11.Meta analysis04.Randomized trials - sporedata/researchdesigneR GitHub Wiki

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

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

  • At least two, previously published, interventional clinical trials with adequate quality and focusing on the same intervention (including times when interventions were measured), sample characteristics (inclusion/exclusion criteria), and outcome measures.

3. Algorithm: how does the method work?

Model mechanics

The goal of meta-analyses for clinical trials is to augment systematic reviews as a way to quantitatively bring together the results of multiple previous clinical trials, both evaluating their heterogeneity as well as defining a single measure of effect when that measure might be both clinically and statistically plausible.

The number needed to treat (NNT) is commonly used as a mechanism to communicate with clinicians and patients, but it has a number of issues such as very large confidence intervals (often unreported), the fact that it is a group measure used for an individual - see Problems with NNT.

Reporting guidelines for Methods

Data science packages

Suggested companion methods

  • Can serve as prior information for the simulation of Bayesian adaptive trials or as input for the sample size calculation for frequentist trials.
  • Are often one of the main pillars behind clinical practice guidelines.

Learning materials

  1. Books

    • Doing Meta-Analysis in R [3].
    • Applied Meta-Analysis with R [4].

  2. Articles

    • Random-effects meta-analysis: summarizing evidence with caveats [4].

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

Reporting guidelines for Results

5. SporeData-specific

Templates

Data science functions

References

[1] Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS med. 2009 Jul 21;6(7):e1000097.

[2] Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D. D. (2019). Doing Meta-Analysis in R: A Hands-on Guide.

[3] Chen DG, Peace KE. Applied meta-analysis with R. CRC Press; 2013 May 3.

[4] Serghiou S, Goodman SN. Random-effects meta-analysis: summarizing evidence with caveats. Jama. 2019 Jan 22;321(3):301-2.

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