Browsing by Subject "Data Interpretation, Statistical"
Now showing 1 - 18 of 18
- Results Per Page
- Sort Options
Item Open Access A latent factor linear mixed model for high-dimensional longitudinal data analysis.(Statistics in medicine, 2013-10) An, Xinming; Yang, Qing; Bentler, Peter MHigh-dimensional longitudinal data involving latent variables such as depression and anxiety that cannot be quantified directly are often encountered in biomedical and social sciences. Multiple responses are used to characterize these latent quantities, and repeated measures are collected to capture their trends over time. Furthermore, substantive research questions may concern issues such as interrelated trends among latent variables that can only be addressed by modeling them jointly. Although statistical analysis of univariate longitudinal data has been well developed, methods for modeling multivariate high-dimensional longitudinal data are still under development. In this paper, we propose a latent factor linear mixed model (LFLMM) for analyzing this type of data. This model is a combination of the factor analysis and multivariate linear mixed models. Under this modeling framework, we reduced the high-dimensional responses to low-dimensional latent factors by the factor analysis model, and then we used the multivariate linear mixed model to study the longitudinal trends of these latent factors. We developed an expectation-maximization algorithm to estimate the model. We used simulation studies to investigate the computational properties of the expectation-maximization algorithm and compare the LFLMM model with other approaches for high-dimensional longitudinal data analysis. We used a real data example to illustrate the practical usefulness of the model.Item Open Access Computational Methods for RNA Structure Validation and Improvement.(Methods Enzymol, 2015) Jain, Swati; Richardson, David C; Richardson, Jane SWith increasing recognition of the roles RNA molecules and RNA/protein complexes play in an unexpected variety of biological processes, understanding of RNA structure-function relationships is of high current importance. To make clean biological interpretations from three-dimensional structures, it is imperative to have high-quality, accurate RNA crystal structures available, and the community has thoroughly embraced that goal. However, due to the many degrees of freedom inherent in RNA structure (especially for the backbone), it is a significant challenge to succeed in building accurate experimental models for RNA structures. This chapter describes the tools and techniques our research group and our collaborators have developed over the years to help RNA structural biologists both evaluate and achieve better accuracy. Expert analysis of large, high-resolution, quality-conscious RNA datasets provides the fundamental information that enables automated methods for robust and efficient error diagnosis in validating RNA structures at all resolutions. The even more crucial goal of correcting the diagnosed outliers has steadily developed toward highly effective, computationally based techniques. Automation enables solving complex issues in large RNA structures, but cannot circumvent the need for thoughtful examination of local details, and so we also provide some guidance for interpreting and acting on the results of current structure validation for RNA.Item Unknown Cost of innovation in the pharmaceutical industry.(J Health Econ, 1991-07) DiMasi, JA; Hansen, RW; Grabowski, HG; Lasagna, LThe research and development costs of 93 randomly selected new chemical entities (NCEs) were obtained from a survey of 12 U.S.-owned pharmaceutical firms. These data were used to estimate the pre-tax average cost of new drug development. The costs of abandoned NCEs were linked to the costs of NCEs that obtained marketing approval. For base case parameter values, the estimated out-of-pocket cost per approved NCE is $114 million (1987 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a 9% discount rate yielded an average cost estimate of $231 million (1987 dollars).Item Unknown Developing Treatment Guidelines During a Pandemic Health Crisis: Lessons Learned From COVID-19.(Annals of internal medicine, 2021-08) Kuriakose, Safia; Singh, Kanal; Pau, Alice K; Daar, Eric; Gandhi, Rajesh; Tebas, Pablo; Evans, Laura; Gulick, Roy M; Lane, H Clifford; Masur, Henry; NIH COVID-19 Treatment Guidelines Panel; Aberg, Judith A; Adimora, Adaora A; Baker, Jason; Kreuziger, Lisa Baumann; Bedimo, Roger; Belperio, Pamela S; Cantrill, Stephen V; Coopersmith, Craig M; Davis, Susan L; Dzierba, Amy L; Gallagher, John J; Glidden, David V; Grund, Birgit; Hardy, Erica J; Hinkson, Carl; Hughes, Brenna L; Johnson, Steven; Keller, Marla J; Kim, Arthur Y; Lennox, Jeffrey L; Levy, Mitchell M; Li, Jonathan Z; Martin, Greg S; Naggie, Susanna; Pavia, Andrew T; Seam, Nitin; Simpson, Steven Q; Swindells, Susan; Tien, Phyllis; Waghmare, Alpana A; Wilson, Kevin