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Relevant bibliographies by topics / Binary outcome

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Academic literature on the topic 'Binary outcome'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Binary outcome"

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Heggie, D. C. "A new outcome of binary--binary scattering." Monthly Notices of the Royal Astronomical Society 318, no. 4 (2000): L61—L63. http://dx.doi.org/10.1046/j.1365-8711.2000.04027.x.

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Cope, Thomas. "The binary-outcome detection loophole." New Journal of Physics 23, no. 7 (2021): 073032. http://dx.doi.org/10.1088/1367-2630/ac10a5.

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Reed, James F. "AB/BA Crossover Trials - Binary Outcome." Journal of Modern Applied Statistical Methods 5, no. 2 (2005): 452–57. http://dx.doi.org/10.22237/jmasm/1162354680.

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Testik, Firat Y. "Outcome regimes of binary raindrop collisions." Atmospheric Research 94, no. 3 (2009): 389–99. http://dx.doi.org/10.1016/j.atmosres.2009.06.017.

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Mbekwe Yepnang, Ariane M., Agnès Caille, Sandra M. Eldridge, and Bruno Giraudeau. "Association of intracluster correlation measures with outcome prevalence for binary outcomes in cluster randomised trials." Statistical Methods in Medical Research 30, no. 8 (2021): 1988–2003. http://dx.doi.org/10.1177/09622802211026004.

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In cluster randomised trials, a measure of intracluster correlation such as the intraclass correlation coefficient (ICC) should be reported for each primary outcome. Providing intracluster correlation estimates may help in calculating sample size of future cluster randomised trials and also in interpreting the results of the trial from which they are derived. For a binary outcome, the ICC is known to be associated with its prevalence, which raises at least two issues. First, it questions the use of ICC estimates obtained on a binary outcome in a trial for sample size calculations in a subseque

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Kavelaars, Xynthia, Joris Mulder, and Maurits Kaptein. "Decision-making with multiple correlated binary outcomes in clinical trials." Statistical Methods in Medical Research 29, no. 11 (2020): 3265–77. http://dx.doi.org/10.1177/0962280220922256.

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Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all

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Rosner, Bernard, and Roy C. Milton. "Significance Testing for Correlated Binary Outcome Data." Biometrics 44, no. 2 (1988): 505. http://dx.doi.org/10.2307/2531863.

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Zhu, Han, Qingzhao Yu, and Donald E. Mercante. "A Bayesian sequential design with binary outcome." Pharmaceutical Statistics 16, no. 3 (2017): 192–200. http://dx.doi.org/10.1002/pst.1805.

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Browning, Martin, and Jesus M. Carro. "Dynamic binary outcome models with maximal heterogeneity." Journal of Econometrics 178, no. 2 (2014): 805–23. http://dx.doi.org/10.1016/j.jeconom.2013.11.005.

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Franzese, Robert J., Jude C. Hays, and Scott J. Cook. "Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes." Political Science Research and Methods 4, no. 1 (2015): 151–73. http://dx.doi.org/10.1017/psrm.2015.14.

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Spatial/spatiotemporal interdependence—that is, that outcomes, actions or choices of some unit-times depend on those of other unit-times—is substantively important and empirically ubiquitous in binary outcomes of interest across the social sciences. Estimating and interpreting binary-outcome models that incorporate such spatial/spatiotemporal dynamics directly is difficult and rarely attempted, however. This article explains the inferential challenges posed by spatiotemporal interdependence in binary-outcome models and recent advances in their estimation. Monte Carlo simulations compare the pe

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Dissertations / Theses on the topic "Binary outcome"

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Beauchamp, Marie-Eve. "Generalized linear mixed models for binary outcome data with a low proportion of occurrences." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86709.

