Nothing
tests/testthat/test-differences.R
In presize: Precision Based Sample Size Calculation
context("Differences")
test_that("errors", {
expect_error(prec_riskratio(.1, 1.1, r = 1, conf.width = .1))
expect_error(prec_riskratio(1.1, .1, r = 1, conf.width = .1))
expect_error(prec_riskratio(1.1, r = 1, conf.width = .1))
expect_error(prec_riskratio(1.1, r = 1))
expect_error(prec_riskratio(p1 = 36/40, p2 = 16/80,
n1 = 40, r = 2, met = "koopman"), NA)
expect_error(prec_riskratio(p1 = 36/40, p2 = 16/80,
n1 = 40, r = 2, met = "katz"), NA)
expect_error(prec_riskratio(p1 = 36/40, p2 = 16/80,
conf.width = .2, r = 2, met = "koopman"), NA)
expect_error(prec_riskratio(p1 = 36/40, p2 = 16/80,
conf.width = .2, r = 2, met = "katz"), NA)
expect_warning(prec_riskratio(p1 = 36/40, p2 = 16/80,
n1 = 40, r = 2))
expect_warning(prec_riskratio(p1 = 36/40, p2 = 16/80,
n1 = 40, r = 2, method = "foo"))
expect_error(prec_rateratio(rate1 = 1, rate2 = NULL))
expect_error(prec_rateratio(rate1 = NULL, rate2 = NULL))
expect_error(prec_rateratio(rate1 = 1, rate2 = 1))
expect_error(prec_rateratio(20, rate1 = 1, rate2 = 1), regexp = NA)
expect_error(prec_meandiff("foo"), "'delta'")
expect_error(prec_meandiff(2, "foo"), "'sd1'")
expect_error(prec_meandiff(2, 1, "foo"), "'sd2'")
expect_error(prec_meandiff(2, 1, r = "foo"), "'r'")
expect_error(prec_meandiff(2, 1, r = 2), "one of 'n', and 'conf.width'")
expect_error(prec_meandiff(2, 1, n = 50, variance = "equal"), NA)
expect_error(prec_meandiff(2, 1, n = 50, variance = "unequal"), NA)
expect_message(prec_meandiff(2, 1, n = 50))
expect_error(prec_meandiff(2, 1, conf.width = .5, variance = "equal"), NA)
expect_error(prec_meandiff(2, 1, conf.width = .5, variance = "unequal"), NA)
expect_message(prec_meandiff(2, 1, 2, conf.width = .5),
"Variance was changed")
expect_error(prec_meandiff(2, 1, 2, conf.width = .5, variance = "foo"),
"variance is not correctly specified")
expect_message(prec_meandiff(2, 1, 2, conf.width = .5, variance = "equal"),
"equal variance was chosen")
expect_error(prec_meandiff(2, .5, n1 = 50, variance = "equal"), NA)
expect_error(prec_meandiff(2, .5, conf.width = .1, variance = "equal"), NA)
expect_error(prec_meandiff(2, .5, n1 = 50, variance = "unequal"), NA)
expect_error(prec_meandiff(2, .5, conf.width = .1, variance = "unequal"), NA)
expect_error(prec_riskdiff())
expect_error(prec_riskdiff(n = 100))
expect_error(prec_riskdiff(n = 100, p1 = 2), "numeric in")
expect_error(prec_riskdiff(n = 100, p1 = .2))
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = 2), "numeric in")
expect_warning(prec_riskdiff(n = 100, p1 = .2, p2 = .4))
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "new"), NA)
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "mn"), NA)
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "ac"), NA)
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "wald"), NA)
expect_error(prec_riskdiff(conf.width = .1,
p1 = .2, p2 = .4, method = "new"), NA)
expect_error(prec_riskdiff(conf.width = .1,
p1 = .2, p2 = .4, method = "mn"), NA)
expect_error(prec_riskdiff(conf.width = .1,
p1 = .2, p2 = .4, method = "ac"), NA)
expect_error(prec_riskdiff(conf.width = .1,
p1 = .2, p2 = .4, method = "wald"), NA)
expect_warning(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "foo"), "Method 'foo' is not available")
expect_error(prec_or(), "exactly one")
expect_error(prec_or(n1 = 50), "p1")
expect_error(prec_or(n1 = 50, p1 = 2), "p1' must be numeric in")
expect_error(prec_or(n1 = 50, p1 = .5), "p2")
expect_error(prec_or(n1 = 50, p1 = .5, p2 = 2), "p2' must be numeric in")
expect_warning(prec_or(n1 = 50, p1 = .5, p2 = .8))
expect_error(prec_or(n1 = 50, p1 = .5, p2 = .8, method = "indip_smooth"), NA)
expect_error(prec_or(n1 = 50, p1 = .5, p2 = .8, method = "woolf"), NA)
expect_error(prec_or(n1 = 50, p1 = .5, p2 = .8, method = "gart"), NA)
expect_warning(prec_or(conf.width = .2, p1 = .5, p2 = .8))
expect_error(prec_or(conf.width = .2, p1 = .5, p2 = .8,
method = "indip_smooth"), NA)
expect_error(prec_or(conf.width = .2, p1 = .5, p2 = .8, method = "woolf"), NA)
expect_error(prec_or(conf.width = .2, p1 = .5, p2 = .8, method = "gart"), NA)
expect_warning(prec_or(conf.width = .2, p1 = .5, p2 = .8, method = "foo"), "Method 'foo' is not available")
})
