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  • 1
    In: JAMA Oncology, American Medical Association (AMA)
    Abstract: Systematic data on the association between anticancer therapies and thromboembolic events (TEEs) in patients with COVID-19 are lacking. Objective To assess the association between anticancer therapy exposure within 3 months prior to COVID-19 and TEEs following COVID-19 diagnosis in patients with cancer. Design, Setting, and Participants This registry-based retrospective cohort study included patients who were hospitalized and had active cancer and laboratory-confirmed SARS-CoV-2 infection. Data were accrued from March 2020 to December 2021 and analyzed from December 2021 to October 2022. Exposure Treatments of interest (TOIs) (endocrine therapy, vascular endothelial growth factor inhibitors/tyrosine kinase inhibitors [VEGFis/TKIs], immunomodulators [IMiDs] , immune checkpoint inhibitors [ICIs], chemotherapy) vs reference (no systemic therapy) in 3 months prior to COVID-19. Main Outcomes and Measures Main outcomes were (1) venous thromboembolism (VTE) and (2) arterial thromboembolism (ATE). Secondary outcome was severity of COVID-19 (rates of intensive care unit admission, mechanical ventilation, 30-day all-cause mortality following TEEs in TOI vs reference group) at 30-day follow-up. Results Of 4988 hospitalized patients with cancer (median [IQR] age, 69 [59-78] years; 2608 [52%] male), 1869 had received 1 or more TOIs. Incidence of VTE was higher in all TOI groups: endocrine therapy, 7%; VEGFis/TKIs, 10%; IMiDs, 8%; ICIs, 12%; and chemotherapy, 10%, compared with patients not receiving systemic therapies (6%). In multivariable log-binomial regression analyses, relative risk of VTE (adjusted risk ratio [aRR] , 1.33; 95% CI, 1.04-1.69) but not ATE (aRR, 0.81; 95% CI, 0.56-1.16) was significantly higher in those exposed to all TOIs pooled together vs those with no exposure. Among individual drugs, ICIs were significantly associated with VTE (aRR, 1.45; 95% CI, 1.01-2.07). Also noted were significant associations between VTE and active and progressing cancer (aRR, 1.43; 95% CI, 1.01-2.03), history of VTE (aRR, 3.10; 95% CI, 2.38-4.04), and high-risk site of cancer (aRR, 1.42; 95% CI, 1.14-1.75). Black patients had a higher risk of TEEs (aRR, 1.24; 95% CI, 1.03-1.50) than White patients. Patients with TEEs had high intensive care unit admission (46%) and mechanical ventilation (31%) rates. Relative risk of death in patients with TEEs was higher in those exposed to TOIs vs not (aRR, 1.12; 95% CI, 0.91-1.38) and was significantly associated with poor performance status (aRR, 1.77; 95% CI, 1.30-2.40) and active/progressing cancer (aRR, 1.55; 95% CI, 1.13-2.13). Conclusions and Relevance In this cohort study, relative risk of developing VTE was high among patients receiving TOIs and varied by the type of therapy, underlying risk factors, and demographics, such as race and ethnicity. These findings highlight the need for close monitoring and perhaps personalized thromboprophylaxis to prevent morbidity and mortality associated with COVID-19–related thromboembolism in patients with cancer.