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Many studies in epidemiology and other fields such as econometrics and social sciences give rise to correlated outcome data (e.g., longitudinal studies, meta-analyses, and multi-centre studies). Parameter estimation of generalized linear mixed models (GLMMs), which are frequently used to perform inference on correlated binary outcomes, is complicated by intractable integrals in the marginal likelihood. Penalized quasi-likelihood (PQL) and maximum likelihood estimation in conjunction with numerical integration via adaptive Gauss-Hermite quadrature (AGHQ) are estimation methods that are commonly

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Sloan, Lauren Elizabeth. "Methods for analysis of missing data using simulated longitudinal data with a binary outcome." Oklahoma City : [s.n.], 2005.

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Warn, David Edward. "Applications and extensions of Bayesian hierarchical models for meta-analysis of binary outcome data." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620461.

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Matshego, James Moeng. "The variable selection problem and the application of the roc curve for binary outcome variables." Diss., Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-08112008-104847.

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Yu, Ly-Mee. "The impact of adjustment for covariates on meta-analysis of randomised intervention studies for binary outcome." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:970440ee-b772-43d6-89c2-62e7ae3b8910.

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Background: Covariate adjustment analysis is often used in epidemiological studies but is less common in randomised controlled trials (RCTs) and RCT meta-analyses. There is a lack of consensus on whether the analysis of RCT data should adjust for important baseline covariates. The estimated treatment effect of a binary covariate can differ when logistic regression is carried out, even when the covariate is balanced between treatment groups. Objectives: The objectives of this study were to examine the factors that affect the impact of adjusted analysis in different RCT scenarios and to explore

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Gustafsson, Andreas. "Winner Prediction of Blood Bowl 2 Matches with Binary Classification." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20368.

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Being able to predict the outcome of a game is useful in many aspects. Such as,to aid designers in the process of understanding how the game is played by theplayers, as well as how to be able to balance the elements within the game aretwo of those aspects. If one could predict the outcome of games with certaintythe design process could possibly be evolved into more of an experiment basedapproach where one can observe cause and effect to some degree. It has previouslybeen shown that it is possible to predict outcomes of games to varying degrees ofsuccess. However, there is a lack of research wh

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Mukaka, Mavuto. "Comparison of statistical methods of handling missing binary outcome data in randomized controlled trials of efficacy studies." Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/14593/.

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The presence of some missing outcomes in randomized studies often complicates the estimation of measures of effect, even in well designed randomized controlled trials. The process may be complicated further when the efficacy rates are close to 0% or 100% as the standard binomial model is susceptible to model non-convergence. The main objective of this study was to compare the performance of multiple imputation (MI) and Complete Case analysis for dealing with missing binary outcomes when modeling a risk difference. Firstly, however, the binomial regression COPY method and the Cheung’s modified

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Schildcrout, Jonathan Scott. "Marginal modeling of longitudinal, binary response data : semiparametric and parametric estimation with long response series and an efficient outcome dependent sampling design /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9540.

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Hedar, Sara. "Applying Machine Learning Methods to Predict the Outcome of Shots in Football." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414774.

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The thesis investigates a publicly available dataset which covers morethan three million events in football matches. The aim of the study isto train machine learning models capable of modeling the relationshipbetween a shot event and its outcome. That is, to predict if a footballshot will result in a goal or not. By representing the shot indifferent ways, the aim is to draw conclusion regarding what elementsof a shot allows for a good prediction of its outcome. The shotrepresentation was varied both by including different numbers of eventspreceding the shot and by varying the set of features d

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Bailey, Brittney E. "Data analysis and multiple imputation for two-level nested designs." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531822703002162.

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Books on the topic "Binary outcome"

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Spreadbury, Penny. Measuring the outcomes of individualised care: The Binary Individualised Outcome Measure, a tool for clinical audit, care planning and patient documentation. Nottingham City Hospital NHS Trust, 1995.

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Press, S. James. Analysis of repeated measures experiments with binary outcomes. Rand, 1987.

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Miksza, Peter, and Kenneth Elpus. Regression. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0010.