### Mean difference
test_that("mean diff n = stata", {
# . ciwidth twomeans 0 5, probwidth(0.5) width(.5) sd(3)
# note: command arguments (control- and experimental-group mean estimates) do not
# affect computations and are used only for display
#
# Performing iteration ...
#
# Estimated sample sizes for a two-means-difference CI
# Student's t two-sided CI assuming sd1 = sd2 = sd
#
# Study parameters:
#
# level = 95.00
# Pr_width = 0.5000
# width = 0.5000
# m1 = 0.0000
# m2 = 5.0000
# diff = 5.0000
# sd = 3.0000
#
# Estimated sample sizes:
#
# N = 2,216
# N per group = 1,108
ex <- prec_meandiff(delta = 5, sd1 = 3, sd2 = 3, r = 1, conf.width = .5, conf.level = 0.95, variance = 'equal')
expect_equal(ex$n1 , 1108 , tolerance = 1, scale = 1)
ex <- prec_meandiff(delta = 5, sd1 = 3, sd2 = 3, r = 1, n = 1108, conf.level = 0.95, variance = 'equal')
expect_equal(ex$conf.width , .5 , tolerance = .001, scale = 1)
})
test_that("mean diff n = stata", {
# . ciwidth twomeans 0 5, width(.5) sd1(3) sd2(2) knownsds
# note: command arguments (control- and experimental-group mean estimates) do not
# affect computations and are used only for display
#
# Estimated sample sizes for a two-means-difference CI
# Normal two-sided CI
#
# Study parameters:
#
# level = 95.00
# width = 0.5000
# m1 = 0.0000
# m2 = 5.0000
# diff = 5.0000
# sd1 = 3.0000
# sd2 = 2.0000
#
# Estimated sample sizes:
#
# N = 1,600
# N per group = 800
ex <- prec_meandiff(delta = 5, sd1 = 3, sd2 = 2, r = 1, conf.width = 0.5, conf.level = 0.95, variance = 'equal')
expect_equal(ex$n1 , 800, tolerance = 3, scale = 1)
ex <- prec_meandiff(delta = 5, sd1 = 3, sd2 = 2, r = 1, n = 800, conf.level = 0.95, variance = 'equal')
expect_equal(ex$conf.width , .5, tolerance = .001, scale = 1)
})
test_that("mean diff conf int = stata", {
# . ciwidth twomeans 0 5, n(160) sd(2) probwidth(0.5)
# note: command arguments (control- and experimental-group mean estimates) do not
# affect computations and are used only for display
#
# Estimated width for a two-means-difference CI
# Student's t two-sided CI assuming sd1 = sd2 = sd
#
# Study parameters:
#
# level = 95.00
# N = 160
# N per group = 80
# Pr_width = 0.5000
# m1 = 0.0000
# m2 = 5.0000
# diff = 5.0000
# sd = 2.0000
#
# Estimated width:
#
# width = 1.2465
ex <- prec_meandiff(delta = 10, sd1 = 2, sd2 = 2, r = 1, n = 80, conf.level = 0.95, variance = 'equal')
expect_equal(ex$conf.width , 1.2465 , tolerance = .05, scale = 1)
ex <- prec_meandiff(delta = 10, sd1 = 2, sd2 = 2, r = 1, conf.width = 1.2465, conf.level = 0.95, variance = 'equal')
expect_equal(ex$n1 , 80 , tolerance = 2, scale = 1)
})
### Risk difference
test_that("risk diff conf int ac = stata", {
# . rdcii 45 15 105 135
#
# Confidence intervals for risk difference
#
# Risk for unexposed (p0): 0.100
# Risk for exposed (p1): 0.300
# Risk difference (p1 - p0): 0.200
#
# -----------------------------------------
# Method [95% Conf. Interval]
# -----------------------------------------
# Agresti-Caffo 0.110 0.285
# Newcombe Method 10 0.111 0.287
# Wallenstein 0.110 0.285
# Miettinen-Nurminen 0.112 0.289
# . disp r(lb_ac)
# .10964857
#
# . disp r(ub_ac)
# .28529424
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, n1 = 150, conf.level = 0.95, method = 'ac')
expect_equal(c(ex$lwr, ex$upr) , c(.10964857, .28529424), tolerance = .01, scale = 1)
