14.Randomization12.Randomization schedules - sporedata/researchdesigneR GitHub Wiki

1. Use cases: in which situations should I use this method?

  • The goal behind randomization is to allocate confounding factors equally among intervention arms. Randomization will avoid an association between potential confounders and the intervention, therefore avoiding bias of measured and unmeasured variables.

2. Input: what kind of data does the method require?

  • Number of study participants to be randomized, the presence of clustering and strata.
  • How the randomization will be delivered: through the electronic data capture, envelopes, etc

3. Algorithm: how does the method work?

Model mechanics

Reporting guidelines

  • Most common types of randomization include:

    • simple - equivalent to flipping a coin.
    • complete - fixed number of subjects to each intervention.
    • blocked - complete random allocation for groups of subjects, e.g., for patients with a given characteristic that is considered an important potential confounder. Examples include gender, race, etc.
    • cluster - assignment by subject clusters, such as a site.
    • block and cluster - combining the previous two randomization units.
    • multi-arm trials might require setting different randomization characteristics for each arm.

  • When there are a large number of strata in the randomization, one might consider using minimization randomization algorithms, although the implementation complexity will be increased [1].

  • Guidelines for the Content of Statistical Analysis Plans in Clinical Trials [2].

Data science packages

Suggested companion methods

Learning materials

  1. Books *
  2. Articles

4. Output: how do I interpret this method's results?

Mock conclusions or most frequent format for conclusions reached at the end of a typical analysis.

Tables, plots, and their interpretation

5. SporeData-specific

Templates

Data science functions

References

[1] Jin M, Polis A, Hartzel J. Algorithms for minimization randomization and the implementation with an R package. 2019 May 30:1-1.

[2] Gamble C, Krishan A, Stocken D, Lewis S, Juszczak E, Doré C, Williamson PR, Altman DG, Montgomery A, Lim P, Berlin J. Guidelines for the Content of Statistical Analysis Plans in Clinical Trials. Jama. 2017 Dec 19;318(23):2337-43.

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