Buch, Englisch, 276 Seiten, Format (B × H): 234 mm x 155 mm, Gewicht: 426 g
Reihe: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Buch, Englisch, 276 Seiten, Format (B × H): 234 mm x 155 mm, Gewicht: 426 g
Reihe: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
ISBN: 978-0-367-75170-8
Verlag: Taylor & Francis Ltd
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.
Zielgruppe
Postgraduate and Professional
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
Weitere Infos & Material
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.