Download or read book Propensity Score Estimation with Data Mining Techniques written by Bryan S. B. Keller and published by . This book was released on 2013 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has traditionally been the most frequently used method for modeling selection in PSA. There are, however, circumstances under which logistic regression may not perform well. The most important disadvantage of a propensity score (PS) estimation approach that uses logistic regression is the need for iterative specification of the model, which can be rather time intensive and comes with no guarantee of success, in particular with many covariates. A careful review of the burgeoning PS estimation literature has shown that the neural network and the support vector machine (SVM) are promising alternatives to logistic regression which avoid the need for respecification because they automatically model nonlinearities in the selection response surface, and are well suited for high-dimensional data. These two methods, although promising, are heretofore largely or completely empirically untested in this context. Through simulation, this study examines the conditions under which logistic regression is relatively robust to model misspecification and the conditions under which the neural network or the support vector machine will provide a less biased estimate of the effect of a treatment. Researchers evaluate through simulation, and make available a program written in R which carries out a cross-validated grid search for the optimal tuning parameters for the data mining methods based on maximizing the balance as opposed to minimizing the prediction error. The results of the simulation study clearly demonstrate that the misspecification of the PS model via logistic regression leads to the potential for gross bias in the estimate of the treatment effect when there are nonlinear or nonadditive confounders. The data mining techniques were less biased and had smaller mean square error in that case. The simulation study further explores the effect of the number of covariates and the number and strength of higher order confounders on the performance of the PS estimation methods. The authors provide recommendations based on the simulation study results in hopes of guiding researchers to make informed decisions about which propensity score estimation technique to use for their given situation in order to maximize the accuracy and efficiency of research. A table is appended.

Download or read book Data Mining Alternatives to Logistic Regression for Propensity Score Estimation written by and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Logistic regression has traditionally been the most frequently used method for modeling selection in propensity score analysis. In theory, any method which relates a binary variable to predictors would be a suitable alternative to logistic regression. In this dissertation I review the extant studies which focus on the evaluation of data mining approaches to propensity score estimation in order to identify theoretical or empirical bases for informing the direction of future research. I conduct two simulation studies which use the findings from the literature review to inform their design and a case study which provides an applied example demonstrating the use of the methods. In the first simulation study, neural networks were found to outperform a linear logistic regression model in terms of bias and mean squared error when the models used to generate the selection and outcome both contained nonlinear confounding terms. In the second simulation study, neural networks and random forests were compared with linear logistic regression in a factorial design that included 1000 simulation replications in 240 cells across six factors. Results of the second simulation study revealed that the data mining techniques for propensity score estimation were much more effective and precise when paired with inverse probability weighting than optimal full matching, while the opposite held true for linear logistic regression. Over all simulation cells of Study 2, the propensity score estimation method that was associated with the best balance on first-order terms was the least biased when the selection model was linear and the propensity score estimation method that was associated with the best balance on second-order terms (these were data mining methods, without exception) was the least biased when the selection model was more complex. This result underscores the importance of checking balance on higher-order terms and using a more flexible approach to propensity score estimation than linear logistic regression.

Download or read book Performance of Parametric Vs Data Mining Methods for Estimating Propensity Scores with Multilevel Data written by Meng Fan and published by . This book was released on 2020 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are several limitations in this study. First, this study did not consider varied correlation between covariates. Future research can be done to incorporate varied correlations among covariates. Second, balanced cluster size scenarios were created in this study. It is worth exploring the effect of the imbalance on the estimation of treatment effect. Third, this study included only propensity score weighting as the conditioning method. Future research can assess the performance of data mining approaches to estimate the propensity score using matching and stratification conditioning methods. Fourth, when using GBM to generate the propensity score in this study, only one algorithm specification was specified. Further research should include different algorithm specifications for GBM with multilevel data.

Download or read book Propensity Score Analysis written by Wei Pan and published by Guilford Publications. This book was released on 2015-04-07 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).

Download or read book Propensity Score Methods and Applications written by Haiyan Bai and published by SAGE Publications. This book was released on 2018-11-20 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise, introductory text, Propensity Score Methods and Applications describes propensity score methods (PSM) and how they are used to balance the distributions of observed covariates between treatment conditions as a means to reduce selection bias. This new QASS title specifically focuses on the procedures of implementing PSM for research in social sciences, instead of merely demonstrating the effectiveness of the method. Using succinct and approachable language to introduce the basic concepts of PSM, authors Haiyan Bai and M. H. Clark present basic concepts, assumptions, procedures, available software packages, and step-by-step examples for implementing PSM using real-world data, with exercises at the end of each chapter allowing readers to replicate examples on their own.

Download or read book Propensity Score Analysis written by Wei Pan and published by Guilford Publications. This book was released on 2015-03-18 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (www.guilford.com/pan-materials).

Download or read book Data Mining Techniques written by Gordon S. Linoff and published by John Wiley & Sons. This book was released on 2011-03-23 with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

Download or read book Practical Propensity Score Methods Using R written by Walter Leite and published by SAGE Publications. This book was released on 2016-10-28 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Propensity Score Methods Using R by Walter Leite is a practical book that uses a step-by-step analysis of realistic examples to help students understand the theory and code for implementing propensity score analysis with the R statistical language. With a comparison of both well-established and cutting-edge propensity score methods, the text highlights where solid guidelines exist to support best practices and where there is scarcity of research. Readers will find that this scaffolded approach to R and the book’s free online resources help them apply the text’s concepts to the analysis of their own data.

