR-Cas technology have expanded our ability to manipulate complex genomes for myriad biomedical and biotechnological applications. Some of these applications are time-sensitive or demand high spatial precision. Here, we describe the use of an inducible CRISPR-Cas9 system, termed iCas, which we have developed to enable rapid and tight control of genome editing in mammalian cells. The iCas system can be switched on or off as desired through the introduction or removal of the small molecule tamoxifen or its related analogs such as 4-hydroxytamoxifen (4-HT).

Resume Cet article examine les strategies de modelisation pour des mesures repetees de variables a reponses multiples. Ces donnes surviennent dans le contexte de variables categoriques ou l'on peut selectionner une ou plusieurs categories de reponse. Nous considerons chacune des reponses multiples comme un resultat binaire et nous utilisons une approche de modelisation marginal (ou population-moyenne) pour analyser ses esprances. Des equations d'estimation generalisees sont utilisees pour tenir compte des differentes structures de correlation, a la fois dans le temps et entre les elements. Nous examinons egalement une approche alternative en utilisant un modele lineaire generalise mixte avec des interpretations conditionnelles. Nous appliquons les diffrentes methodes et comparons les resultats en utilisant des donnees provenant dun groupe detudes realise en Australie appele “the Household, Income and Labour Dynamics Survey” (Enquete dynamique sur les foyers, les revenus et le travail). Summary This article discusses modelling strategies for repeated measurements of multiple response variables. Such data arise in the context of categorical variables where one can select more than one of the categories as the response. We consider each of the multiple responses as a binary outcome and use a marginal (or population-averaged) modelling approach to analyse its means. Generalized estimating equations are used to account for different correlation structures, both over time and between items. We also discuss an alternative approach using a generalized linear mixed model with conditional interpretations. We illustrate the methods using data from a panel study in Australia called the Household, Income, and Labour Dynamics Survey.

Dimension reduction is a preprocessing step in many classification tasks, but reducing dimensionality and finding the optimal set of features or attributes are challenging because of the big search space and interactions between attributes. This paper proposes a new dimension reduction method by using a statistical variable grouping method that groups similar attributes into a group by considering interaction between attributes and using particle swarm optimisation as a search technique to adopt the discovered statistical grouping information to search optimal attribute subsets. Two types of approaches are developed, where the first aims to select one attribute from each group to reduce the dimensionality, and the second allows the selection of multiple attributes from one group to further improve the classification performance. Experiments on ten datasets of varying difficulties show that all the two approaches can successfully address dimension reduction tasks to decrease the number of attributes, and achieve the similar of better classification performance. The first approach selects a smaller number of attributes than the second approach while the second approach achieves better classification performance. The proposed new algorithms outperform other recent dimension reduction algorithms in terms of the classification performance, or further reduce the number of attributes while maintaining the classification performance.

Study objective Open educational resources such as blogs are increasingly used for medical education. Gestalt is generally the evaluation method used for these resources; however, little information has been published on it. We aim to evaluate the reliability of gestalt in the assessment of emergency medicine blogs. Methods We identified 60 English-language emergency medicine Web sites that posted clinically oriented blogs between January 1, 2016, and February 24, 2016. Ten Web sites were selected with a random-number generator. Medical students, emergency medicine residents, and emergency medicine attending physicians evaluated the 2 most recent clinical blog posts from each site for quality, using a 7-point Likert scale. The mean gestalt scores of each blog post were compared between groups with Pearson's correlations. Single and average measure intraclass correlation coefficients were calculated within groups. A generalizability study evaluated variance within gestalt and a decision study calculated the number of raters required to reliably (>0.8) estimate quality. Results One hundred twenty-one medical students, 88 residents, and 100 attending physicians (93.6% of enrolled participants) evaluated all 20 blog posts. Single-measure intraclass correlation coefficients within groups were fair to poor (0.36 to 0.40). Average-measure intraclass correlation coefficients were more reliable (0.811 to 0.840). Mean gestalt ratings by attending physicians correlated strongly with those by medical students (r=0.92) and residents (r=0.99). The generalizability coefficient was 0.91 for the complete data set. The decision study found that 42 gestalt ratings were required to reliably evaluate quality (>0.8). Conclusion The mean gestalt quality ratings of blog posts between medical students, residents, and attending physicians correlate strongly, but individual ratings are unreliable. With sufficient raters, mean gestalt ratings provide a community standard for assessment.

The work in this paper introduces finite mixture models that can be used to simultaneously cluster the rows and columns of two-mode ordinal categorical response data, such as those resulting from Likert scale responses. We use the popular proportional odds parameterisation and propose models which provide insights into major patterns in the data. Model-fitting is performed using the EM algorithm, and a fuzzy allocation of rows and columns to corresponding clusters is obtained. The clustering ability of the models is evaluated in a simulation study and demonstrated using two real data sets. Electronic supplementary material The online version of this article (doi:10.1007/s11336-016-9503-3) contains supplementary material, which is available to authorized users.