Download or read book Propensity Score Estimation with Random Forests written by Hei Ning Cham and published by . This book was released on 2013 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
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 Performance of the Propensity Score Methods Using Random Forest and Logistic Regression Approaches on the Treatment Effect Estimation in Observational Study written by and published by . This book was released on 2017 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: The propensity score (PS) is the probability of a subject receiving the treatment given the baseline covariates. People with the same propensity score tend to have the same distribution of covariates. Thus, propensity score related methods can be used to eliminate the systematic difference between treatment and control group so that improving the causal inferences in the observational study. In this project, a series of simulation studies are conducted to evaluate two widely used propensity score methods, matching and inverse probability of treatment weighting (IPTW), on their relative ability to estimate the treatment effect from non-randomized trials. One observes that the random forest based propensity score weighting can yield more promising treatment effect estimates compared with other PS methods. Besides that, simulated samples are also implemented to compare the performance of several matching methods on the balancing the covariates. It turns out that logistic regression based propensity score matching can reduce most of systematic differences between treatment and control group although it is not the top performer in the causal effect estimation. Finally, we illustrate the application of the propensity score methods discussed in the paper with an empirical example.
Download or read book Estimating Average Treatment Effects With Propensity Scores Estimated With Four Machine Learning Procedures written by Kip Brown and published by . This book was released on 2018 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: The increased availability of claims data allows one to build high dimensional datasets, rich in covariates, for accurately estimating treatment effects in medical and epidemiological cohort studies. This paper shows the full potential of machine learning for the estimation of average treatment effects with propensity score methods in a context rich and high dimensional datasets. Methods: Four different methods are used to estimate average treatment effects in the context of time to event outcomes. The four methods explored in this study are LASSO, Random Forest, Gradient Descent Boosting and Artificial Neural networks. Simulations based on an actual medical claims data set are used to assess the efficiency of these methods. The simulations are performed with over 100, 000 observations and 1,100 explanatory variables. Each method is tested on 500 datasets that are created from the original dataset, allowing us to report the mean and standard deviation of estimated average treatment effects. Results: The results are very promising for all four methods; however, LASSO, Random Forest and Gradient Boosting seem to be performing better than Random Forest. Conclusion: Machine Learning methods can be helpful for observational studies that use the propensity score when a very large number of covariates are available, the total number of observations is large, and the dependent event rare. This is an important result given the availability of big data related to Health Economics and Outcomes Research (HEOR) around the world.
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 Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation written by Daniel Goller and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a radius matching framework. Considering two popular methods, the results are ambiguous: We find that using LASSO based logit models to estimate the propensity score delivers more credible results than conventional methods in small and medium sized high dimensional datasets. However, the usage of Random Forests to estimate the propensity score may lead to a deterioration of the performance in situations with a low treatment share. The application reveals a positive effect of the training programme on days in employment for long-term unemployed. While the choice of the "first stage" is highly relevant for settings with low number of observations and few treated, machine learning and conventional estimation becomes more similar in larger samples and higher treatment shares.
Download or read book Nonlinear Estimation and Classification written by David D. Denison and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.
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 COMPSTAT 2008 written by Paula Brito and published by Springer Science & Business Media. This book was released on 2008-08-11 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: 18th Symposium Held in Porto, Portugal, 2008
Download or read book Probability Estimation in Random Forests written by Chunyang Li and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Random Forests is a useful ensemble approach that provides accurate predictions for classification, regression and many different machine learning problems. Classification has been a very useful and popular application for Random Forests. However, it is preferable to have the probability of a membership rather than the simple knowledge that one belongs to whichever group. Votes and the regression method are current probability estimation methods that have been developed in Random Forests. In this thesis, we introduce two new methods, proximity weighting and the out-of-bag method, trying to improve the current methods. Several different simulations are designed to evaluate the new methods and compare them with the old ones. Finally, we use real data sets from UCI machine learning repository to further evaluate and compare those methods.
Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
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 Propensity Score Analysis written by Shenyang Guo and published by SAGE. This book was released on 2015 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides readers with a systematic review of the origins, history, and statistical foundations of Propensity Score Analysis (PSA) and illustrates how it can be used for solving evaluation and causal-inference problems.
Download or read book Targeted Learning written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2011-06-17 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
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 Quantitative Psychology written by Marie Wiberg and published by Springer Nature. This book was released on with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Reviewer s Guide to Quantitative Methods in the Social Sciences written by Gregory R. Hancock and published by Routledge. This book was released on 2018-11-15 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Reviewer’s Guide to Quantitative Methods in the Social Sciences provides evaluators of research manuscripts and proposals in the social and behavioral sciences with the resources they need to read, understand, and assess quantitative work. 35 uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The second edition of this valuable resource updates readers on each technique’s key principles, appropriate usage, underlying assumptions and limitations, providing reviewers with the information they need to offer constructive commentary on works they evaluate. Written by methodological and applied scholars, this volume is also an indispensable author’s reference for preparing sound research manuscripts and proposals.