    Type of Medium: Online Resource
    ISSN: 2374-2437
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2023
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  • 2
    In: BMC Cancer, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2023-03-23)
    Abstract: COVID-19 particularly impacted patients with co-morbid conditions, including cancer. Patients with melanoma have not been specifically studied in large numbers. Here, we sought to identify factors that associated with COVID-19 severity among patients with melanoma, particularly assessing outcomes of patients on active targeted or immune therapy. Methods Using the COVID-19 and Cancer Consortium (CCC19) registry, we identified 307 patients with melanoma diagnosed with COVID-19. We used multivariable models to assess demographic, cancer-related, and treatment-related factors associated with COVID-19 severity on a 6-level ordinal severity scale. We assessed whether treatment was associated with increased cardiac or pulmonary dysfunction among hospitalized patients and assessed mortality among patients with a history of melanoma compared with other cancer survivors. Results Of 307 patients, 52 received immunotherapy (17%), and 32 targeted therapy (10%) in the previous 3 months. Using multivariable analyses, these treatments were not associated with COVID-19 severity (immunotherapy OR 0.51, 95% CI 0.19 – 1.39; targeted therapy OR 1.89, 95% CI 0.64 – 5.55). Among hospitalized patients, no signals of increased cardiac or pulmonary organ dysfunction, as measured by troponin, brain natriuretic peptide, and oxygenation were noted. Patients with a history of melanoma had similar 90-day mortality compared with other cancer survivors (OR 1.21, 95% CI 0.62 – 2.35). Conclusions Melanoma therapies did not appear to be associated with increased severity of COVID-19 or worsening organ dysfunction. Patients with history of melanoma had similar 90-day survival following COVID-19 compared with other cancer survivors.
    Type of Medium: Online Resource
    ISSN: 1471-2407
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2041352-X
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  • 3
    In: Oncotarget, Impact Journals, LLC, Vol. 8, No. 17 ( 2017-04-25), p. 28971-28989
    Type of Medium: Online Resource
    ISSN: 1949-2553
    URL: Issue
    Language: English
    Publisher: Impact Journals, LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2560162-3
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  • 4
    Online Resource
    Online Resource
    American Educational Research Association (AERA) ; 2014
    In:  Journal of Educational and Behavioral Statistics Vol. 39, No. 5 ( 2014-10), p. 333-367
    In: Journal of Educational and Behavioral Statistics, American Educational Research Association (AERA), Vol. 39, No. 5 ( 2014-10), p. 333-367
    Abstract: Investigations of the effects of schools (or teachers) on student achievement focus on either (1) individual school effects, such as value-added analyses, or (2) school-type effects, such as comparisons of charter and public schools. Controlling for school composition by including student covariates is critical for valid estimation of either kind of school effect. Student covariates often have different effects between schools than within schools. Econometricians typically attribute such differences to a form of endogeneity, specifically, “Level-2 endogeneity,” or the confounding of student covariates with unobserved school characteristics, whereas education researchers primarily interpret the differences as contextual effects or the effects of collective peer attributes on individual student achievement. This article considers both and makes connections between the econometric and education research literatures. We show that the Hausman and Taylor approach from panel data econometrics can be used for valid estimation of individual school or school-type effects when there is only Level-2 endogeneity but can lead to bias when there are also contextual or peer effects. In contrast, contextual effects are typically estimated by including school means of student covariates in addition to the student-level covariates (equivalent to the Mundlak device), but this leads to biased school comparisons in the presence of Level-2 endogeneity. We interpret the estimates from these two competing estimators in terms of the “Type A” and “Type B” school effects defined by Raudenbush and Willms and show that both estimators are preferable to the common group-mean-centering approach.
    Type of Medium: Online Resource
    ISSN: 1076-9986 , 1935-1054
    Language: English
    Publisher: American Educational Research Association (AERA)
    Publication Date: 2014
    detail.hit.zdb_id: 1225314-5
    detail.hit.zdb_id: 2174169-4
    SSG: 5,3
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  • 5
    Online Resource
    Online Resource
    American Educational Research Association (AERA) ; 2015
    In:  Journal of Educational and Behavioral Statistics Vol. 40, No. 1 ( 2015-02), p. 35-68
    In: Journal of Educational and Behavioral Statistics, American Educational Research Association (AERA), Vol. 40, No. 1 ( 2015-02), p. 35-68
    Abstract: Aggregate-level conditional status metrics (ACSMs) describe the status of a group by referencing current performance to expectations given past scores. This article provides a framework for these metrics, classifying them by aggregation function (mean or median), regression approach (linear mean and nonlinear quantile), and the scale that supports interpretations (percentile rank and score scale), among other factors. This study addresses the question “how different are these ACSMs?” in three ways. First, using simulated data, it evaluates how well each model recovers its respective parameters. Second, using both simulated and empirical data, it illustrates practical differences among ACSMs in terms of pairwise rank differences incurred by switching between metrics. Third, it ranks ACSMs in terms of their robustness under scale transformations. The results consistently show that choices between mean- and median-based metrics lead to more substantial differences than choices between fixed- and random-effects or linear mean and nonlinear quantile regression. The findings set expectations for cross-metric comparability in realistic data scenarios.