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This chapter presents the logic and technique of analyzing data using simple linear regression and multiple linear regression. Regression is a remarkably versatile statistical procedure that can be used not only to understand whether or not variables are related to each other (as in correlation) but also for providing estimates of the direction of the relationship and of the degree to which the variables are related. Beginning with a simple bivariate case analyzing a single predictor on a single outcome, the flexibility and ability for regression to analyze increasingly complex data, including

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Hoste, Eric A. J., John A. Kellum, and Norbert Lameire. Definitions, classification, epidemiology, and risk factors of acute kidney injury. Edited by Norbert Lameire. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0220_update_001.

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The lack of a precise biochemical definition of acute kidney injury (AKI) resulted in at least 35 definitions in the medical literature, which gave rise to a wide variation in reported incidence and clinical significance of AKI, impeded a meaningful comparison of studies.The first part of this chapter describes and discusses different definitions and classification systems of AKI. Patient outcome and the need for renal replacement therapy are directly related to the severity of AKI, an observation that supports the use of a categorical staging system rather than a simple binary descriptor. The

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Berkel, Hanna, and Finn Tarp. Informality and firm performance in Myanmar. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/930-3.

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Using a novel panel survey of enterprises in Myanmar, we compare the performance of manufacturing firms by three different informality definitions. The first is binary, based on whether firms pay taxes. The second captures five categories of registration with the authorities, and the third definition relates to three groupings of the informality status of a firm’s workers. Depending on the informality concept used, formalization has positive, insignificant, and negative performance outcomes. However, our analysis shows that independent of the informality definition, differences between formali

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Charles N, Brower, and Ahmad Jawad. Part III Guide to Key Jurisdictional Issues, 18 The State’s Corruption Defence, Prosecutorial Efforts, and Anti-corruption Norms in Investment Treaty Arbitration. Oxford University Press, 2018. http://dx.doi.org/10.1093/law/9780198758082.003.0018.

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This chapter addresses two developments in the field of investment arbitration and corruption. First, it discusses the ‘binary’ or ‘harsh’ outcomes in investment arbitration awards and query whether there is scope within the field to level the playing field. It discusses cases where illegality — including corruption — has surfaced, whether as a question of jurisdiction, or of admissibility or of the merits. It then considers the viability of conditioning the State’s entitlement to assert corruption as a defence on proof of steps by the respondent State to investigate and prosecute public offic

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Book chapters on the topic "Binary outcome"

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Shoukri, Mohamed M. "Modeling Binary Outcome Data." In Analysis of Correlated Data with SAS and R. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315277738-5.

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Steyerberg, E. W. "Prediction of a binary outcome:30-day mortality after acute myocardial infarction." In Statistics for Biology and Health. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77244-8_22.

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Chen, (Din) Ding-Geng, and Xinguang Chen. "Logistic Cusp Catastrophe Regression for Binary Outcome: Method Development and Empirical Testing." In Statistical Methods for Global Health and Epidemiology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35260-8_16.

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van Kuijk, Sander M. J., Frank J. W. M. Dankers, Alberto Traverso, and Leonard Wee. "Preparing Data for Predictive Modelling." In Fundamentals of Clinical Data Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99713-1_6.

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AbstractThis is the first chapter of five that cover an introduction to developing and validating models for predicting outcomes for the individual patient. Such prediction models can be used for predicting the occurrence or recurrence of an event, or of the most likely value on a continuous outcome. We will mainly focus on the prediction of binary outcomes, such as the occurrence of a complication, recurrence of disease, the presence of metastases, remission, survival, etc. This chapter deals with the selection of an appropriate study design for a study on prediction, and on methods to manipulate the data before the statistical modelling can begin.

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Nahler, Gerhard. "binary outcomes." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_116.

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Guilkey, David K., and Peter M. Lance. "Program Impact Estimation with Binary Outcome Variables: Monte Carlo Results for Alternative Estimators and Empirical Examples." In Festschrift in Honor of Peter Schmidt. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4899-8008-3_2.