C; Yazdany, Jinoos; Zachariah, Philip; Campbell, Danielle M; Harrison, Carly; Burgess, Timothy; Francis, Joseph; Sheikh, Virginia; Uyeki, Timothy M; Walker, Robert; Brooks, John T; Ortiz, Laura Bosque; Davey, Richard T; Doepel, Laurie K; Eisinger, Robert W; Han, Alison; Higgs, Elizabeth S; Nason, Martha C; Crew, Page; Lerner, Andrea M; Lund, Claire; Worthington, ChristopherThe development of the National Institutes of Health (NIH) COVID-19 Treatment Guidelines began in March 2020 in response to a request from the White House Coronavirus Task Force. Within 4 days of the request, the NIH COVID-19 Treatment Guidelines Panel was established and the first meeting took place (virtually-as did subsequent meetings). The Panel comprises 57 individuals representing 6 governmental agencies, 11 professional societies, and 33 medical centers, plus 2 community members, who have worked together to create and frequently update the guidelines on the basis of evidence from the most recent clinical studies available. The initial version of the guidelines was completed within 2 weeks and posted online on 21 April 2020. Initially, sparse evidence was available to guide COVID-19 treatment recommendations. However, treatment data rapidly accrued based on results from clinical studies that used various study designs and evaluated different therapeutic agents and approaches. Data have continued to evolve at a rapid pace, leading to 24 revisions and updates of the guidelines in the first year. This process has provided important lessons for responding to an unprecedented public health emergency: Providers and stakeholders are eager to access credible, current treatment guidelines; governmental agencies, professional societies, and health care leaders can work together effectively and expeditiously; panelists from various disciplines, including biostatistics, are important for quickly developing well-informed recommendations; well-powered randomized clinical trials continue to provide the most compelling evidence to guide treatment recommendations; treatment recommendations need to be developed in a confidential setting free from external pressures; development of a user-friendly, web-based format for communicating with health care providers requires substantial administrative support; and frequent updates are necessary as clinical evidence rapidly emerges.Item Open Access Evaluating marker-guided treatment selection strategies.(Biometrics, 2014-09) Matsouaka, Roland A; Li, Junlong; Cai, TianxiA potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.Item Open Access Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association.(Circulation, 2018-03) Benjamin, Emelia J; Virani, Salim S; Callaway, Clifton W; Chamberlain, Alanna M; Chang, Alexander R; Cheng, Susan; Chiuve, Stephanie E; Cushman, Mary; Delling, Francesca N; Deo, Rajat; de Ferranti, Sarah D; Ferguson, Jane F; Fornage, Myriam; Gillespie, Cathleen; Isasi, Carmen R; Jiménez, Monik C; Jordan, Lori Chaffin; Judd, Suzanne E; Lackland, Daniel; Lichtman, Judith H; Lisabeth, Lynda; Liu, Simin; Longenecker, Chris T; Lutsey, Pamela L; Mackey, Jason S; Matchar, David B; Matsushita, Kunihiro; Mussolino, Michael E; Nasir, Khurram; O'Flaherty, Martin; Palaniappan, Latha P; Pandey, Ambarish; Pandey, Dilip K; Reeves, Mathew J; Ritchey, Matthew D; Rodriguez, Carlos J; Roth, Gregory A; Rosamond, Wayne D; Sampson, Uchechukwu KA; Satou, Gary M; Shah, Svati H; Spartano, Nicole L; Tirschwell, David L; Tsao, Connie W; Voeks, Jenifer H; Willey, Joshua Z; Wilkins, John T; Wu, Jason Hy; Alger, Heather M; Wong, Sally S; Muntner, Paul; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics SubcommitteeEach year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA's My Life Check - Life's Simple 7 (Figure ), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions. Cardiovascular disease (CVD) and stroke produce immense health and economic burdens in the United States and globally. The Update also presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease [CHD], heart failure [HF], valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). Since 2007, the annual versions of the Statistical Update have been cited >20 000 times in the literature. From January to July 2017 alone, the 2017 Statistical Update was accessed >106 500 times. Each annual version of the Statistical Update undergoes revisions to include the newest nationally representative data, add additional relevant published scientific findings, remove older information, add new sections or chapters, and increase the number of ways to access and use the assembled information. This year-long process, which begins as soon as the previous Statistical Update is published, is performed by the AHA Statistics Committee faculty volunteers and staff and government agency partners. This year's edition includes new data on the monitoring and benefits of cardiovascular health in the population, new metrics to assess and monitor healthy diets, new information on stroke in young adults, an enhanced focus on underserved and minority populations, a substantively expanded focus on the global burden of CVD, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the AHA's 2020 Impact Goals. Below are a few highlights from this year's Update. 