Cluster-Randomized Experiment Evaluation Algorithm

AKA: Group Randomized Experiment Analysis Algorithm.
Context:
- It can range from being a Categorical Outcome Cluster Randomized Experiment Evaluation Algorithm to being a Continuous Outcome Cluster Randomized Experiment Evaluation Algorithm.
See: Cluster Randomized Experiment Evaluation System.

References

(Turner et al., 2006) ⇒ Rebecca M Turner, Ian R White, and Tim Croudace. (2006). “Analysis of Cluster Randomized Cross-over Trial Data: A Comparison of Methods." Wiley Online Library. doi:10.1002/sim.2537

(Donner & Klar, 2000) ⇒ Allan Donner, and Neil Klar. (2000). “Design and Analysis of Cluster Randomization Trials in Health Research.." Wiley. ISBN:0470711000

(Donner & Klar, 1994) ⇒ Allan Donner, and Neil Klar. (1994). “Cluster Randomization Trials in Epidemiology: Theory and Application." Elsevier. doi:10.1016/0378-3758(94)90188-0
- QUOTE: It is becoming increasingly common for epidemiologists to consider randomizing intact clusters (e.g. families, schools, communities) rather than individuals in experimental trials. Reasons are diverse, but include administrative convenience, a desire to reduce the effect of treatment contamination and the need to avoid ethical issues which might otherwise arise. Dependencies among cluster members typical of such designs must be considered when determining sample size and analyzing the resulting data. Well-known methods such as generalized least squares can be used to analyze continuous outcome data, while methods for the analysis of binary outcome data and correlated failure time data are in the development stage. The purpose of this paper is to review methods used in the design and analysis of cluster randomization trials applied in health sciences research.