11.Meta analysis02.Individual participants - sporedata/researchdesigneR GitHub Wiki

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

  • When authors from articles to be included in a meta-analysis are willing to provide patient-level data.

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

  1. Trialists or study principal investigators willing to share raw study to be combined into a single database.

3. Algorithm: how does the method work?

Model mechanics

  1. Individual participant meta-analysis combines individual participant data from multiple studies (trials, registries, etc). Since the data are much more granular than simply bringing together results extracted from publications. This granularity and the possibility of running patient-level models make individual patient meta-analysis one of the top designs in the evidence-based pyramid.

Reporting guidelines for Methods

Data science packages

Suggested companion methods

  • Bayesian multilevel models are important to account for the common variance within each study dataset.

Learning materials

  1. Books *
  2. Articles
    • Random-effects meta-analysis [3].

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] Stewart LA, Clarke M, Rovers M, Riley RD, Simmonds M, Stewart G, Tierney JF. Preferred reporting items for a systematic review and meta-analysis of individual participant data: the PRISMA-IPD statement. Jama. 2015 Apr 28;313(16):1657-65.

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

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