Given two treatment effects of A vs. C and B vs. C derive the treatment effects of A vs. B using the Bucher method. Two-sided confidence interval and Z-test p-value are also calculated. Treatment effects and standard errors should be in log scale for hazard ratio, odds ratio, and risk ratio. Treatment effects and standard errors should be in natural scale for risk difference and mean difference.
bucher(trt, com, conf_lv = 0.95)# S3 method for maicplus_bucher
print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)
a list with 5 elements,
a scalar, point estimate of the treatment effect
a scalar, standard error of the treatment effect
a scalar, lower confidence limit of a two-sided CI
with prescribed nominal level by conf_lv
a scalar, upper confidence limit of a two-sided CI
with prescribed nominal level by conf_lv
p-value of Z-test, with null hypothesis that
est
is zero
a list of two scalars for the study with the
experimental arm. 'est'
is the point estimate and 'se'
is the standard error of the treatment effect.
For time-to-event data, 'est'
and 'se'
should be point estimate and
standard error of the log hazard ratio.
For binary data, 'est'
and 'se'
should be point estimate and
standard error of the log odds ratio, log risk ratio, or risk
difference.
For continuous data, 'est'
and 'se'
should be point estimate and
standard error of the mean difference.
same as trt
, but for the study with the
control arm
a numerical scalar, prescribe confidence level to derive two-sided confidence interval for the treatment effect
maicplus_bucher
object
an integer, number of decimal places for point estimate and derived confidence limits
an integer, number of decimal places to display Z-test p-value
whether the treatment effect and confidence interval should be exponentiated. This applies to relative treatment effects. Default is set to false.
not used
print(maicplus_bucher)
: Print method for maicplus_bucher
objects
trt <- list(est = log(1.1), se = 0.2)
com <- list(est = log(1.3), se = 0.18)
result <- bucher(trt, com, conf_lv = 0.9)
print(result, ci_digits = 3, pval_digits = 3)
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