Randomized Experiment Evaluation Task

Context:
- It can range from being a Subject-level Randomized Experiment Analysis to being a Cluster-level Randomized Experiment Analysis.
- It can range from being a Single-Factor Randomized Experiment Analysis to being a Factorial Randomized Experiment Analysis.
- It can range from being a Stable Randomized Experiment Analysis to being a Crossover Randomized Experiment Analysis ..
- It can range from being a Categorical Outcome Randomized Experiment Evaluation to being a Continuous Outcome Randomized Experiment Evaluation.
- It can be performed by a Randomized Experiment Evaluation System (that implements a Randomized Experiment Evaluation Algorithm.
- It can try to Maximize Evaluation Validity.
- It can try to Minimize Evaluation Bias.
Example(s):
- a Randomized Experiment with Baseline and Post-Treatment Measures Evaluation Task.
See: Randomized Experiment Evaluation Algorithm, Hypothesis Testing Task.

References

http://en.wikipedia.org/wiki/Randomized_controlled_trial#Analysis_of_data_from_RCTs
- Regardless of the statistical methods used, important considerations in the analysis of RCT data include:
  - Whether a RCT should be stopped early due to interim results. For example, RCTs may be stopped early if an intervention produces "larger than expected benefit or harm," or if "investigators find evidence of no important difference between experimental and control interventions."
  - The extent to which the groups can be analyzed exactly as they existed upon randomization (i.e., whether a so-called “intention-to-treat analysis” is used). A "pure" intention-to-treat analysis is "possible only when complete outcome data are available" for all randomized subjects;^[1] when some outcome data are missing, options include analyzing only cases with known outcomes and using imputed data. Nevertheless, the more that analyses can include all participants in the groups to which they were randomized, the less bias that an RCT will be subject to.
  - Whether subgroup analysis should be performed. These are "often discouraged" because multiple comparisons may produce false positive findings that cannot be confirmed by other studies.