Design of Observational Studies

  • Paul R. Rosenbaum

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Beginnings

    1. Front Matter
      Pages 1-1
    2. Paul R. Rosenbaum
      Pages 3-20
    3. Paul R. Rosenbaum
      Pages 21-63
    4. Paul R. Rosenbaum
      Pages 65-94
    5. Paul R. Rosenbaum
      Pages 95-112
    6. Paul R. Rosenbaum
      Pages 113-145
    7. Paul R. Rosenbaum
      Pages 147-149
  3. Matching

    1. Front Matter
      Pages 151-151
    2. Paul R. Rosenbaum
      Pages 153-161
    3. Paul R. Rosenbaum
      Pages 163-186
    4. Paul R. Rosenbaum
      Pages 187-195
    5. Paul R. Rosenbaum
      Pages 197-205
    6. Paul R. Rosenbaum
      Pages 207-221
    7. Paul R. Rosenbaum
      Pages 223-235
    8. Paul R. Rosenbaum
      Pages 237-253
  4. Design Sensitivity

    1. Front Matter
      Pages 255-255
    2. Paul R. Rosenbaum
      Pages 257-274
    3. Paul R. Rosenbaum
      Pages 275-285
    4. Paul R. Rosenbaum
      Pages 287-298

About this book

Introduction

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."

Paul R. Rosenbaum is the Robert G. Putzel Professor of Statistics at the Wharton School of the University of Pennsylvania. He is a fellow of the American Statistical Association. In 2003, he received the George W. Snedecor Award from the Committee of Presidents of Statistical Societies. He is a senior fellow of the Leonard Davis Institute of Health Economics and a Research Associate at the Population Studies Center, both at the University of Pennsylvania. The second edition of his book, Observational Studies, was published by Springer in 2002.

Keywords

Analysis Causal inference Statistical Inference natural experiment observational study propensity score sensitivity analysis

Authors and affiliations

  • Paul R. Rosenbaum
    • 1
  1. 1.Wharton School University of PennsylvaniaPhiladelphiaU.S.A.

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-1213-8
  • Copyright Information Springer-Verlag New York 2010
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-1212-1
  • Online ISBN 978-1-4419-1213-8
  • Series Print ISSN 0172-7397
  • About this book