Browsing by Subject "Data Accuracy"

Now showing 1 - 7 of 7
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    A Bayesian Approach to Inferring Rates of Selfing and Locus-Specific Mutation.
    (Genetics, 2015-11) Redelings, Benjamin D; Kumagai, Seiji; Tatarenkov, Andrey; Wang, Liuyang; Sakai, Ann K; Weller, Stephen G; Culley, Theresa M; Avise, John C; Uyenoyama, Marcy K
    We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about reproduction under pure hermaphroditism, gynodioecy, and a model developed to describe the self-fertilizing killifish Kryptolebias marmoratus. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens sampling formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet process prior model. Our sampler is designed to accommodate additional information, including observations pertaining to the sex ratio, the intensity of inbreeding depression, and other aspects of reproduction. It can provide joint posterior distributions for the population-wide proportion of uniparental individuals, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual. Further, estimation of all basic parameters of a given model permits estimation of functions of those parameters, including the proportion of the gene pool contributed by each sex and relative effective numbers.
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    A Systematic Framework to Rapidly Obtain Data on Patients with Cancer and COVID-19: CCC19 Governance, Protocol, and Quality Assurance.
    (Cancer cell, 2020-12) COVID-19 and Cancer Consortium. Electronic address: jeremy.warner@vumc.org; COVID-19 and Cancer Consortium
    When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments.
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    Caveat emptor: the combined effects of multiplicity and selective reporting.
    (Trials, 2018-09-17) Li, Tianjing; Mayo-Wilson, Evan; Fusco, Nicole; Hong, Hwanhee; Dickersin, Kay
    Clinical trials and systematic reviews of clinical trials inform healthcare decisions. There is growing concern, however, about results from clinical trials that cannot be reproduced. Reasons for nonreproducibility include that outcomes are defined in multiple ways, results can be obtained using multiple methods of analysis, and trial findings are reported in multiple sources ("multiplicity"). Multiplicity combined with selective reporting can influence dissemination of trial findings and decision-making. In particular, users of evidence might be misled by exposure to selected sources and overly optimistic representations of intervention effects. In this commentary, drawing from our experience in the Multiple Data Sources in Systematic Reviews (MUDS) study and evidence from previous research, we offer practical recommendations to enhance the reproducibility of clinical trials and systematic reviews.
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    Critical Review of Current Approaches for Echocardiographic Reproducibility and Reliability Assessment in Clinical Research.
    (Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2016-12) Crowley, Anna Lisa; Yow, Eric; Barnhart, Huiman X; Daubert, Melissa A; Bigelow, Robert; Sullivan, Daniel C; Pencina, Michael; Douglas, Pamela S

    Background

    There is no broadly accepted standard method for assessing the quality of echocardiographic measurements in clinical research reports, despite the recognized importance of this information in assessing the quality of study results.

    Methods

    Twenty unique clinical studies were identified reporting echocardiographic data quality for determinations of left ventricular (LV) volumes (n = 13), ejection fraction (n = 12), mass (n = 9), outflow tract diameter (n = 3), and mitral Doppler peak early velocity (n = 4). To better understand the range of possible estimates of data quality and to compare their utility, reported reproducibility measures were tabulated, and de novo estimates were then calculated for missing measures, including intraclass correlation coefficient (ICC), 95% limits of agreement, coefficient of variation (CV), coverage probability, and total deviation index, for each variable for each study.

    Results

    The studies varied in approaches to reproducibility testing, sample size, and metrics assessed and values reported. Reported metrics included mean difference and its SD (n = 7 studies), ICC (n = 5), CV (n = 4), and Bland-Altman limits of agreement (n = 4). Once de novo estimates of all missing indices were determined, reasonable reproducibility targets for each were identified as those achieved by the majority of studies. These included, for LV end-diastolic volume, ICC > 0.95, CV < 7%, and coverage probability > 0.93 within 30 mL; for LV ejection fraction, ICC > 0.85, CV < 8%, and coverage probability > 0.85 within 10%; and for LV mass, ICC > 0.85, CV < 10%, and coverage probability > 0.60 within 20 g.