Download or read book Data Mining Techniques in CRM written by Konstantinos K. Tsiptsis and published by John Wiley & Sons. This book was released on 2011-08-24 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

Download or read book Some Statistical and Data Mining Techniques for Analyzing Perioperative Medical Data written by Briahnna Austin (Graduate student) and published by . This book was released on 2021 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This thesis will dive into statistical analysis and data mining of perioperative medical data, which is data collected between initial consultation with a doctor to the recovery period. I will use a variety of techniques from survival analysis (Kaplan-Meier estimation and curve, accelerated failure time regression models, and Cox proportional hazard model), propensity score analysis (logistic regression with nearest-neighbor matching), and data mining (CART, C5.0, and random forest). Illustrative examples for each technique will be presented. Datasets for applications will be taken from three articles published in peer-reviewed medical journals, for which I am the primary author or a co-author. The thesis will not merely duplicate the published analysis, but build upon it, involving more statistical methodology.

Download or read book Handbook of Matching and Weighting Adjustments for Causal Inference written by José R. Zubizarreta and published by CRC Press. This book was released on 2023-04-11 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.

Download or read book 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering written by D. Jude Hemanth and published by Springer Nature. This book was released on 2023-07-02 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: As general, this book is a collection of the most recent, quality research papers regarding applications of Artificial Intelligence and Applied Mathematics for engineering problems. The papers included in the book were accepted and presented in the 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2022), which was held in Baku, Azerbaijan (Azerbaijan Technical University) between May 20 and 22, 2022. Objective of the book content is to inform the international audience about the cutting-edge, effective developments and improvements in different engineering fields. As a collection of the ICAIAME 2022 event, the book gives consideration for the results by especially intelligent system formations and the associated applications. The target audience of the book is international researchers, degree students, practitioners from industry, and experts from different engineering disciplines.

Download or read book Applied Multivariate Statistical Concepts written by Debbie L. Hahs-Vaughn and published by Routledge. This book was released on 2016-12-01 with total page 812 pages. Available in PDF, EPUB and Kindle. Book excerpt: More comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today’s research. Ideal for courses on multivariate statistics/analysis/design, advanced statistics or quantitative techniques taught in psychology, education, sociology, and business, the book also appeals to researchers with no training in multivariate methods. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps reader’s master key concepts so they can implement and interpret results generated by today’s sophisticated software. Annotated screenshots from SPSS and other packages are integrated throughout. Designed for course flexibility, after the first 4 chapters, instructors can use chapters in any sequence or combination to fit the needs of their students. Each chapter includes a ‘mathematical snapshot’ that highlights the technical components of each procedure, so only the most crucial equations are included. Highlights include: -Outlines, key concepts, and vignettes related to key concepts preview what’s to come in each chapter -Examples using real data from education, psychology, and other social sciences illustrate key concepts -Extensive coverage of assumptions including tables, the effects of their violation, and how to test for each technique -Conceptual, computational, and interpretative problems mirror the real-world problems students encounter in their studies and careers -A focus on data screening and power analysis with attention on the special needs of each particular method -Instructions for using SPSS via screenshots and annotated output along with HLM, Mplus, LISREL, and G*Power where appropriate, to demonstrate how to interpret results -Templates for writing research questions and APA-style write-ups of results which serve as models -Propensity score analysis chapter that demonstrates the use of this increasingly popular technique -A review of matrix algebra for those who want an introduction (prerequisites include an introduction to factorial ANOVA, ANCOVA, and simple linear regression, but knowledge of matrix algebra is not assumed) -www.routledge.com/9780415842365 provides the text’s datasets preformatted for use in SPSS and other statistical packages for readers, as well as answers to all chapter problems, Power Points, and test items for instructors

Download or read book Quantitative Psychology Research written by L. Andries van der Ark and published by Springer. This book was released on 2015-08-08 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: These research articles from the 79th Annual Meeting of the Psychometric Society (IMPS) cover timely quantitative psychology topics, including new methods in item response theory, computerized adaptive testing, cognitive diagnostic modeling, and psychological scaling. Topics within general quantitative methodology include structural equation modeling, factor analysis, causal modeling, mediation, missing data methods, and longitudinal data analysis. These methods will appeal, in particular, to researchers in the social sciences. The 79th annual meeting took place in Madison, WI between July 21nd and 25th, 2014. Previous volumes to showcase work from the Psychometric Society’s Meeting are New Developments in Quantitative Psychology: Presentations from the 77th Annual Psychometric Society Meeting (Springer, 2013) and Quantitative Psychology Research: The 78th Annual Meeting of the Psychometric Society (Springer, 2015).​

Download or read book Statistical Causal Inferences and Their Applications in Public Health Research written by Hua He and published by Springer. This book was released on 2016-10-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in statistics, biostatistics, and computational biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.

Download or read book Matched Sampling for Causal Effects written by Donald B. Rubin and published by Cambridge University Press. This book was released on 2006-09-04 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.

Download or read book Methods in Comparative Effectiveness Research written by Constantine Gatsonis and published by CRC Press. This book was released on 2017-02-24 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care (IOM 2009). CER is conducted to develop evidence that will aid patients, clinicians, purchasers, and health policy makers in making informed decisions at both the individual and population levels. CER encompasses a very broad range of types of studies—experimental, observational, prospective, retrospective, and research synthesis. This volume covers the main areas of quantitative methodology for the design and analysis of CER studies. The volume has four major sections—causal inference; clinical trials; research synthesis; and specialized topics. The audience includes CER methodologists, quantitative-trained researchers interested in CER, and graduate students in statistics, epidemiology, and health services and outcomes research. The book assumes a masters-level course in regression analysis and familiarity with clinical research.