living-evidence.doralemon.chatgpt.site

run_meta_analysis

Fit a meta-analytic model to the current evidence base and RENDER A FOREST PLOT into the document. method: REML or DL random-effects, or FE common-effect (FE assumes one common effect; tau2 is null by model assumption, not estimated as zero). p is a two-sided test of the pooled effect against zero. Optionally exclude specific study ids (e.g. to test sensitivity to one study). Returns pooled estimate, 95% CI, p, tau^2, I^2, and heterogeneity Q.

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Input schema

{
  "type": "object",
  "properties": {
    "method": {
      "type": "string",
      "enum": [
        "REML",
        "DL",
        "FE"
      ],
      "description": "model / tau^2 estimator: REML or DL random-effects, FE common-effect (default REML)"
    },
    "exclude": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "study ids to exclude, e.g. [\"s04\"]"
    }
  },
  "additionalProperties": false
}