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.
Usage
bucher(trt, com, conf_lv = 0.95)
# S3 method for class 'maicplus_bucher'
print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)
Arguments
- trt
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.- com
same as
trt
, but for the study with the control arm- conf_lv
a numerical scalar, prescribe confidence level to derive two-sided confidence interval for the treatment effect
- x
maicplus_bucher
object- ci_digits
an integer, number of decimal places for point estimate and derived confidence limits
- pval_digits
an integer, number of decimal places to display Z-test p-value
- exponentiate
whether the treatment effect and confidence interval should be exponentiated. This applies to relative treatment effects. Default is set to false.
- ...
not used
Value
a list with 5 elements,
- est
a scalar, point estimate of the treatment effect
- se
a scalar, standard error of the treatment effect
- ci_l
a scalar, lower confidence limit of a two-sided CI with prescribed nominal level by
conf_lv
- ci_u
a scalar, upper confidence limit of a two-sided CI with prescribed nominal level by
conf_lv
- pval
p-value of Z-test, with null hypothesis that
est
is zero