14.Randomization13.Pragmatic - sporedata/researchdesigneR GitHub Wiki

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

Pragmatic trials are used whenever the researcher wants to decrease the gap between the trial study protocol and the real-world practice.

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

Aside from the randomization, there should be little difference between study protocol and real-world practice in terms of inclusion/exclusion, intervention/control, and even the measurement.

3. Algorithm: how does the method work?

Model mechanics

Pragmatic trials impose very few restrictions on inclusion and exclusion criteria, making the intervention reasonably similar to what would be used in the real-world, also making assessment non-intrusive.

Reporting guidelines

Guidelines for the Content of Statistical Analysis Plans in Clinical Trials [1].

Data science packages

Suggested companion methods

Learning materials

Books *
Articles
- PRECIS-2 is a tool to assist researchers to evaluate where they would like their trial to be on the explanatory-pragmatics continuum.
- Pragmatic trials practical answers to “real-world” questions [2].
- Common references for randomized designs

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

Pragmatic trials are designed to assess the effectiveness of interventions in the full spectrum of real-life routine practice conditions and clinical settings to maximize applicability and generalizability. As such, they tend to answer policymakers' most relevant question - comparative effectiveness of interventions in routine practice, informing them of a treatment's cost in real-life situations. On the other hand, comparative effectiveness is geared toward identifying the best intervention option(s). As a result, interventions here are, at the very least, compared based on specific outcome measures so that the benefits and harms are compared.

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] 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.

[2] Sox HC, Lewis RJ. Pragmatic trials practical answers to “real-world” questions. Jama. 2016 Sep 20;316(11):1205-6.