observational studies springer series in statistics

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Design Of Observational Studies

Author : Paul R. Rosenbaum
ISBN : 9781441912138
Genre : Mathematics
File Size : 35. 52 MB
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An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. Design of Observational Studies is divided into four parts. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum’s Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Part II includes a chapter on matching in R. In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Part IV discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher’s striking advice for observational studies, "make your theories elaborate." The second edition of his book, Observational Studies, was published by Springer in 2002.

Observational Studies

Author : Paul R. Rosenbaum
ISBN : 9781475736922
Genre : Mathematics
File Size : 64. 23 MB
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A sound statistical account of the principles and methods for the design and analysis of observational studies. Readers are assumed to have a working knowledge of basic probability and statistics, but otherwise the account is reasonably self- contained. Throughout there are extended discussions of actual observational studies to illustrate the ideas discussed, drawn from topics as diverse as smoking and lung cancer, lead in children, nuclear weapons testing, and placement programs for students. As a result, many researchers will find this an invaluable companion in their work.

Statistical Causal Inferences And Their Applications In Public Health Research

Author : Hua He
ISBN : 9783319412597
Genre : Medical
File Size : 86. 48 MB
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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.

Epidemiology And Medical Statistics

Author :
ISBN : 0080554210
Genre : Mathematics
File Size : 86. 76 MB
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This volume, representing a compilation of authoritative reviews on a multitude of uses of statistics in epidemiology and medical statistics written by internationally renowned experts, is addressed to statisticians working in biomedical and epidemiological fields who use statistical and quantitative methods in their work. While the use of statistics in these fields has a long and rich history, explosive growth of science in general and clinical and epidemiological sciences in particular have gone through a see of change, spawning the development of new methods and innovative adaptations of standard methods. Since the literature is highly scattered, the Editors have undertaken this humble exercise to document a representative collection of topics of broad interest to diverse users. The volume spans a cross section of standard topics oriented toward users in the current evolving field, as well as special topics in much need which have more recent origins. This volume was prepared especially keeping the applied statisticians in mind, emphasizing applications-oriented methods and techniques, including references to appropriate software when relevant. · Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis

Propensity Score Matching Methods For Non Experimental Causal Studies

Author : Rajeev H. Dehejia
ISBN : IND:30000060941535
Genre : Evaluation research (Social action programs)
File Size : 45. 77 MB
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This paper considers causal inference and sample selection bias in non-experimental settings in which: (i) few units in the non-experimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We propose the use of propensity score matching methods and implement them using data from the NSW experiment. Following Lalonde (1986), we pair the experimental treated units with non-experimental comparison units from the CPS and PSID and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. We show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units

Amstat News

Author :
ISBN : UOM:39015055740198
Genre : Statistics
File Size : 78. 4 MB
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Artificial Intelligence And Statistics 2001

Author : Tommi Sakari Jaakkola
ISBN : 155860748X
Genre : Computers
File Size : 79. 2 MB
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Artificial Intelligence and Statistics 2001: Proceedings of the Eighth Internatinoal Workshop.

The Propensity Score

Author : Boris Augurzky
ISBN : CORNELL:31924088100783
Genre : Business & Economics
File Size : 28. 65 MB
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Propensity score matching is a prominent strategy to reduce imbalance in observational studies. However, if imbalance is considerable and the control reservoir is small, either one has to match one control to several treated units or, alternatively, discard many treated persons. The first strategy tends to increase standard errors of the estimated treatment effects while the second might produce a matched sample that is not anymore representative of the original one. As an alternative approach, this paper argues to carefully reconsider the selection equation upon which the propensity score estimates are based. Often, all available variables that rule the selection process are included into the selection equation. Yet, it would suffice to concentrate on only those exhibiting a large impact on the outcome under scrutiny, as well. This would introduce more stochastic noise making treatment and comparison group more similar. We assess the advantages and disadvantages of the latter approach in a simulation study.

Mathematical Reviews

Author :
ISBN : UVA:X006195256
Genre : Mathematics
File Size : 21. 12 MB
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Disentangling Treatment Effects Of Polish Active Labor Market Policies

Author : Jochen Kluve
ISBN : CORNELL:31924088123678
Genre : Labor market
File Size : 49. 12 MB
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