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, conf.width = .28529424-.10964857, conf.level = 0.95, method = 'ac')
expect_equal(ex$n1, 150, tolerance = 1, scale = 1)
})
test_that("risk diff conf int newcombe = stata", {
# . rdcii 45 15 105 135
#
# Confidence intervals for risk difference
#
# Risk for unexposed (p0): 0.100
# Risk for exposed (p1): 0.300
# Risk difference (p1 - p0): 0.200
#
# -----------------------------------------
# Method [95% Conf. Interval]
# -----------------------------------------
# Agresti-Caffo 0.110 0.285
# Newcombe Method 10 0.111 0.287
# Wallenstein 0.110 0.285
# Miettinen-Nurminen 0.112 0.289
# . disp r(lb_ne)
# .11065088
#
# . disp r(ub_ne)
# .28658921
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, n1 = 150, conf.level = 0.95, method = 'newcombe')
expect_equal(c(ex$lwr, ex$upr) , c(.11065088, .28658921), tolerance = .001, scale = 1)
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, conf.width = .28658921-.11065088, conf.level = 0.95, method = 'newcombe')
expect_equal(ex$n1, 150, tolerance = 1, scale = 1)
})
test_that("risk diff conf int nm = stata", {
# . rdcii 45 15 105 135
#
# Confidence intervals for risk difference
#
# Risk for unexposed (p0): 0.100
# Risk for exposed (p1): 0.300
# Risk difference (p1 - p0): 0.200
#
# -----------------------------------------
# Method [95% Conf. Interval]
# -----------------------------------------
# Agresti-Caffo 0.110 0.285
# Newcombe Method 10 0.111 0.287
# Wallenstein 0.110 0.285
# Miettinen-Nurminen 0.112 0.289
# . disp r(lb_mn)
# .11200421
#
# . disp r(ub_mn)
# .28854354
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, n1 = 150, conf.level = 0.95, method = 'mn')
expect_equal(c(ex$lwr, ex$upr) , c(.11200421, .28854354), tolerance = .001, scale = 1)
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, conf.width = .28854354-.11200421, conf.level = 0.95, method = 'mn')
expect_equal(ex$n1, 150, tolerance = 1, scale = 1)
})
# "Wald not tested"
# prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, n1 = 150, conf.level = 0.95, method = 'wald')
### Odds ratio
test_that("odds ratio conf int woolf = stata", {
# Ref. Fagerland 2015
# Confidence interval
# Lower Upper Length
#Woolf logit 0.99 74 4.31
#Gart adjusted logit 0.98 38 3.65
#Independence-smoothed logit 0.99 60 4.11
# . csi 7 1 27 33 , or woolf
#
# | Exposed Unexposed | Total
# ---------+------------------------+------------
# Cases | 7 1 | 8
# Noncases | 27 33 | 60
# ---------+------------------------+------------
# Total | 34 34 | 68
# | |
# Risk | .2058824 .0294118 | .1176471
# | |
# | Point estimate | [95% Conf. Interval]
# |------------------------+------------------------
# Risk difference | .1764706 | .0291694 .3237718
# Risk ratio | 7 | .9096055 53.86951
# Attr. frac. ex. | .8571429 | -.0993777 .9814366
# Attr. frac. pop | .75 |
# Odds ratio | 8.555556 | .9904903 73.9003 (Woolf)
# +-------------------------------------------------
# chi2(1) = 5.10 Pr>chi2 = 0.0239
#
ex <- prec_or(p1 = 7/34, p2 = 1/34, n1 = 34, r = 1, conf.width = NULL, conf.level = 0.95, method = "woolf")
expect_equal(c(ex$lwr, ex$upr) , c(0.99, 74), tolerance = .1, scale = 1)
expect_equal(c(ex$lwr, ex$upr) , c(.9904903, 73.9003), tolerance = .0001, scale = 1)
ex <- prec_or(p1 = 7/34, p2 = 1/34, r = 1, conf.width = 73.9003-.9904903, conf.level = 0.95, method = "woolf")
