Statistical Techniques for Estimating Causal Effects in Biomedical Research PDF Download
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Author: Claudia Coscia Requena
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Causal inference methods are statistical techniques used to analyse the causal effect of a treatment/exposure on an outcome. Their use is increasing in the last decade, especially in the framework of observational studies where the no randomization of the treatment/exposure may lead to confounding bias. These methods present great advantages versus classic regression models due to their capability of reducing and controlling for confounding bias.This thesis begins with the use of known techniques applied in real clinical scenarios, second, a lack of developed statistical methods to estimate causal effects in complex epidemiological scenarios is noted. These findings support the main objective of this thesis, which is the development of causal inference methods to better understand and diagnose clinical and epidemiological outcomes. A comparison between the Propensity Score and classic regression models was made using an Intensive Care Unit database where it was shown that, in presence of confounding bias, Propensity Score performed better. Moreover, based on a systematic review and metaanalysis, causal estimates from Propensity Score and Randomized Controlled Trials were compared. It was observed that similar estimations were obtained in both approaches...
Author: Claudia Coscia Requena
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Causal inference methods are statistical techniques used to analyse the causal effect of a treatment/exposure on an outcome. Their use is increasing in the last decade, especially in the framework of observational studies where the no randomization of the treatment/exposure may lead to confounding bias. These methods present great advantages versus classic regression models due to their capability of reducing and controlling for confounding bias.This thesis begins with the use of known techniques applied in real clinical scenarios, second, a lack of developed statistical methods to estimate causal effects in complex epidemiological scenarios is noted. These findings support the main objective of this thesis, which is the development of causal inference methods to better understand and diagnose clinical and epidemiological outcomes. A comparison between the Propensity Score and classic regression models was made using an Intensive Care Unit database where it was shown that, in presence of confounding bias, Propensity Score performed better. Moreover, based on a systematic review and metaanalysis, causal estimates from Propensity Score and Randomized Controlled Trials were compared. It was observed that similar estimations were obtained in both approaches...
Author: Guido W. Imbens
Publisher: Cambridge University Press
ISBN: 0521885884
Category : Business & Economics
Languages : en
Pages : 647
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Book Description
This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
Author: Barbara Schneider
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 160
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Book Description
Explains the value of quasi-experimental techniques that can be used to approximate randomized experiments. The goal is to describe the logic of causal inference for researchers and policymakers who are not necessarily trained in experimental and quasi-experimental designs and statistical techniques.
Author: Guido W. Imbens
Publisher: Cambridge University Press
ISBN: 1316094391
Category : Mathematics
Languages : en
Pages : 647
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Book Description
Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.
Author: Hua He
Publisher: Springer
ISBN: 3319412590
Category : Medical
Languages : en
Pages : 324
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Book Description
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.
Author: Guanglei Hong
Publisher: John Wiley & Sons
ISBN: 1118332563
Category : Mathematics
Languages : en
Pages : 443
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Book Description
Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.
Author: Ji-qian Fang
Publisher: World Scientific
ISBN: 9811228884
Category : Medical
Languages : en
Pages : 1159
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Book Description
This book consists of four parts with 32 chapters adapted for four short courses, from the basic to the advanced levels of medical statistics (biostatistics), ideal for biomedical students. Part 1 is a compulsory course of Basic Statistics with descriptive statistics, parameter estimation and hypothesis test, simple correlation and regression. Part 2 is a selective course on Study Design and Implementation with sampling survey, interventional study, observational study, diagnosis study, data sorting and article writing. Part 3 is a specially curated course of Multivariate Analyses with complex analyses of variance, variety of regressions and classical multivariate analyses. Part 4 is a seminar course on Introduction to Advanced Statistical Methods with meta-analysis, time series, item response theory, structure equation model, multi-level model, bio-informatics, genetic statistics and data mining.The main body of each chapter is followed by five practical sections: Report Writing, Case Discrimination, Computer Experiments, Frequently Asked Questions and Summary, and Practice & Think. Moreover, there are 2 attached Appendices, Appendix A includes Introductions to SPSS, Excel and R respectively, and Appendix B includes all the programs, data and printouts for Computer Experiments in addition to the Tests for Review and the reference answers for Case Discrimination as well as Practice & Think..This book can be used as a textbook for biomedical students at both under- and postgraduate levels. It can also serve as an important guide for researchers, professionals and officers in the biomedical field.
Author: Alfred DeMaris
Publisher: Springer Science & Business Media
ISBN: 1461477921
Category : Medical
Languages : en
Pages : 231
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Book Description
Converting Data into Evidence: A Statistics Primer for the Medical Practitioner provides a thorough introduction to the key statistical techniques that medical practitioners encounter throughout their professional careers. These techniques play an important part in evidence-based medicine or EBM. Adherence to EBM requires medical practitioners to keep abreast of the results of medical research as reported in their general and specialty journals. At the heart of this research is the science of statistics. It is through statistical techniques that researchers are able to discern the patterns in the data that tell a clinical story worth reporting. The authors begin by discussing samples and populations, issues involved in causality and causal inference, and ways of describing data. They then proceed through the major inferential techniques of hypothesis testing and estimation, providing examples of univariate and bivariate tests. The coverage then moves to statistical modeling, including linear and logistic regression and survival analysis. In a final chapter, a user-friendly introduction to some newer, cutting-edge, regression techniques will be included, such as fixed-effects regression and growth-curve modeling. A unique feature of the work is the extensive presentation of statistical applications from recent medical literature. Over 30 different articles are explicated herein, taken from such journals. With the aid of this primer, the medical researcher will also find it easier to communicate with the statisticians on his or her research team. The book includes a glossary of statistical terms for easy access. This is an important reference work for the shelves of physicians, nurses, nurse practitioners, physician’s assistants, medical students, and residents.
Author: Tyler J. VanderWeele
Publisher: Oxford University Press, USA
ISBN: 0199325871
Category : Mathematics
Languages : en
Pages : 729
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Book Description
A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences.
Author: Donald B. Rubin
Publisher: Cambridge University Press
ISBN: 1139458507
Category : Mathematics
Languages : en
Pages : 5
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Book Description
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.
