Replication and evidence factors in observational studies
著者
書誌事項
Replication and evidence factors in observational studies
（Monographs on statistics and applied probability, 167）（A Chapman & Hall book）
CRC Press, 2021
- : hbk
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注記
Includes bibliographical references (p. 225-243) and index
内容説明・目次
内容説明
Association does not imply causation, yet some causal conclusions are firmly established based on associations found in observational studies. How does that happen?
A study has two evidence factors if it provides two statistically independent tests of one causal hypothesis, susceptible to different biases. Two evidence factors can jointly provide quantifiably stronger evidence than either factor can provide on its own.
The first book about evidence factors.
Examples are drawn from epidemiology, economics, medical research and other fields. Data from these examples is available in a companion R package that reproduces many of the analyses.
Self-contained, presenting needed background from causal inference, statistics and mathematics.
Part 1 of the book presents concepts, methods and applications using limited mathematics.
The theory of evidence factors is presented in a separate, second part of the book.
Mathematics required for the theory is presented from the beginning.
目次
I Background: Aspects of Causal Inference. 1. Causal Inference in Randomized Experiments. 2. Causal Inference in Observational Studies. 3. Replication and its Limits. II Evidence Factors in Practice. 4. Examples of Studies with Evidence Factors. 5. Simple Analyses with Evidence Factors. 6. Planned Analyses with Evidence Factors. 7. Dependent P-Values. 8. Treatment Assignments as Permutations. 9. Sets of Treatment Assignments. 10. Probability Distributions for Treatment Assignments. 11. Factors. 12.*Groups of Permutation Matrices. IV Aspects of Design. 13. Constructing Matched Samples with Evidence Factors. 14. Design Elements for Evidence Factors.
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