expect_equal(ex$n1 , 34, tolerance = 1, scale = 1)
})
test_that("odds ratio conf int gart = stata", {
# Ref. Fagerland 2015
# Confidence interval
# Lower Upper Length
#Woolf logit 0.99 74 4.31
#Gart adjusted logit 0.98 38 3.65
#Independence-smoothed logit 0.99 60 4.11
ex <- prec_or(p1 = 7/34, p2 = 1/34, n1 = 34, r = 1, conf.width = NULL, conf.level = 0.95, method = "gart")
expect_equal(ex$lwr , 0.98, tolerance = .01, scale = 1)
expect_equal(ex$upr , 38, tolerance = .5, scale = 1)
ex <- prec_or(p1 = 7/34, p2 = 1/34, r = 1, conf.width = 38-.98, conf.level = 0.95, method = "gart")
expect_equal(ex$n1 , 34, tolerance = 1, scale = 1)
})
test_that("odds ratio conf int indip smooth = stata", {
# Ref. Fagerland 2015
# Confidence interval
# Lower Upper Length
#Woolf logit 0.99 74 4.31
#Gart adjusted logit 0.98 38 3.65
#Independence-smoothed logit 0.99 60 4.11
ex <- prec_or(p1 = 7/34, p2 = 1/34, n1 = 34, r = 1, conf.width = NULL, conf.level = 0.95, method = "indip_smooth")
expect_equal(ex$lwr , 0.99, tolerance = .01, scale = 1)
expect_equal(ex$upr , 60, tolerance = .5, scale = 1)
ex <- prec_or(p1 = 7/34, p2 = 1/34, r = 1, conf.width = 60-.99, conf.level = 0.95, method = "indip_smooth")
expect_equal(ex$n1 , 34, tolerance = 1, scale = 1)
})
test_that("Fagerland et al. (2015), Table 5", {
p1 <- 7/34
p2 <- 1/34
n1 <- 34
r <- 1
method <- "katz"
p <- prec_riskratio(p1 = p1, p2 = p2, n1 = n1, r = r, method = method)
expect_equal(class(p), "presize")
expect_equal(p$p1, p1)
expect_equal(p$p2, p2)
expect_equal(p$n1, n1)
expect_equal(p$conf.level, 0.95)
expect_equivalent(p$rr, 7)
expect_equivalent(round(p$lwr, 2), .91)
expect_equivalent(round(p$upr, 0), 54)
p1 <- 7/34
p2 <- 1/34
n1 <- 34
r <- 1
method <- "koopman"
p <- prec_riskratio(p1 = p1, p2 = p2, n1 = n1, r = r, method = method)
expect_equal(class(p), "presize")
expect_equal(p$p1, p1)
expect_equal(p$p2, p2)
expect_equal(p$n1, n1)
expect_equal(p$conf.level, 0.95)
expect_equivalent(p$rr, 7)
expect_equivalent(round(p$lwr, 1), 1.2)
expect_equivalent(round(p$upr, 0), 43)
})
context("rateratio")
test_that("inverse", {
x <- prec_rateratio(20, rate1 = 1, rate2 = 1)
y <- prec_rateratio(rate1 = 1, rate2 = 1, prec.level = 3.454197)
expect_equal(x[1:11], y[1:11], tolerance = .05, scale = 1)
})
test_that("rateratio = stata outputs", {
# . iri 30 60 54308 51477
#
# | Exposed Unexposed | Total
# -----------------+------------------------+------------
# Cases | 30 60 | 90
# Person-time | 54308 51477 | 105785
# ------------+------------------------+------------
# | |
# Incidence rate | .0005524 .0011656 | .0008508
# | |
# | Point estimate | [95% Conf. Interval]
# |------------------------+------------------------
# Inc. rate diff. | -.0006132 | -.0009682 -.0002581
# Inc. rate ratio | .4739357 | .2951285 .7464182 (exact)
# Prev. frac. ex. | .5260643 | .2535818 .7048715 (exact)
# Prev. frac. pop | .2700714 |
# +-------------------------------------------------
# (midp) Pr(k<=30) = 0.0003 (exact)
# (midp) 2*Pr(k<=30) = 0.0006 (exact)
ex <- prec_rateratio(n1 = 54308, rate1 = 30/54308, rate2 = 60/51477, r = 51477/54308)
expect_equal(c(ex$lwr, ex$upr), c(.2951285, .7464182) , tolerance = .05, scale = 1)
ex <- prec_rateratio(rate1 = 30/54308, rate2 = 60/51477, prec.level = 2.4, r = 51477/54308)
expect_equal(ex$n1, 54308, tolerance = 150, scale = 1) # 150 patient-year difference is less than 0.3% difference
