Rによるメタ解析/ネットワークメタ解析

Calculation of common effect and random effects estimates (risk ratio, odds ratio, risk difference, arcsine difference, or diagnostic odds ratio) for meta-analyses with binary outcome data. Mantel-Haenszel, inverse variance, Peto method, generalised linear mixed model (GLMM), and sample size method are available for pooling. For GLMMs, the rma.glmm function from R package metafor (Viechtbauer, 2010) is called internally. Number of events in experimental group or true positives in diagnostic study.Number of observations in experimental group or number of ill participants in diagnostic study.Number of events in control group or false positives in diagnostic study.Number of observations in control group or number of healthy participants in diagnostic study.An optional vector with study labels.An optional data frame containing the study information, i.e., event.e, n.e, event.c, and n.c.An optional vector specifying a subset of studies to be used.An optional vector specifying studies to exc

Draws a forest plot in the active graphics window (using grid graphics system). An object of class metabind.A character vector specifying (additional) columns to be plotted on the left side of the forest plot or a logical value (see Details).A character vector specifying labels for (additional) columns on left side of the forest plot (see Details).A character vector specifying (additional) columns to be plotted on the right side of the forest plot or a logical value (see Details).A character vector specifying labels for (additional) columns on right side of the forest plot (see Details).A logical indicating whether overall summaries should be plotted. This argument is useful in a meta-analysis with subgroups if summaries should only be plotted on group level.A logical indicating whether subgroup results should be shown in forest plot. This argument is useful in a meta-analysis with subgroups if summaries should not be plotted on group level.Either a logical value indicating whether to