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Williams, Gail M., and Robert Ware. "Modelling Binary Outcomes." In Springer Series on Epidemiology and Public Health. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37131-8_10.

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Etzioni, Ruth, Micha Mandel, and Roman Gulati. "Binary and Categorical Outcomes." In Springer Texts in Statistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59889-1_4.

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Fitzmaurice, Garrett M., and Stuart R. Lipsitz. "Binary and Ordinal Outcomes." In Handbook of Statistical Methods for Randomized Controlled Trials. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315119694-2.

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Bryant, Randal E., and Marijn J. H. Heule. "Dual Proof Generation for Quantified Boolean Formulas with a BDD-based Solver." In Automated Deduction – CADE 28. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79876-5_25.

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AbstractExisting proof-generating quantified Boolean formula (QBF) solvers must construct a different type of proof depending on whether the formula is false (refutation) or true (satisfaction). We show that a QBF solver based on ordered binary decision diagrams (BDDs) can emit a single dual proof as it operates, supporting either outcome. This form consists of a sequence of equivalence-preserving clause addition and deletion steps in an extended resolution framework. For a false formula, the proof terminates with the empty clause, indicating conflict. For a true one, it terminates with all clauses deleted, indicating tautology. Both the length of the proof and the time required to check it are proportional to the total number of BDD operations performed. We evaluate our solver using a scalable benchmark based on a two-player tiling game.

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Conference papers on the topic "Binary outcome"

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Nagrebeckaya, Yuliya, and Vladimir Panov. "EQUIVALENCE OF TWO DEFINITIONS OF JOINT ACTION OF k BINARY FACTORS NOTION." In XIV International Scientific Conference "System Analysis in Medicine". Far Eastern Scientific Center of Physiology and Pathology of Respiration, 2020. http://dx.doi.org/10.12737/conferencearticle_5fe01d9b5fef55.02114623.

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In the formal model of the binary sufficient cause theory two definitions of a notion “joint action of k factors in a given outcome” are known. One of them appeals to a logical structure of the outcome, and another uses some formal notions of Boolean algebras theory. We discuss peculiarities of either definition and formulate their equivalence statement.

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Nagrebeckaya, Yuliya, and Vladimir Panov. "ESTIMATION OF COMPLEXITY OF EFFECTIVE ALGORITHMS FOR CHECKING A PRESENCE OF JOINT ACTION OF BINARY FACTORS." In XIV International Scientific Conference "System Analysis in Medicine". Far Eastern Scientific Center of Physiology and Pathology of Respiration, 2020. http://dx.doi.org/10.12737/conferencearticle_5fe01d9b660121.60953168.

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Effective algorithms are provided for checking presence of joint action of k factors in a given outcome which depends on n factors (k < n) and for calculation of degrees of that joint action for any k. It is demonstrated that asymptotic time complexity of the proposed algorithms does not exceed square of the input data size representing the given outcome

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Hassanpour, Negar, and Russell Greiner. "CounterFactual Regression with Importance Sampling Weights." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/815.

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Perhaps the most pressing concern of a patient diagnosed with cancer is her life expectancy under various treatment options. For a binary-treatment case, this translates into estimating the difference between the outcomes (e.g., survival time) of the two available treatment options – i.e., her Individual Treatment Effect (ITE). This is especially challenging to estimate from observational data, as that data has selection bias: the treatment assigned to a patient depends on that patient's attributes. In this work, we borrow ideas from domain adaptation to address the distributional shift betwee

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Ishihata, Masakazu, and Takanori Maehara. "Exact Bernoulli Scan Statistics using Binary Decision Diagrams." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/795.

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In combinatorial statistics, we are interested in a statistical test of combinatorial correlation, i.e., existence a subset from an underlying combinatorial structure such that the observation is large on the subset. The combinatorial scan statistics has been proposed for such a statistical test; however, it is not commonly used in practice because of its high computational cost. In this study, we restrict our attention to the case that the number of data points is moderately small (e.g., 50), the outcome is binary, and the underlying combinatorial structure is represented by a zero-suppressed

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Bredereck, Robert, and Edith Elkind. "Manipulating Opinion Diffusion in Social Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/124.