})
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context("Differences")
test_that("errors", {
expect_error(prec_riskratio(.1, 1.1, r = 1, conf.width = .1))
expect_error(prec_riskratio(1.1, .1, r = 1, conf.width = .1))
expect_error(prec_riskratio(1.1, r = 1, conf.width = .1))
expect_error(prec_riskratio(1.1, r = 1))
expect_error(prec_riskratio(p1 = 36/40, p2 = 16/80,
n1 = 40, r = 2, met = "koopman"), NA)
expect_error(prec_riskratio(p1 = 36/40, p2 = 16/80,
n1 = 40, r = 2, met = "katz"), NA)
expect_error(prec_riskratio(p1 = 36/40, p2 = 16/80,
conf.width = .2, r = 2, met = "koopman"), NA)
expect_error(prec_riskratio(p1 = 36/40, p2 = 16/80,
conf.width = .2, r = 2, met = "katz"), NA)
expect_warning(prec_riskratio(p1 = 36/40, p2 = 16/80,
n1 = 40, r = 2))
expect_warning(prec_riskratio(p1 = 36/40, p2 = 16/80,
n1 = 40, r = 2, method = "foo"))
expect_error(prec_rateratio(rate1 = 1, rate2 = NULL))
expect_error(prec_rateratio(rate1 = NULL, rate2 = NULL))
expect_error(prec_rateratio(rate1 = 1, rate2 = 1))
expect_error(prec_rateratio(20, rate1 = 1, rate2 = 1), regexp = NA)
expect_error(prec_meandiff("foo"), "'delta'")
expect_error(prec_meandiff(2, "foo"), "'sd1'")
expect_error(prec_meandiff(2, 1, "foo"), "'sd2'")
expect_error(prec_meandiff(2, 1, r = "foo"), "'r'")
expect_error(prec_meandiff(2, 1, r = 2), "one of 'n', and 'conf.width'")
expect_error(prec_meandiff(2, 1, n = 50, variance = "equal"), NA)
expect_error(prec_meandiff(2, 1, n = 50, variance = "unequal"), NA)
expect_message(prec_meandiff(2, 1, n = 50))
expect_error(prec_meandiff(2, 1, conf.width = .5, variance = "equal"), NA)
expect_error(prec_meandiff(2, 1, conf.width = .5, variance = "unequal"), NA)
expect_message(prec_meandiff(2, 1, 2, conf.width = .5),
"Variance was changed")
expect_error(prec_meandiff(2, 1, 2, conf.width = .5, variance = "foo"),
"variance is not correctly specified")
expect_message(prec_meandiff(2, 1, 2, conf.width = .5, variance = "equal"),
"equal variance was chosen")
expect_error(prec_meandiff(2, .5, n1 = 50, variance = "equal"), NA)
expect_error(prec_meandiff(2, .5, conf.width = .1, variance = "equal"), NA)
expect_error(prec_meandiff(2, .5, n1 = 50, variance = "unequal"), NA)
expect_error(prec_meandiff(2, .5, conf.width = .1, variance = "unequal"), NA)
expect_error(prec_riskdiff())
expect_error(prec_riskdiff(n = 100))
expect_error(prec_riskdiff(n = 100, p1 = 2), "numeric in")
expect_error(prec_riskdiff(n = 100, p1 = .2))
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = 2), "numeric in")
expect_warning(prec_riskdiff(n = 100, p1 = .2, p2 = .4))
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "new"), NA)
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "mn"), NA)
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "ac"), NA)
expect_error(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "wald"), NA)
expect_error(prec_riskdiff(conf.width = .1,
p1 = .2, p2 = .4, method = "new"), NA)
expect_error(prec_riskdiff(conf.width = .1,
p1 = .2, p2 = .4, method = "mn"), NA)
expect_error(prec_riskdiff(conf.width = .1,
p1 = .2, p2 = .4, method = "ac"), NA)
expect_error(prec_riskdiff(conf.width = .1,
p1 = .2, p2 = .4, method = "wald"), NA)
expect_warning(prec_riskdiff(n = 100, p1 = .2, p2 = .4, method = "foo"), "Method 'foo' is not available")