1Item Open Access High Dimensional Variable Selection with Error Control.(Biomed Res Int, 2016) Kim, Sangjin; Halabi, SusanBackground. The iterative sure independence screening (ISIS) is a popular method in selecting important variables while maintaining most of the informative variables relevant to the outcome in high throughput data. However, it not only is computationally intensive but also may cause high false discovery rate (FDR). We propose to use the FDR as a screening method to reduce the high dimension to a lower dimension as well as controlling the FDR with three popular variable selection methods: LASSO, SCAD, and MCP. Method. The three methods with the proposed screenings were applied to prostate cancer data with presence of metastasis as the outcome. Results. Simulations showed that the three variable selection methods with the proposed screenings controlled the predefined FDR and produced high area under the receiver operating characteristic curve (AUROC) scores. In applying these methods to the prostate cancer example, LASSO and MCP selected 12 and 8 genes and produced AUROC scores of 0.746 and 0.764, respectively. Conclusions. We demonstrated that the variable selection methods with the sequential use of FDR and ISIS not only controlled the predefined FDR in the final models but also had relatively high AUROC scores.Item Open Access Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial.(Trials, 2020-02) Shepherd-Banigan, Megan; Smith, Valerie A; Lindquist, Jennifer H; Cary, Michael Paul; Miller, Katherine EM; Chapman, Jennifer G; Van Houtven, Courtney HBackground
Informal caregivers report substantial burden and depressive symptoms which predict higher rates of patient institutionalization. While caregiver education interventions may reduce caregiver distress and decrease the use of long-term institutional care, evidence is mixed. Inconsistent findings across studies may be the result of reporting average treatment effects which do not account for how effects differ by participant characteristics. We apply a machine-learning approach to randomized clinical trial (RCT) data of the Helping Invested Family Members Improve Veteran's Experiences Study (HI-FIVES) intervention to explore how intervention effects vary by caregiver and patient characteristics.Methods
We used model-based recursive partitioning models. Caregivers of community-residing older adult US veterans with functional or cognitive impairment at a single VA Medical Center site were randomized to receive HI-FIVES (n = 118) vs. usual care (n = 123). The outcomes included cumulative days not in the community and caregiver depressive symptoms assessed at 12 months post intervention. Potential moderating characteristics were: veteran age, caregiver age, caregiver ethnicity and race, relationship satisfaction, caregiver burden, perceived financial strain, caregiver depressive symptoms, and patient risk score.Results
The effect of HI-FIVES on days not at home was moderated by caregiver burden (p < 0.001); treatment effects were higher for caregivers with a Zarit Burden Scale score ≤ 28. Caregivers with lower baseline Center for Epidemiologic Studies Depression Scale (CESD-10) scores (≤ 8) had slightly lower CESD-10 scores at follow-up (p < 0.001).Conclusions
Family caregiver education interventions may be less beneficial for highly burdened and distressed caregivers; these caregivers may require a more tailored approach that involves assessing caregiver needs and developing personalized approaches.Trial registration
ClinicalTrials.gov, ID:NCT01777490. Registered on 28 January 2013.Item Open Access Illicit and nonmedical drug use among Asian Americans, Native Hawaiians/Pacific Islanders, and mixed-race individuals.(Drug and alcohol dependence, 2013-12) Wu, Li-Tzy; Blazer, Dan G; Swartz, Marvin S; Burchett, Bruce; Brady, Kathleen T; NIDA AAPI WorkgroupThe racial/ethnic composition of the United States is shifting rapidly, with non-Hispanic Asian-Americans, Native Hawaiians/Pacific Islanders (NHs/PIs), and mixed-race individuals the fastest growing segments of the population. We determined new drug use estimates for these rising groups. Prevalences among Whites were included as a comparison.Data were from the 2005-2011 National Surveys on Drug Use and Health. Substance use among respondents aged ≥ 12 years was assessed by computer-assisted self-interviewing methods. Respondents' self-reported race/ethnicity, age, gender, household income, government assistance, county type, residential stability, major depressive episode, history of being arrested, tobacco use, and alcohol use were examined as correlates. We stratified the analysis by race/ethnicity and used logistic regression to estimate