    Type of Medium: Online Resource
    ISSN: 1076-9986 , 1935-1054
    Language: English
    Publisher: American Educational Research Association (AERA)
    Publication Date: 2015
    detail.hit.zdb_id: 1225314-5
    detail.hit.zdb_id: 2174169-4
    SSG: 5,3
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  • 6
    Online Resource
    Online Resource
    Wiley ; 2023
    In:  Journal of Educational Measurement Vol. 60, No. 2 ( 2023-06), p. 173-201
    In: Journal of Educational Measurement, Wiley, Vol. 60, No. 2 ( 2023-06), p. 173-201
    Abstract: The simple average of student growth scores is often used in accountability systems, but it can be problematic for decision making. When computed using a small/moderate number of students, it can be sensitive to the sample, resulting in inaccurate representations of growth of the students, low year‐to‐year stability, and inequities for low‐incidence groups. An alternative designed to address these issues is to use an Empirical Best Linear Prediction (EBLP), which is a weighted average of growth score data from other years and/or subjects. We apply both approaches to two statewide datasets to answer empirical questions about their performance. The EBLP outperforms the simple average in accuracy and cross‐year stability with the exception that accuracy was not necessarily improved for very large districts in one of the states. In such exceptions, we show a beneficial alternative may be to use a hybrid approach in which very large districts receive the simple average and all others receive the EBLP. We find that adding more growth score data to the computation of the EBLP can improve accuracy, but not necessarily for larger schools/districts. We review key decision points in aggregate growth reporting and in specifying an EBLP weighted average in practice.
    Type of Medium: Online Resource
    ISSN: 0022-0655 , 1745-3984
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2066579-9
    detail.hit.zdb_id: 218333-X
    SSG: 5,2
    SSG: 5,3
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  • 7
    Online Resource
    Online Resource
    American Educational Research Association (AERA) ; 2018
    In:  Journal of Educational and Behavioral Statistics Vol. 43, No. 6 ( 2018-12), p. 663-692
    In: Journal of Educational and Behavioral Statistics, American Educational Research Association (AERA), Vol. 43, No. 6 ( 2018-12), p. 663-692
    Abstract: This article proposes a flexible extension of the Fay–Herriot model for making inferences from coarsened, group-level achievement data, for example, school-level data consisting of numbers of students falling into various ordinal performance categories. The model builds on the heteroskedastic ordered probit (HETOP) framework advocated by Reardon, Shear, Castellano, and Ho by allowing group parameters to be modeled with regressions on group-level covariates, and residuals modeled using the flexible exponential family of distributions recommended by Efron. We demonstrate that the alternative modeling framework, termed the “Fay–Herriot heteroskedastic ordered probit” (FH-HETOP) model, is useful for mitigating some of the challenges with direct maximum likelihood estimators from the HETOP model. We conduct a simulation study to compare the costs and benefits of several methods for using the FH-HETOP model to estimate group parameters and functions of them, including posterior means, constrained Bayes estimators, and the “triple goal” estimators of Shen and Louis. We also provide an application of the FH-HETOP model to math proficiency data from the Early Childhood Longitudinal Study. Code for estimating the FH-HETOP model and conducting supporting calculations is provided in a new package for the R environment.