expect_error(prec_or(), "exactly one")
expect_error(prec_or(n1 = 50), "p1")
expect_error(prec_or(n1 = 50, p1 = 2), "p1' must be numeric in")
expect_error(prec_or(n1 = 50, p1 = .5), "p2")
expect_error(prec_or(n1 = 50, p1 = .5, p2 = 2), "p2' must be numeric in")
expect_warning(prec_or(n1 = 50, p1 = .5, p2 = .8))
expect_error(prec_or(n1 = 50, p1 = .5, p2 = .8, method = "indip_smooth"), NA)
expect_error(prec_or(n1 = 50, p1 = .5, p2 = .8, method = "woolf"), NA)
expect_error(prec_or(n1 = 50, p1 = .5, p2 = .8, method = "gart"), NA)
expect_warning(prec_or(conf.width = .2, p1 = .5, p2 = .8))
expect_error(prec_or(conf.width = .2, p1 = .5, p2 = .8,
method = "indip_smooth"), NA)
expect_error(prec_or(conf.width = .2, p1 = .5, p2 = .8, method = "woolf"), NA)
expect_error(prec_or(conf.width = .2, p1 = .5, p2 = .8, method = "gart"), NA)
expect_warning(prec_or(conf.width = .2, p1 = .5, p2 = .8, method = "foo"), "Method 'foo' is not available")
})
### Mean difference
test_that("mean diff n = stata", {
# . ciwidth twomeans 0 5, probwidth(0.5) width(.5) sd(3)
# note: command arguments (control- and experimental-group mean estimates) do not
# affect computations and are used only for display
#
# Performing iteration ...
#
# Estimated sample sizes for a two-means-difference CI
# Student's t two-sided CI assuming sd1 = sd2 = sd
#
# Study parameters:
#
# level = 95.00
# Pr_width = 0.5000
# width = 0.5000
# m1 = 0.0000
# m2 = 5.0000
# diff = 5.0000
# sd = 3.0000
#
# Estimated sample sizes:
#
# N = 2,216
# N per group = 1,108
ex <- prec_meandiff(delta = 5, sd1 = 3, sd2 = 3, r = 1, conf.width = .5, conf.level = 0.95, variance = 'equal')
expect_equal(ex$n1 , 1108 , tolerance = 1, scale = 1)
ex <- prec_meandiff(delta = 5, sd1 = 3, sd2 = 3, r = 1, n = 1108, conf.level = 0.95, variance = 'equal')
expect_equal(ex$conf.width , .5 , tolerance = .001, scale = 1)
})
test_that("mean diff n = stata", {
# . ciwidth twomeans 0 5, width(.5) sd1(3) sd2(2) knownsds
# note: command arguments (control- and experimental-group mean estimates) do not
# affect computations and are used only for display
#
# Estimated sample sizes for a two-means-difference CI
# Normal two-sided CI
#
# Study parameters:
#
# level = 95.00
# width = 0.5000
# m1 = 0.0000
# m2 = 5.0000
# diff = 5.0000
# sd1 = 3.0000
# sd2 = 2.0000
#
# Estimated sample sizes:
#
# N = 1,600
# N per group = 800
ex <- prec_meandiff(delta = 5, sd1 = 3, sd2 = 2, r = 1, conf.width = 0.5, conf.level = 0.95, variance = 'equal')
expect_equal(ex$n1 , 800, tolerance = 3, scale = 1)
ex <- prec_meandiff(delta = 5, sd1 = 3, sd2 = 2, r = 1, n = 800, conf.level = 0.95, variance = 'equal')
expect_equal(ex$conf.width , .5, tolerance = .001, scale = 1)
})
test_that("mean diff conf int = stata", {
# . ciwidth twomeans 0 5, n(160) sd(2) probwidth(0.5)
# note: command arguments (control- and experimental-group mean estimates) do not
# affect computations and are used only for display
#
# Estimated width for a two-means-difference CI
# Student's t two-sided CI assuming sd1 = sd2 = sd
#
# Study parameters:
#
# level = 95.00
# N = 160
# N per group = 80
# Pr_width = 0.5000
# m1 = 0.0000
# m2 = 5.0000
# diff = 5.0000
# sd = 2.0000
#
# Estimated width:
#
# width = 1.2465
ex <- prec_meandiff(delta = 10, sd1 = 2, sd2 = 2, r = 1, n = 80, conf.level = 0.95, variance = 'equal')