odds of drug use.Prevalence of past-year marijuana use among Whites increased from 10.7% in 2005 to 11.6-11.8% in 2009-2011 (P<0.05). There were no significant yearly changes in drug use prevalences among Asian-Americans, NHs/PIs, and mixed-race people; but use of any drug, especially marijuana, was prevalent among NHs/PIs and mixed-race people (21.2% and 23.3%, respectively, in 2011). Compared with Asian-Americans, NHs/PIs had higher odds of marijuana use, and mixed-race individuals had higher odds of using marijuana, cocaine, hallucinogens, stimulants, sedatives, and tranquilizers. Compared with Whites, mixed-race individuals had greater odds of any drug use, mainly marijuana, and NHs/PIs resembled Whites in odds of any drug use.Findings reveal alarmingly prevalent drug use among NHs/PIs and mixed-race people. Research on drug use is needed in these rising populations to inform prevention and treatment efforts.Item Restricted Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.(PLoS One, 2009-08-13) Elobeid, Mai A; Padilla, Miguel A; McVie, Theresa; Thomas, Olivia; Brock, David W; Musser, Bret; Lu, Kaifeng; Coffey, Christopher S; Desmond, Renee A; St-Onge, Marie-Pierre; Gadde, Kishore M; Heymsfield, Steven B; Allison, David BBACKGROUND: Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed and Cochrane databases (2000-2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e(-lambdat)) where lambda was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. CONCLUSION/SIGNIFICANCE: Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.Item Open Access Neuronal adaptation caused by sequential visual stimulation in the frontal eye field.(J Neurophysiol, 2008-10) Mayo, J Patrick; Sommer, Marc AImages on the retina can change drastically in only a few milliseconds. A robust description of visual temporal processing is therefore necessary to understand visual analysis in the real world. To this end, we studied subsecond visual changes and asked how prefrontal neurons in monkeys respond to stimuli presented in quick succession. We recorded the visual responses of single neurons in the frontal eye field (FEF), a prefrontal area polysynaptically removed from the retina that is involved with higher level cognition. For comparison, we also recorded from small groups of neurons in the superficial superior colliculus (supSC), an area that receives direct retinal input. Two sequential flashes of light at varying interstimulus intervals were presented in a neuron's receptive field. We found pervasive neuronal adaptation in FEF and supSC. Visual responses to the second stimulus were diminished for up to half a second after the first stimulus presentation. Adaptation required a similar amount of time to return to full responsiveness in both structures, but there was significantly more neuronal adaptation overall in FEF. Adaptation was not affected by saccades, although visual responses to single stimuli were transiently suppressed postsaccadically. Our FEF and supSC results systematically document subsecond visual adaptation in prefrontal cortex and show that this adaptation is comparable to, but stronger than, adaptation found earlier in the visual system.Item Open Access Overview of FEED, the feeding experiments end-user database.(Integr Comp Biol, 2011-08) Wall, Christine E; Vinyard, Christopher J; Williams, Susan H; Gapeyev, Vladimir; Liu, Xianhua; Lapp, Hilmar; German, Rebecca ZThe Feeding Experiments End-user Database (FEED) is a research tool developed by the Mammalian Feeding Working Group at the National Evolutionary Synthesis Center that permits synthetic, evolutionary analyses of the physiology of mammalian feeding. The tasks of the Working Group are to compile physiologic data sets into a uniform digital format stored at a central source, develop a standardized terminology for describing and organizing the data, and carry out a set of novel analyses using FEED. FEED contains raw physiologic data linked to extensive metadata. It serves as an archive for a large number of existing data sets and a repository for future data sets. The metadata are stored as text and images that describe experimental protocols, research subjects, and anatomical information. The metadata incorporate controlled vocabularies to allow consistent use of the terms used to describe and organize the physiologic data. The planned analyses address long-standing questions concerning the phylogenetic distribution of phenotypes involving muscle anatomy and feeding physiology among mammals, the presence and nature of motor pattern conservation in the mammalian feeding muscles, and the extent to which suckling constrains the evolution of feeding behavior in adult mammals. We expect FEED to be a growing digital archive that will facilitate new research into understanding the evolution of feeding anatomy.Item Open Access PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs.(Biometrics, 2016-03) Ha, Min Jin; Sun, Wei; Xie, JichunEstimation of the skeleton of a directed acyclic graph (DAG) is of great importance for understanding the underlying DAG and causal effects can be assessed from the skeleton when the DAG is not identifiable. We propose a novel method named PenPC to estimate the skeleton of a high-dimensional DAG by a two-step approach. We first estimate the nonzero entries of a concentration matrix using penalized regression, and then fix the difference between the concentration matrix and the skeleton by evaluating a set of conditional independence hypotheses. For high-dimensional problems where the number of vertices p is in polynomial or exponential scale of sample size n, we study the asymptotic property of PenPC on two types of graphs: traditional random graphs where all the vertices have the same expected number of neighbors, and scale-free graphs where a few vertices may have a large number of neighbors. As illustrated by extensive simulations and applications on gene expression data of cancer patients, PenPC has higher sensitivity and specificity than the state-of-the-art method, the PC-stable algorithm.Item Open Access Science incubators: synthesis centers and their role in the research ecosystem.(PLoS Biol, 2013) Rodrigo, Allen; Alberts, Susan; Cranston, Karen; Kingsolver, Joel; Lapp, Hilmar; McClain, Craig; Smith, Robin; Vision, Todd; Weintraub, Jory; Wiegmann, BrianHow should funding agencies enable researchers to explore high-risk but potentially high-reward science? One model that appears to work is the NSF-funded synthesis center, an incubator for community-led, innovative science.Item Open Access Statistical methods for the assessment of EQAPOL proficiency testing: ELISpot, Luminex, and Flow Cytometry.(Journal of Immunological Methods, 2014-07) Rountree, Wes; Vandergrift, Nathan; Bainbridge, John; Sanchez, Ana M; Denny, Thomas NIn September 2011 Duke University was awarded a contract to develop the National Institutes of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) External Quality Assurance Program Oversight Laboratory (EQAPOL). Through EQAPOL, proficiency testing programs are administered for Interferon-γ (IFN-γ) Enzyme-linked immunosorbent spot (ELISpot), Intracellular Cytokine Staining Flow Cytometry (ICS) and Luminex-based cytokine assays. One of the charges of the EQAPOL program was to apply statistical methods to determine overall site performance. We utilized various statistical methods for each program to find the most appropriate for assessing laboratory performance using the consensus average as the target value. Accuracy ranges were calculated based on Wald-type confidence intervals, exact Poisson confidence intervals, or via simulations. Given the nature of proficiency testing data, which has repeated measures within donor/sample made across several laboratories; the use of mixed effects models with alpha adjustments for multiple comparisons was also explored. Mixed effects models were found to be the most useful method to assess laboratory performance with respect to accuracy to the consensus. Model based approaches to the proficiency testing data in EQAPOL will continue to be utilized. Mixed effects models also provided a means of performing more complex analyses that would address secondary research questions regarding within and between laboratory variability as well as longitudinal analyses.Item Open Access The Immunology Quality Assessment Proficiency Testing Program for CD3⁺4⁺ and CD3⁺8⁺ lymphocyte subsets: a ten year review via longitudinal mixed effects modeling.(Journal of Immunological Methods, 2014-07) Bainbridge, J; Wilkening, CL; Rountree, W; Louzao, R; Wong, J; Perza, N; Garcia, A; Denny, TNSince 1999, the National Institute of Allergy and Infectious Diseases Division of AIDS (NIAID DAIDS) has funded the Immunology Quality Assessment (IQA) Program with the goal of assessing proficiency in basic lymphocyte subset immunophenotyping for each North American laboratory supporting the NIAID DAIDS HIV clinical trial networks. Further, the purpose of this program is to facilitate an increase in the consistency of interlaboratory T-cell subset measurement (CD3(+)4(+)/CD3(+)8(+) percentages and absolute counts) and likewise, a decrease in intralaboratory variability. IQA T-cell subset measurement proficiency testing was performed over a ten-year period (January 2003-July 2012), and the results were analyzed via longitudinal analysis using mixed effects models. The goal of this analysis was to describe how a typical laboratory (a statistical modeling construct) participating in the IQA Program performed over time. Specifically, these models were utilized to examine trends in interlaboratory agreement, as well as successful passing of proficiency testing. Intralaboratory variability (i.e., precision) was determined by the repeated measures variance, while fixed and random effects were taken into account for changes in interlaboratory agreement (i.e., accuracy) over time. A flow cytometer (single-platform technology, SPT) or