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Printable Handouts
Navigable Slide Index
- Introduction
- Trial design
- Types of randomisation (1)
- Types of randomisation (2)
- From individual to cluster randomisation
- Why cluster randomisation?
- Contamination
- Individual vs. cluster randomisation
- EASYcare study - elderly with geriatric problems
- EASYcare study - design
- EASYcare study - individual randomisation?
- EASYcare study - cluster randomisation? (1)
- EASYcare study - cluster randomisation? (2)
- Dilemma
- Solution?
- Pseudo cluster randomisation (rando) (1)
- Pseudo cluster randomisation (rando) (2)
- Addressing EASYcare problems by psedo cluster
- Pseudo cluster rando: less contamination
- Pseudo cluster rando: less selection bias
- Pseudo cluster rando: better recruitment
- Conclusion for EASYcare study
- Efficiency and power of pseudo cluster rando
- Estimators treatment results
- Simple, unweighted mean
- Minimal contamination mean
- Compromise: weighted mean
- Sample size cluster randomisation
- Sample size pseudo cluster randomisation
- D-pseudo cluster (minimal variance approach)
- Sample size EASYcare study
- Cluster randomised design
- Pseudo cluster design
- Individual randomisation
- Sample size comparison
- Efficiency: contamination 20%, cluster size 10
- Efficiency: contamination 20%, cluster size 30
- Efficiency: contamination 40%, cluster size 10
- Efficiency: contamination 40%, cluster size 30
- Conclusion
- Analysis pseudo cluster trial
- Inappropriate methods
- Mixed models
- EASYcare study: variables
- EASYcare study: dataset
- EASYcare study: analysis (1)
- EASYcare study: analysis (2)
- EASYcare study: analysis (3)
- EASYcare study: specify fixed part of model (1)
- EASYcare study: specify fixed part of model (2)
- EASYcare study: specify random part of model (1)
- EASYcare study: specify random part of model (2)
- EASYcare study: request model statistics (1)
- EASYcare study: request model statistics (2)
- Analysis program - SPSS code
- Output: independent variables
- Output: test of fixed effects
- Output: estimates fixed effects (1)
- Output: within and between variance
- Analysis program: SAS code
- Literature
Topics Covered
- Individual randomization versus cluster randomization
- Contamination as a possible problem with individual randomization
- Possible problems with cluster randomization: selection bias and slow recruitment control arm
- Pseudo cluster randomization as an alternative method: less contamination, less selection bias, better recruitment and statistically efficient
- Formula to calculate power
- Analysis methods
Links
Series:
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Talk Citation
Borm, G. (2007, October 1). Pseudo cluster randomization [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 13, 2024, from https://doi.org/10.69645/DQSJ2777.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. George Borm has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.