expect_equal(ex$conf.width , 1.2465 , tolerance = .05, scale = 1)
ex <- prec_meandiff(delta = 10, sd1 = 2, sd2 = 2, r = 1, conf.width = 1.2465, conf.level = 0.95, variance = 'equal')
expect_equal(ex$n1 , 80 , tolerance = 2, scale = 1)
})
### Risk difference
test_that("risk diff conf int ac = stata", {
# . rdcii 45 15 105 135
#
# Confidence intervals for risk difference
#
# Risk for unexposed (p0): 0.100
# Risk for exposed (p1): 0.300
# Risk difference (p1 - p0): 0.200
#
# -----------------------------------------
# Method [95% Conf. Interval]
# -----------------------------------------
# Agresti-Caffo 0.110 0.285
# Newcombe Method 10 0.111 0.287
# Wallenstein 0.110 0.285
# Miettinen-Nurminen 0.112 0.289
# . disp r(lb_ac)
# .10964857
#
# . disp r(ub_ac)
# .28529424
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, n1 = 150, conf.level = 0.95, method = 'ac')
expect_equal(c(ex$lwr, ex$upr) , c(.10964857, .28529424), tolerance = .01, scale = 1)
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, conf.width = .28529424-.10964857, conf.level = 0.95, method = 'ac')
expect_equal(ex$n1, 150, tolerance = 1, scale = 1)
})
test_that("risk diff conf int newcombe = stata", {
# . rdcii 45 15 105 135
#
# Confidence intervals for risk difference
#
# Risk for unexposed (p0): 0.100
# Risk for exposed (p1): 0.300
# Risk difference (p1 - p0): 0.200
#
# -----------------------------------------
# Method [95% Conf. Interval]
# -----------------------------------------
# Agresti-Caffo 0.110 0.285
# Newcombe Method 10 0.111 0.287
# Wallenstein 0.110 0.285
# Miettinen-Nurminen 0.112 0.289
# . disp r(lb_ne)
# .11065088
#
# . disp r(ub_ne)
# .28658921
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, n1 = 150, conf.level = 0.95, method = 'newcombe')
expect_equal(c(ex$lwr, ex$upr) , c(.11065088, .28658921), tolerance = .001, scale = 1)
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, conf.width = .28658921-.11065088, conf.level = 0.95, method = 'newcombe')
expect_equal(ex$n1, 150, tolerance = 1, scale = 1)
})
test_that("risk diff conf int nm = stata", {
# . rdcii 45 15 105 135
#
# Confidence intervals for risk difference
#
# Risk for unexposed (p0): 0.100
# Risk for exposed (p1): 0.300
# Risk difference (p1 - p0): 0.200
#
# -----------------------------------------
# Method [95% Conf. Interval]
# -----------------------------------------
# Agresti-Caffo 0.110 0.285
# Newcombe Method 10 0.111 0.287
# Wallenstein 0.110 0.285
# Miettinen-Nurminen 0.112 0.289
# . disp r(lb_mn)
# .11200421
#
# . disp r(ub_mn)
# .28854354
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, n1 = 150, conf.level = 0.95, method = 'mn')
expect_equal(c(ex$lwr, ex$upr) , c(.11200421, .28854354), tolerance = .001, scale = 1)
ex <- prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, conf.width = .28854354-.11200421, conf.level = 0.95, method = 'mn')
expect_equal(ex$n1, 150, tolerance = 1, scale = 1)
})
# "Wald not tested"
# prec_riskdiff(p1 = 0.3, p2 = 0.1, r = 1, n1 = 150, conf.level = 0.95, method = 'wald')
### Odds ratio
test_that("odds ratio conf int woolf = stata", {
# Ref. Fagerland 2015
# Confidence interval
# Lower Upper Length
#Woolf logit 0.99 74 4.31
#Gart adjusted logit 0.98 38 3.65
#Independence-smoothed logit 0.99 60 4.11
