Testing Alternative Regression Frameworks for Predictive Modeling of Healthcare Costs PDF Download
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Author: Ian Duncan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Predictive models of healthcare costs have become mainstream in much healthcare actuarial work. The Affordable Care Act requires the use of predictive modeling-based risk-adjuster models to transfer revenue between different health exchange participants. While the predictive accuracy of these models has been investigated in a number of studies, the accuracy and use of models for applications other than risk adjustment has not been the subject of much investigation. We investigate predictive modeling of future healthcare costs using a number of different statistical techniques. Our analysis was performed based on a dataset of 30,000 insureds containing claims information from two contiguous years. The dataset contains over a hundred covariates for each insured, including detailed breakdown of past costs and causes encoded via coexisting condition (CC) flags. We discuss statistical models for the relationship between next-year costs and medical and cost information to predict the mean and quantiles of future cost, ranking risks and identifying most predictive covariates. A comparison of multiple models is presented, including (in addition to the traditional linear regression model underlying risk adjusters) Lasso GLM, multivariate adaptive regression splines, random forests, decision trees, and boosted trees. A detailed performance analysis shows that the traditional regression approach does not perform well and that more accurate models are possible.
Author: Ian Duncan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Predictive models of healthcare costs have become mainstream in much healthcare actuarial work. The Affordable Care Act requires the use of predictive modeling-based risk-adjuster models to transfer revenue between different health exchange participants. While the predictive accuracy of these models has been investigated in a number of studies, the accuracy and use of models for applications other than risk adjustment has not been the subject of much investigation. We investigate predictive modeling of future healthcare costs using a number of different statistical techniques. Our analysis was performed based on a dataset of 30,000 insureds containing claims information from two contiguous years. The dataset contains over a hundred covariates for each insured, including detailed breakdown of past costs and causes encoded via coexisting condition (CC) flags. We discuss statistical models for the relationship between next-year costs and medical and cost information to predict the mean and quantiles of future cost, ranking risks and identifying most predictive covariates. A comparison of multiple models is presented, including (in addition to the traditional linear regression model underlying risk adjusters) Lasso GLM, multivariate adaptive regression splines, random forests, decision trees, and boosted trees. A detailed performance analysis shows that the traditional regression approach does not perform well and that more accurate models are possible.
Author: Russ B Altman
Publisher: World Scientific
ISBN: 9811215642
Category : Computers
Languages : en
Pages : 764
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Book Description
The Pacific Symposium on Biocomputing (PSB) 2020 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2020 will be held on January 3 -7, 2020 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2020 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
Author: Marek Pagac
Publisher: Springer Nature
ISBN: 3031656563
Category :
Languages : en
Pages : 497
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Book Description
Author: Onur Baser
Publisher:
ISBN: 9780615630632
Category :
Languages : en
Pages : 91
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Book Description
Statistical modeling methods for pharmaceutical health economics and outcomes research, including discussion and programming examples.
Author: Francis Yin Yee Lau
Publisher:
ISBN: 9781550586015
Category : Medical care
Languages : en
Pages : 487
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Book Description
To order please visit https://onlineacademiccommunity.uvic.ca/press/books/ordering/
Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 1107029872
Category : Business & Economics
Languages : en
Pages : 565
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Book Description
This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.
Author: L. R. Lopes
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 0521760119
Category : Business & Economics
Languages : en
Pages : 585
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Book Description
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Author: Lori Beth Chibnik
Publisher:
ISBN:
Category :
Languages : en
Pages : 1368
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Book Description
Abstract: With 6 million pregnancies a year in the US, one of the most prevalent screening tests is prenatal screening for Down syndrome. A woman's risk of carrying an affected fetus is estimated using maternai age and 7 markers. In this thesis, we examine the standard method for estimating a woman's risk which multiplies an a priori risk based on maternai age atone by a likelihood ratio which reflects the increased odds of carrying an affected fetus based on the measured markers. This method ignores the correlations among the individual markers and between the markers and the a priori risk. We show that this assumption of independence among the markers leads to biased estimates of absolute risk. Second, we propose logistic regression analysis as an alternative method for combining the markers. Using simulated datasets, we show that absolute risk estimates from logistic regression have less variability and better calibration than the standard method and perform as well as the standard method in terms of predictive accuracy, producing sensitivities as high as 97% with false positive fractions as low as 2%. Next, we use generalized additive models (GAMs) to assess non-linearity in the relationships between maternai age and the individual markers and risk of carrying an affected fetus. Based on the results of the GAM models, we modified the logistic regression model to account for the non-linear relationships and evaluated this new model on simulated datasets. The modified logistic regression model produced higher sensitivities and lower false positive fractions as compared to the standard method and original logistic model. Finally, we use the novel methods of decision curve and net benefit analyses to compare predictive models across various thresholds for positive screening results. We developed a measure of public health cost that quantifies the number of unnecessary invasive procedures that must be performed to detect one affected pregnancy. Ultimately we find that the logistic regression model is easier to understand and use, performs better than the standard method for estimating a woman's risk of carrying an affected fetus and if implemented would decrease the number of unnecessary invasive procedures performed each year.
Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 595
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Book Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
