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Printable Handouts
Navigable Slide Index
- Introduction
- Outline
- Post-randomization adjustment
- Why post-randomization adjustment?
- Example (1)
- Example (2)
- Regression based method
- A scheme demonstrating the two models (1)
- Concerns on the conventional method (I)
- A scheme demonstrating the two models (2)
- Concerns on the conventional method (II)
- Concerns on the conventional method (III)
- Conventional method (III) - continue
- Alternative methods (I)
- Alternative methods (II)
- Alternative methods (II) - continue
- Alternative methods (III)
- Alternative methods (III) - continue
- Simulation
- Simulation (cont.): conventional regression method
- Simulation (cont.): simple stratification (1)
- FGP/TG values distribution in 4 sub-groups (1)
- Simulation (cont.): simple stratification (2)
- Simulation (cont.): principle stratification (1)
- FGP/TG values distribution in 4 sub-groups (2)
- Simulation (cont.): principle stratification (2)
- Conclusions
- References
Topics Covered
- Adjustment for post-randomization variables: uses in practice to obtain additional information in randomized experiments
- Potential problems of the conventional regression based post-randomization adjustment method: reliability, precision and causality
- Available alternative methods that could provide either more powerful, less biased evaluation or more appropriate assessment for causality
- Examples of the application of the different post-randomization adjustment methods
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Talk Citation
Chen, X. (2007, October 1). A note on the application of post-randomization adjustment [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 13, 2024, from https://doi.org/10.69645/HMXS4946.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Xun Chen has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.