    Conclusions

    Assessment of data quality in echocardiographic clinical research is infrequent, and methods vary substantially. A first step to standardizing echocardiographic quality reporting is to standardize assessments and reporting metrics. Potential benefits include clearer communication of data quality and the identification of achievable targets to benchmark quality improvement initiatives.
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    Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
    (Proceedings of the National Academy of Sciences of the United States of America, 2022-04) Cramer, Estee Y; Ray, Evan L; Lopez, Velma K; Bracher, Johannes; Brennen, Andrea; Castro Rivadeneira, Alvaro J; Gerding, Aaron; Gneiting, Tilmann; House, Katie H; Huang, Yuxin; Jayawardena, Dasuni; Kanji, Abdul H; Khandelwal, Ayush; Le, Khoa; Mühlemann, Anja; Niemi, Jarad; Shah, Apurv; Stark, Ariane; Wang, Yijin; Wattanachit, Nutcha; Zorn, Martha W; Gu, Youyang; Jain, Sansiddh; Bannur, Nayana; Deva, Ayush; Kulkarni, Mihir; Merugu, Srujana; Raval, Alpan; Shingi, Siddhant; Tiwari, Avtansh; White, Jerome; Abernethy, Neil F; Woody, Spencer; Dahan, Maytal; Fox, Spencer; Gaither, Kelly; Lachmann, Michael; Meyers, Lauren Ancel; Scott, James G; Tec, Mauricio; Srivastava, Ajitesh; George, Glover E; Cegan, Jeffrey C; Dettwiller, Ian D; England, William P; Farthing, Matthew W; Hunter, Robert H; Lafferty, Brandon; Linkov, Igor; Mayo, Michael L; Parno, Matthew D; Rowland, Michael A; Trump, Benjamin D; Zhang-James, Yanli; Chen, Samuel; Faraone, Stephen V; Hess, Jonathan; Morley, Christopher P; Salekin, Asif; Wang, Dongliang; Corsetti, Sabrina M; Baer, Thomas M; Eisenberg, Marisa C; Falb, Karl; Huang, Yitao; Martin, Emily T; McCauley, Ella; Myers, Robert L; Schwarz, Tom; Sheldon, Daniel; Gibson, Graham Casey; Yu, Rose; Gao, Liyao; Ma, Yian; Wu, Dongxia; Yan, Xifeng; Jin, Xiaoyong; Wang, Yu-Xiang; Chen, YangQuan; Guo, Lihong; Zhao, Yanting; Gu, Quanquan; Chen, Jinghui; Wang, Lingxiao; Xu, Pan; Zhang, Weitong; Zou, Difan; Biegel, Hannah; Lega, Joceline; McConnell, Steve; Nagraj, VP; Guertin, Stephanie L; Hulme-Lowe, Christopher; Turner, Stephen D; Shi, Yunfeng; Ban, Xuegang; Walraven, Robert; Hong, Qi-Jun; Kong, Stanley; van de Walle, Axel; Turtle, James A; Ben-Nun, Michal; Riley, Steven; Riley, Pete; Koyluoglu, Ugur; DesRoches, David; Forli, Pedro; Hamory, Bruce; Kyriakides, Christina; Leis, Helen; Milliken, John; Moloney, Michael; Morgan, James; Nirgudkar, Ninad; Ozcan, Gokce; Piwonka, Noah; Ravi, Matt; Schrader, Chris; Shakhnovich, Elizabeth; Siegel, Daniel; Spatz, Ryan; Stiefeling, Chris; Wilkinson, Barrie; Wong, Alexander; Cavany, Sean; España, Guido; Moore, Sean; Oidtman, Rachel; Perkins, Alex; Kraus, David; Kraus, Andrea; Gao, Zhifeng; Bian, Jiang; Cao, Wei; Lavista Ferres, Juan; Li, Chaozhuo; Liu, Tie-Yan; Xie, Xing; Zhang, Shun; Zheng, Shun; Vespignani, Alessandro; Chinazzi, Matteo; Davis, Jessica T; Mu, Kunpeng; Pastore Y Piontti, Ana; Xiong, Xinyue; Zheng, Andrew; Baek, Jackie; Farias, Vivek; Georgescu, Andreea; Levi, Retsef; Sinha, Deeksha; Wilde, Joshua; Perakis, Georgia; Bennouna, Mohammed Amine; Nze-Ndong, David; Singhvi, Divya; Spantidakis, Ioannis; Thayaparan, Leann; Tsiourvas, Asterios; Sarker, Arnab; Jadbabaie, Ali; Shah, Devavrat; Della Penna, Nicolas; Celi, Leo A; Sundar, Saketh; Wolfinger, Russ; Osthus, Dave; Castro, Lauren; Fairchild, Geoffrey; Michaud, Isaac; Karlen, Dean; Kinsey, Matt; Mullany, Luke C; Rainwater-Lovett, Kaitlin; Shin, Lauren; Tallaksen, Katharine; Wilson, Shelby; Lee, Elizabeth C; Dent, Juan; Grantz, Kyra H; Hill, Alison L; Kaminsky, Joshua; Kaminsky, Kathryn; Keegan, Lindsay T; Lauer, Stephen A; Lemaitre, Joseph C; Lessler, Justin; Meredith, Hannah R; Perez-Saez, Javier; Shah, Sam; Smith, Claire P; Truelove, Shaun A; Wills, Josh; Marshall, Maximilian; Gardner, Lauren; Nixon, Kristen; Burant, John C; Wang, Lily; Gao, Lei; Gu, Zhiling; Kim, Myungjin; Li, Xinyi; Wang, Guannan; Wang, Yueying; Yu, Shan; Reiner, Robert C; Barber, Ryan; Gakidou, Emmanuela; Hay, Simon I; Lim, Steve; Murray, Chris; Pigott, David; Gurung, Heidi L; Baccam, Prasith; Stage, Steven A; Suchoski, Bradley T; Prakash, B Aditya; Adhikari, Bijaya; Cui, Jiaming; Rodríguez, Alexander; Tabassum, Anika; Xie, Jiajia; Keskinocak, Pinar; Asplund, John; Baxter, Arden; Oruc, Buse Eylul; Serban, Nicoleta; Arik, Sercan O; Dusenberry, Mike; Epshteyn, Arkady; Kanal, Elli; Le, Long T; Li, Chun-Liang; Pfister, Tomas; Sava, Dario; Sinha, Rajarishi; Tsai, Thomas; Yoder, Nate; Yoon, Jinsung; Zhang, Leyou; Abbott, Sam; Bosse, Nikos I; Funk, Sebastian; Hellewell, Joel; Meakin, Sophie R; Sherratt, Katharine; Zhou, Mingyuan; Kalantari, Rahi; Yamana, Teresa K; Pei, Sen; Shaman, Jeffrey; Li, Michael L; Bertsimas, Dimitris; Skali Lami, Omar; Soni, Saksham; Tazi Bouardi, Hamza; Ayer, Turgay; Adee, Madeline; Chhatwal, Jagpreet; Dalgic, Ozden O; Ladd, Mary A; Linas, Benjamin P; Mueller, Peter; Xiao, Jade; Wang, Yuanjia; Wang, Qinxia; Xie, Shanghong; Zeng, Donglin; Green, Alden; Bien, Jacob; Brooks, Logan; Hu, Addison J; Jahja, Maria; McDonald, Daniel; Narasimhan, Balasubramanian; Politsch, Collin; Rajanala, Samyak; Rumack, Aaron; Simon, Noah; Tibshirani, Ryan J; Tibshirani, Rob; Ventura, Valerie; Wasserman, Larry; O'Dea, Eamon B; Drake, John M; Pagano, Robert; Tran, Quoc T; Ho, Lam Si Tung; Huynh, Huong; Walker, Jo W; Slayton, Rachel B; Johansson, Michael A; Biggerstaff, Matthew; Reich, Nicholas G
    Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
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    Priorities to Overcome Barriers Impacting Data Science Application in Emergency Care Research.
    (Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 2019-01) Puskarich, Michael A; Callaway, Clif; Silbergleit, Robert; Pines, Jesse M; Obermeyer, Ziad; Wright, David W; Hsia, Renee Y; Shah, Manish N; Monte, Andrew A; Limkakeng, Alexander T; Meisel, Zachary F; Levy, Phillip D
    For a variety of reasons including cheap computing, widespread adoption of electronic medical records, digitalization of imaging and biosignals, and rapid development of novel technologies, the amount of health care data being collected, recorded, and stored is increasing at an exponential rate. Yet despite these advances, methods for the valid, efficient, and ethical utilization of these data remain underdeveloped. Emergency care research, in particular, poses several unique challenges in this rapidly evolving field. A group of content experts was recently convened to identify research priorities related to barriers to the application of data science to emergency care research. These recommendations included: 1) developing methods for cross-platform identification and linkage of patients; 2) creating central, deidentified, open-access databases; 3) improving methodologies for visualization and analysis of intensively sampled data; 4) developing methods to identify and standardize electronic medical record data quality; 5) improving and utilizing natural language processing; 6) developing and utilizing syndrome or complaint-based based taxonomies of disease; 7) developing practical and ethical framework to leverage electronic systems for controlled trials; 8) exploring technologies to help enable clinical trials in the emergency setting; and 9) training emergency care clinicians in data science and data scientists in emergency care medicine. The background, rationale, and conclusions of these recommendations are included in the present article.
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    The impact of respiratory gating on improving volume measurement of murine lung tumors in micro-CT imaging.
    (PloS one, 2020-01) Blocker, SJ; Holbrook, MD; Mowery, YM; Sullivan, DC; Badea, CT
    Small animal imaging has become essential in evaluating new cancer therapies as they are translated from the preclinical to clinical domain. However, preclinical imaging faces unique challenges that emphasize the gap between mouse and man. One example is the difference in breathing patterns and breath-holding ability, which can dramatically affect tumor burden assessment in lung tissue. As part of a co-clinical trial studying immunotherapy and radiotherapy in sarcomas, we are using micro-CT of the lungs to detect and measure metastases as a metric of disease progression. To effectively utilize metastatic disease detection as a metric of progression, we have addressed the impact of respiratory gating during micro-CT acquisition on improving lung tumor detection and volume quantitation. Accuracy and precision of lung tumor measurements with and without respiratory gating were studied by performing experiments with in vivo images, simulations, and a pocket phantom. When performing test-retest studies in vivo, the variance in volume calculations was 5.9% in gated images and 15.8% in non-gated images, compared to 2.9% in post-mortem images. Sensitivity of detection was examined in images with simulated tumors, demonstrating that reliable sensitivity (true positive rate (TPR) ≥ 90%) was achievable down to 1.0 mm3 lesions with respiratory gating, but was limited to ≥ 8.0 mm3 in non-gated images. Finally, a clinically-inspired "pocket phantom" was used during in vivo mouse scanning to aid in refining and assessing the gating protocols. Application of respiratory gating techniques reduced variance of repeated volume measurements and significantly improved the accuracy of tumor volume quantitation in vivo.