Outside of randomized experiments, association does not imply causation, and yet there is nothing defective about our knowledge that smoking causes lung cancer, a conclusion reached in the absence of randomized experimentation with humans. How is that possible? If observed associations do not identify causal effects in observational studies, how can a sequence of such associations become decisive? Two or more associations may each be susceptible to unmeasured biases, yet not susceptible to the same biases. An observational study has two evidence factors if it provides two comparisons susceptible to different biases that may be combined as if from independent studies of different data by different investigators, despite using the same data twice. If the two factors concur, then they may exhibit greater insensitivity to unmeasured biases than either factor exhibits on its own. Replication and Evidence Factors in Observational Studies includes four parts: A concise introduction to causal inference, making the book self-contained Practical examples of evidence factors from the health and social sciences with analyses in R The theory of evidence factors Study design with evidence factors A companion R package evident is available from CRAN.

Outside of randomized experiments, association does not imply causation, and yet there is nothing defective about our knowledge that smoking causes lung cancer, a conclusion reached in the absence of randomized experimentation with humans. How is that possible? If observed associations do not identify causal effects in observational studies, how can a sequence of such associations become decisive? Two or more associations may each be susceptible to unmeasured biases, yet not susceptible to the same biases. An observational study has two evidence factors if it provides two comparisons susceptible to different biases that may be combined as if from independent studies of different data by different investigators, despite using the same data twice. If the two factors concur, then they may exhibit greater insensitivity to unmeasured biases than either factor exhibits on its own. Replication and Evidence Factors in Observational studies has four parts: A concise introduction to causal inference, making the book self-contained. Practical examples of evidence factors from the health and social sciences with analyses in R. The theory of evidence factors. Study design with evidence factors. A companion R package evident is available from CRAN.

This thesis includes five chapters on evidence factors analysis of causal effect in various observational study settings. Each of these chapters can be read independently without knowledge of the content of any of the other chapters. Evidence factors allow for two independent analyses to be constructed from the same data set. When combining the evidence factors, the type-I error rate must be controlled to obtain valid inference. A powerful method is developed for controlling the familywise error rate for sensitivity analyses to unmeasured confounding with evidence factors. It is shown that the Bahadur efficiency of sensitivity analysis for the combined evidence is greater than for either evidence factor alone. The popular strategy of matching, for controlling the observed covariates, before inferring about the treatment effect, requires solving an optimization problem. This problem can be solved in polynomial time. In an evidence factors analysis we must consider multiple comparisons, thus the matching problem is often of matching at least three groups. This slightly different problem is much more difficult to solve. The third chapter proposes an approximation algorithm to solve this (and more practical versions of this) problem. We prove that the proposed algorithm provides a solution fast, that is provably not a lot further than the optimal solution that is difficult calculate. Two chapters that follow show the applicability of evidence factors analysis in more complicated study designs. The first of these two chapters considers a case-control study with multiple case definitions and the latter one considers studies with instrumental variables, where the instrument(s) may become invalid. The final chapter of the thesis develops a frequentist method for quantification of the degree of corroboration of causal hypothesis using the tool of evidence factors.

An observational study is an empirical investigation of the effects of treatments, policies, or exposures. It differes from an experiment in that the investigator cannot control the assignments of treatments to subjects. Scientists across a wide range of disciplines undertake such studies, and the aim of this book is to provide 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. These are 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 involved in observational studes will find this an invaluable companion to their work.

This second edition of 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. 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 organized into five parts. Chapters 2, 3, and 5 of Part I cover concisely 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, and includes an updated 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 is new to this edition; it discusses evidence factors and the computerized construction of more than one comparison group. Part V 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." This new edition features updated exploration of causal influence, with four new chapters, a new R package DOS2 designed as a companion for the book, and discussion of several of the latest matching packages for R. In particular, DOS2 allows readers to reproduce many analyses from Design of Observational Studies.

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)

Cancer screening is a prominent strategy in cancer control in the United States, yet the ability to correctly interpret cancer screening data eludes many researchers, clinicians, and policy makers. This open access primer rectifies that situation by teaching readers, in simple language and with straightforward examples, why and how the population-level cancer burden changes when screening is implemented, and how we assess whether that change is of benefit. This book provides an in-depth look at the many aspects of cancer screening and its assessment, including screening phenomena, performance measures, population-level outcomes, research designs, and other important and timely topics. Concise, accessible, and focused, Assessment of Cancer Screening: A Primer is best suited to those with education or experience in clinical research or public health in the United States - no previous knowledge of cancer screening assessment is necessary. This is the first text dedicated to cancer screening theory and methodology to be published in 20 years.