Description Usage Arguments Details See Also
Simple indirect (Bucher) meta-analysis
1 2 |
abTE |
Numeric. The treatment effect for a vs b. e.g. log OR, log HR, mean difference |
se.abTE |
Numeric. The standard error of the treatment effect for a vs b, e.g. se of log OR |
cbTE |
Numeric. The treatment effect for c vs b. e.g. log OR, log HR, mean difference |
se.cbTE |
Numeric. The standard error of the treatment effect for c vs b, e.g. se of log OR |
effect |
Character string describing the effect measure, e.g. 'Rate Ratio' or 'log Odds Ratio' |
model |
Character string indicating whether abTE and cbTE come from a fixed effect model or a random effect model |
continuous |
Logical (TRUE/FALSE) indicating whether the effect measure is continuous (mean difference) or a ratio measure (odds ratio, hazard ratio etc). |
intervention |
Character string. Name of the intervention treatment |
comparator |
Character string. Name of the comparator treatment |
common |
Character string. Name of the common comparator that links the indirect comparison, e.g. placebo |
backtransf |
Logical indicating whether the results should be exponentiated or not. If abTE and cbTE are on the log scale (e.g. log hazard ratio) set this to TRUE to return the exponentiated results (e.g. hazard ratio). If TRUE this will return both the log estimates and the exponentiated estimates |
ab.studies |
Character string giving the names of studies in the A vs B comparison separated by commas |
cb.studies |
Character string giving the names of studies in the C vs B comparison separated by commas |
Calculate an indirect estimate of the relative effect of two treatments using the Bucher method
The inputs are the relative treatment effects for two pairs of treatments linked by a common comparator. For example, if you have the relative effects (e.g. log hazard ratio) for treatment A vs placebo and treatment C vs placebo then this function will return the relative effect of treatment A compared to treatment C.
This function is mainly intended to be called from doBucher
but can
be used directly if required.
effect
, model
, intervention
, comparator
and
common
are used for labels only. The results depend only on
abTE
, cbTE
and the respective standard errors.
@return A data frame with the following columns:
Intervention
The name of the intervention
Comparator
The name of the comparator
Common
The name of the common treatment linking intervention
and comparator
Effect
The type of effect measure. Takes the
value of the effect
argument
Model
The type of model. Should be 'Fixed', 'Random' or NA
log.TE.ind
The treatment effect on log scale, e.g. log OR
log.lower.ind
, log.upper.ind
The upper and lower 95% confidence
intervals for the log treatment effect
se.log.TE.ind
The standard error for the log treatment effect
TE.ind
, lower.ind
, upper.ind
The treatment effect with lower
and upper confidence intervals backtransformed to a linear scale
n.studies
The number of studies included in the analysis
Studies
The names of the studies included in the analysis
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