prec_or returns the sample size or the precision for the
provided proportions.
prec_or(
p1,
p2,
n1 = NULL,
r = 1,
conf.width = NULL,
conf.level = 0.95,
method = c("gart", "woolf", "indip_smooth"),
...
)risk among exposed.
risk among unexposed.
number of patients in exposed group.
allocation ratio (relative size of unexposed and exposed cohort
(n2 / n1)).
precision (the full width of the confidence interval).
confidence level.
Exactly one of indip_smooth (default),
gart, or woolf. Methods can be abbreviated.
other arguments to uniroot (e.g. tol).
Object of class "presize", a list of arguments (including the computed one) augmented with method and note elements.
Exactly one of the parameters n1 or conf.width must be passed as NULL,
and that parameter is determined from the other.
Woolf (woolf), Gart (gart), and Independence-smoothed logit
(indip_smooth) belong to a general family of adjusted confidence
intervals, adding 0 (woolf) to each cell, 0.5 (gart) to each cell, or an
adjustment for each cell based on observed data (independence-smoothed). In
gart and indip_smooth, estimate of the CI is not possible if \(p1 = 0\), in
which case the OR becomes 0, but the lower level of the CI is > 0. Further,
if \(p1 = 1\) and \(p2 < 1\), or if \(p1 > 0\) and \(p2 = 0\), the OR
becomes \(\infty\), but the upper limit of the CI is finite. For the
approximate intervals, gart and indip_smooth are the
recommended intervals (Fagerland et al. 2011).
uniroot is used to solve n for the woolf, gart, and
indip_smooth method.
Fagerland MW, Lydersen S, Laake P (2015). Recommended confidence intervals for two independent binomial proportions. Statistical Methods in Medical Research, 24(2):224-254. doi:10.1177/0962280211415469 .
# 10\% events in one group, 15\% in the other, 200 participants total
# (= 100 in each group), estimate confidence interval width
prec_or(p1 = .1, p2 = .15, n1 = 200/2)
#> Warning: more than one method was chosen, 'indip_smooth' will be used
#>
#> precision for an odds ratio with indip_smooth confidence interval
#>
#> p1 p2 n1 n2 ntot r or lwr upr conf.width conf.level
#> 1 0.1 0.15 100 100 200 1 0.6296296 0.2707144 1.478198 1.207484 0.95
# formula by Gart
prec_or(p1 = .1, p2 = .15, n1 = 200/2, method = "gart")
#>
#> precision for an odds ratio with gart confidence interval
#>
#> p1 p2 n1 n2 ntot r or lwr upr conf.width conf.level
#> 1 0.1 0.15 100 100 200 1 0.6296296 0.2770396 1.478456 1.201417 0.95
# formula by Woolf
prec_or(p1 = .1, p2 = .15, n1 = 200/2, method = "woolf")
#>
#> precision for an odds ratio with woolf confidence interval
#>
#> p1 p2 n1 n2 ntot r or lwr upr conf.width conf.level
#> 1 0.1 0.15 100 100 200 1 0.6296296 0.2682267 1.477979 1.209753 0.95
# 10\% odds in one group, 15\% in the other, desired CI width of 0.1,
# estimate N
prec_or(p1 = .1, p2 = .15, conf.width = .1)
#> Warning: more than one method was chosen, 'indip_smooth' will be used
#>
#> sample size for an odds ratio with indip_smooth confidence interval
#>
#> p1 p2 n1 n2 ntot r or lwr upr conf.width
#> 1 0.1 0.15 11570.15 11570.15 23140.3 1 0.6296296 0.5816375 0.6816375 0.1
#> conf.level
#> 1 0.95
# formula by Gart
prec_or(p1 = .1, p2 = .15, conf.width = .1, method = "gart")
#>
#> sample size for an odds ratio with gart confidence interval
#>
#> p1 p2 n1 n2 ntot r or lwr upr conf.width
#> 1 0.1 0.15 11569.79 11569.79 23139.58 1 0.6296296 0.581704 0.681704 0.1
#> conf.level
#> 1 0.95
# formula by Woolf
prec_or(p1 = .1, p2 = .15, conf.width = .1, method = "woolf")
#>
#> sample size for an odds ratio with woolf confidence interval
#>
#> p1 p2 n1 n2 ntot r or lwr upr
#> 1 0.1 0.15 11570.28 11570.28 23140.56 1 0.6296296 0.5816118 0.6816118
#> conf.width conf.level
#> 1 0.1 0.95