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We consider opinion diffusion in binary influence networks, where at each step one or more agents update their opinions so as to be in agreement with the majority of their neighbors. We consider several ways of manipulating the majority opinion in a stable outcome, such as bribing agents, adding/deleting links, and changing the order of updates, and investigate the computational complexity of the associated problems, identifying tractable and intractable cases.

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Novaro, Arianna, Umberto Grandi, Dominique Longin, and Emiliano Lorini. "Goal-Based Collective Decisions: Axiomatics and Computational Complexity." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/65.

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We study agents expressing propositional goals over a set of binary issues to reach a collective decision. We adapt properties and rules from the literature on Social Choice Theory to our setting, providing an axiomatic characterisation of a majority rule for goal-based voting. We study the computational complexity of finding the outcome of our rules (i.e., winner determination), showing that it ranges from Nondeterministic Polynomial Time (NP) to Probabilistic Polynomial Time (PP).

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Pasternak, Lars, and Martin Sommerfeld. "Experimental investigation of size effects in colliding droplet." In ILASS2017 - 28th European Conference on Liquid Atomization and Spray Systems. Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/ilass2017.2017.4781.

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The present work focuses on the size effect of binary collisions of PVP droplets (viscosity 5.5 mPas). The aim was the identification of the boundaries between bouncing, coalescence, stretching and reflexive separation.These boundaries are necessary for numerical simulations of droplet collisions in spray drying processes. Therefore, droplet chains were generated by droplet generators with oscillating membrane and directed towards each other at different angles for producing binary collisions. In the experiments two droplet properties (i.e. droplet size and size ratio) were varied. Two synchro

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Bredereck, Robert, Lilian Jacobs, and Leon Kellerhals. "Maximizing the Spread of an Opinion in Few Steps: Opinion Diffusion in Non-Binary Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/225.

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We consider the setting of asynchronous opinion diffusion with majority threshold: given a social network with each agent assigned to one opinion, an agent will update its opinion if more than half of its neighbors agree on a different opinion. The stabilized final outcome highly depends on the sequence in which agents update their opinion. We are interested in optimistic sequences---sequences that maximize the spread of a chosen opinion. We complement known results for two opinions where optimistic sequences can be computed in time and length linear in the number of agents. We analyze upper a

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Gallo, Elena. "Black hole X-ray binary jets." In INTERACTING BINARIES: Accretion, Evolution, and Outcomes. AIP, 2005. http://dx.doi.org/10.1063/1.2130232.

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Podsiadlowski, Philipp. "Binary Population Synthesis: Theory and Applications." In INTERACTING BINARIES: Accretion, Evolution, and Outcomes. AIP, 2005. http://dx.doi.org/10.1063/1.2130258.

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Reports on the topic "Binary outcome"

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Basu, Anirban, Norma Coe, and Cole Chapman. Comparing 2SLS vs 2SRI for Binary Outcomes and Binary Exposures. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23840.

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Vella, Francis, Roger Klein, and Chan Shen. Semiparametric selection models with binary outcomes. Institute for Fiscal Studies, 2011. http://dx.doi.org/10.1920/wp.cem.2011.3011.

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Rosen, Adam, and Andrew Chesher. An instrumental variable random coefficients model for binary outcomes. CemmapLondon, 2012. http://dx.doi.org/10.1920/wp.cem.2012.3412.

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Clarke, Paul S., and Frank Windmeijer. Identification of causal effects on binary outcomes using structural mean models. Institute for Fiscal Studies, 2010. http://dx.doi.org/10.1920/wp.cem.2010.0210.

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Identification in simple binary outcome panel data models. Cemmap, 2021. http://dx.doi.org/10.47004/wp.cem.2021.1421.

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