    Type of Medium: Online Resource
    ISSN: 1076-9986 , 1935-1054
    Language: English
    Publisher: American Educational Research Association (AERA)
    Publication Date: 2018
    detail.hit.zdb_id: 1225314-5
    detail.hit.zdb_id: 2174169-4
    SSG: 5,3
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  • 8
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  Journal of Science Education and Technology Vol. 31, No. 4 ( 2022-08), p. 528-541
    In: Journal of Science Education and Technology, Springer Science and Business Media LLC, Vol. 31, No. 4 ( 2022-08), p. 528-541
    Type of Medium: Online Resource
    ISSN: 1059-0145 , 1573-1839
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2017253-9
    detail.hit.zdb_id: 1188564-6
    SSG: 11
    SSG: 5,3
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  • 9
    In: Journal of Research in Science Teaching, Wiley
    Abstract: There is strong agreement in science teacher education of the importance of teachers' content knowledge for teaching (CKT), which includes their subject matter knowledge and their pedagogical content knowledge. However, there are limited instruments that can be easily administered and scored on a large scale to assess and study elementary science teachers' CKT. Such measures would support strategic monitoring of large groups of science teachers' CKT and the investigation of comparative questions about science teachers' CKT longitudinally across the professional continuum or across teacher education or professional development sites. To address this gap, this study focused on designing an automatically scorable summative assessment that can be used to measure preservice elementary teachers' (PSETs') CKT in one high‐leverage science content area: matter and its interactions. We conducted a field test of this CKT instrument with 822 PSETs from across the United States and used the response data to examine how this instrument functions as a potential tool for measuring PSETs' CKT in this science content area. Results suggest this instrument is reliable and can be used on large scale to support valid inferences about PSETs' CKT in this content area. In addition, the dimensionality analysis showed that all items measure a single construct of CKT about matter and its interactions, as participants did not show any differential performance by content topic or work of teaching science instructional tool categories. Implications for progressing the field's understanding of the nature of CKT and approaches to developing summative instruments to assess science teachers' CKT are discussed.
    Type of Medium: Online Resource
    ISSN: 0022-4308 , 1098-2736
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 410674-X
    detail.hit.zdb_id: 1479335-0
    SSG: 5,3
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  • 10
    Online Resource
    Online Resource
    SAGE Publications ; 2017
    In:  Educational and Psychological Measurement Vol. 77, No. 6 ( 2017-12), p. 917-944
    In: Educational and Psychological Measurement, SAGE Publications, Vol. 77, No. 6 ( 2017-12), p. 917-944
    Abstract: Student Growth Percentiles (SGPs) increasingly are being used in the United States for inferences about student achievement growth and educator effectiveness. Emerging research has indicated that SGPs estimated from observed test scores have large measurement errors. As such, little is known about “true” SGPs, which are defined in terms of nonlinear functions of latent achievement attributes for individual students and their distributions across students. We develop a novel framework using latent regression multidimensional item response theory models to study distributional properties of true SGPs. We apply these methods to several cohorts of longitudinal item response data from more than 330,000 students in a large urban metropolitan area to provide new empirical information about true SGPs. We find that true SGPs are correlated 0.3 to 0.5 across mathematics and English language arts, and that they have nontrivial relationships with individual student characteristics, particularly student race/ethnicity and absenteeism. We evaluate the potential of using these relationships to improve the accuracy of SGPs estimated from observed test scores, finding that accuracy gains even under optimal circumstances are modest. We also consider the properties of SGPs averaged to the teacher level, widely used for teacher evaluations. We find that average true SGPs for individual teachers vary substantially as a function of the characteristics of the students they teach. We discuss implications of our findings for the estimation and interpretation of SGPs at both the individual and aggregate levels.
    Type of Medium: Online Resource
    ISSN: 0013-1644 , 1552-3888
    RVK:
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2017
    detail.hit.zdb_id: 1500101-5
    detail.hit.zdb_id: 206630-0
    SSG: 5,2
    SSG: 5,3
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