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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.

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.

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.

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).

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.

Outcome-dependent sampling designs are commonly used in economics, market research and epidemiological studies. Case-control sampling design is a classic example of outcome-dependent sampling, where exposure information is collected on subjects conditional on their disease status. In many situations, the outcome under consideration may have multiple categories instead of a simple dichotomization. For example, in a case-control study, there may be disease sub-classification among the “cases” based on progression of the disease, or in terms of other histological and morphological characteristics of the disease. In this note, we investigate the issue of fitting prospective multivariate generalized linear models to such multiple-category outcome data, ignoring the retrospective nature of the sampling design. We first provide a set of necessary and sufficient conditions for the link functions that will allow for equivalence of prospective and retrospective inference for the parameters of interest. We show that for categorical outcomes, prospective–retrospective equivalence does not hold beyond the generalized multinomial logit link. We then derive an approximate expression for the bias incurred when link functions outside this class are used. Most popular models for ordinal response fall outside the multiplicative intercept class and one should be cautious while performing a naive prospective analysis of such data as the bias could be substantial. We illustrate the extent of bias through a real data example, based on the ongoing Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial by the National Cancer Institute. The simulations based on the real study illustrate that the bias approximations work well in practice.