a flow cytometer/hematology analyzer (dual-platform technology, DPT) was also examined as a factor for accuracy and precision. The principal finding of this analysis was a significant (p<0.001) increase in accuracy of T-cell subset measurements over time, regardless of technology type (SPT or DPT). Greater precision was found in SPT measurements of all T-cell subset measurements (p<0.001), as well as greater accuracy of SPT on CD3(+)4(+)% and CD3(+)8(+)% assessments (p<0.05 and p<0.001, respectively). However, the interlaboratory random effects variance in DPT results indicates that for some cases DPT can have increased accuracy compared to SPT. Overall, these findings demonstrate that proficiency in and among IQA laboratories have, in general, improved over time and that platform type differences in performance do exist.Item Open Access Toward Synthesizing Our Knowledge of Morphology: Using Ontologies and Machine Reasoning to Extract Presence/Absence Evolutionary Phenotypes across Studies.(Systematic biology, 2015-11) Dececchi, T Alexander; Balhoff, James P; Lapp, Hilmar; Mabee, Paula MThe reality of larger and larger molecular databases and the need to integrate data scalably have presented a major challenge for the use of phenotypic data. Morphology is currently primarily described in discrete publications, entrenched in noncomputer readable text, and requires enormous investments of time and resources to integrate across large numbers of taxa and studies. Here we present a new methodology, using ontology-based reasoning systems working with the Phenoscape Knowledgebase (KB; kb.phenoscape.org), to automatically integrate large amounts of evolutionary character state descriptions into a synthetic character matrix of neomorphic (presence/absence) data. Using the KB, which includes more than 55 studies of sarcopterygian taxa, we generated a synthetic supermatrix of 639 variable characters scored for 1051 taxa, resulting in over 145,000 populated cells. Of these characters, over 76% were made variable through the addition of inferred presence/absence states derived by machine reasoning over the formal semantics of the source ontologies. Inferred data reduced the missing data in the variable character-subset from 98.5% to 78.2%. Machine reasoning also enables the isolation of conflicts in the data, that is, cells where both presence and absence are indicated; reports regarding conflicting data provenance can be generated automatically. Further, reasoning enables quantification and new visualizations of the data, here for example, allowing identification of character space that has been undersampled across the fin-to-limb transition. The approach and methods demonstrated here to compute synthetic presence/absence supermatrices are applicable to any taxonomic and phenotypic slice across the tree of life, providing the data are semantically annotated. Because such data can also be linked to model organism genetics through computational scoring of phenotypic similarity, they open a rich set of future research questions into phenotype-to-genome relationships.Item Open Access Using a latent variable approach to inform gender and racial/ethnic differences in cocaine dependence: a National Drug Abuse Treatment Clinical Trials Network study.(Journal of substance abuse treatment, 2010-06) Wu, Li-Tzy; Pan, Jeng-Jong; Blazer, Dan G; Tai, Betty; Stitzer, Maxine L; Woody, George EThis study applies a latent variable approach to examine gender and racial/ethnic differences in cocaine dependence, to determine the presence of differential item functioning (DIF) or item-response bias to diagnostic questions of cocaine dependence, and to explore the effects of DIF on the predictor analysis of cocaine dependence. The analysis sample included 682 cocaine users enrolled in two national multisite studies of the National Drug Abuse Treatment Clinical Trials Network (CTN). Participants were recruited from 14 community-based substance abuse treatment programs associated with the CTN, including 6 methadone and 8 outpatient nonmethadone programs. Factor and multiple indicators-multiple causes (MIMIC) procedures evaluated the latent continuum of cocaine dependence and its correlates. MIMIC analysis showed that men exhibited lower odds of cocaine dependence than women (regression coefficient, beta = -0.34), controlling for the effects of DIF, years of cocaine use, addiction treatment history, comorbid drug dependence diagnoses, and treatment setting. There were no racial/ethnic differences in cocaine dependence; however, DIF by race/ethnicity was noted. Within the context of multiple community-based addiction treatment settings, women were more likely than men to exhibit cocaine dependence. Addiction treatment research needs to further evaluate gender-related differences in drug dependence in treatment entry and to investigate how these differences may affect study participation, retention, and treatment response to better serve this population.