Cluster-Randomized Experiment Evaluation Task

AKA: Group Randomized Experiment Analysis.
Context:
- It can range from being a One-Factor Cluster Randomized Experiment Analysis to being a Two-Factor Cluster Randomized Analysis to being a Many-Factor Cluster Randomized Analysis.
- It can range from being a Crossover Cluster-Randomized Experiment Analysis to being a Non-Crossover Cluster-Randomized Analysis.
- It can range from being a Categorical Outcome Group Randomized Experiment Evaluation to being a Continuous Outcome Group Randomized Experiment Evaluation.
- It can be solved by a Cluster-Randomized Experiment Analysis System (that implements a Cluster-Randomized Experiment Analysis Algorithm.
- …
Example(s):
- a Crossover-based Randomized eEPC Predictor Lift Analysis Experiment Analysis.
- …
Counter-Example(s):
- a Subject-level Randomized Experiment Analysis Task.
- a Cluster Non-Randomized Experiment Analysis Task.
See: Placebo-Treatment Cluster-Randomized Experiment.

References

http://en.wikipedia.org/wiki/Cluster_randomised_controlled_trial
- … Disadvantages compared with individually randomised controlled trials include greater complexity in design and analysis, and a requirement for more participants to obtain the same statistical power. Specifically, the cluster randomised designs introduce dependence (or clustering) between individual units sampled. An example would be an educational intervention in which schools are randomised to one of several new teaching methods. When comparing differences in outcome achieved under the new methods, researchers must account for the fact that two students sampled from a single school are more likely to be similar (in terms of outcomes) than two students sampled from different schools. Multilevel or similar statistical models are typically used to correct for this non-independence.

(Donner & Klar, 2000) ⇒ Allan Donner, and Neil Klar. (2000). “Design and Analysis of Cluster Randomization Trials in Health Research.." Wiley. ISBN:0470711000
- QUOTE: The increasing popularity of this design among health researchers over the past two decades has led to an extensive body of methodology on the subject. This is the first book to present a systematic and united treatment of this topic; it contains distinctive chapters on the history of cluster randomized trials, ethical issues and reporting guidelines.

(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

(Murray, 1998) ⇒ David Murray. (1998). “Design and Analysis of Group Randomized Trials." Oxford University Press. ISBN:0195120361

(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.