# . csi 7 1 27 33 , or woolf
#
# | Exposed Unexposed | Total
# ---------+------------------------+------------
# Cases | 7 1 | 8
# Noncases | 27 33 | 60
# ---------+------------------------+------------
# Total | 34 34 | 68
# | |
# Risk | .2058824 .0294118 | .1176471
# | |
# | Point estimate | [95% Conf. Interval]
# |------------------------+------------------------
# Risk difference | .1764706 | .0291694 .3237718
# Risk ratio | 7 | .9096055 53.86951
# Attr. frac. ex. | .8571429 | -.0993777 .9814366
# Attr. frac. pop | .75 |
# Odds ratio | 8.555556 | .9904903 73.9003 (Woolf)
# +-------------------------------------------------
# chi2(1) = 5.10 Pr>chi2 = 0.0239
#
ex <- prec_or(p1 = 7/34, p2 = 1/34, n1 = 34, r = 1, conf.width = NULL, conf.level = 0.95, method = "woolf")
expect_equal(c(ex$lwr, ex$upr) , c(0.99, 74), tolerance = .1, scale = 1)
expect_equal(c(ex$lwr, ex$upr) , c(.9904903, 73.9003), tolerance = .0001, scale = 1)
ex <- prec_or(p1 = 7/34, p2 = 1/34, r = 1, conf.width = 73.9003-.9904903, conf.level = 0.95, method = "woolf")
expect_equal(ex$n1 , 34, tolerance = 1, scale = 1)
})
test_that("odds ratio conf int gart = stata", {
# Ref. Fagerland 2015
# Confidence interval
# Lower Upper Length
#Woolf logit 0.99 74 4.31
#Gart adjusted logit 0.98 38 3.65
#Independence-smoothed logit 0.99 60 4.11
ex <- prec_or(p1 = 7/34, p2 = 1/34, n1 = 34, r = 1, conf.width = NULL, conf.level = 0.95, method = "gart")
expect_equal(ex$lwr , 0.98, tolerance = .01, scale = 1)
expect_equal(ex$upr , 38, tolerance = .5, scale = 1)
ex <- prec_or(p1 = 7/34, p2 = 1/34, r = 1, conf.width = 38-.98, conf.level = 0.95, method = "gart")
expect_equal(ex$n1 , 34, tolerance = 1, scale = 1)
})
test_that("odds ratio conf int indip smooth = stata", {
# Ref. Fagerland 2015
# Confidence interval
# Lower Upper Length
#Woolf logit 0.99 74 4.31
#Gart adjusted logit 0.98 38 3.65
#Independence-smoothed logit 0.99 60 4.11
ex <- prec_or(p1 = 7/34, p2 = 1/34, n1 = 34, r = 1, conf.width = NULL, conf.level = 0.95, method = "indip_smooth")
expect_equal(ex$lwr , 0.99, tolerance = .01, scale = 1)
expect_equal(ex$upr , 60, tolerance = .5, scale = 1)
ex <- prec_or(p1 = 7/34, p2 = 1/34, r = 1, conf.width = 60-.99, conf.level = 0.95, method = "indip_smooth")
expect_equal(ex$n1 , 34, tolerance = 1, scale = 1)
})
test_that("Fagerland et al. (2015), Table 5", {
p1 <- 7/34
p2 <- 1/34
n1 <- 34
r <- 1
method <- "katz"
p <- prec_riskratio(p1 = p1, p2 = p2, n1 = n1, r = r, method = method)
expect_equal(class(p), "presize")
expect_equal(p$p1, p1)
expect_equal(p$p2, p2)
expect_equal(p$n1, n1)
expect_equal(p$conf.level, 0.95)
expect_equivalent(p$rr, 7)
expect_equivalent(round(p$lwr, 2), .91)
expect_equivalent(round(p$upr, 0), 54)
p1 <- 7/34
p2 <- 1/34
n1 <- 34
r <- 1
method <- "koopman"
p <- prec_riskratio(p1 = p1, p2 = p2, n1 = n1, r = r, method = method)
expect_equal(class(p), "presize")
expect_equal(p$p1, p1)
expect_equal(p$p2, p2)
expect_equal(p$n1, n1)
expect_equal(p$conf.level, 0.95)
expect_equivalent(p$rr, 7)
expect_equivalent(round(p$lwr, 1), 1.2)
expect_equivalent(round(p$upr, 0), 43)
})
context("rateratio")
test_that("inverse", {
x <- prec_rateratio(20, rate1 = 1, rate2 = 1)
y <- prec_rateratio(rate1 = 1, rate2 = 1, prec.level = 3.454197)
expect_equal(x[1:11], y[1:11], tolerance = .05, scale = 1)
})
test_that("rateratio = stata outputs", {
# . iri 30 60 54308 51477
#
# | Exposed Unexposed | Total
# -----------------+------------------------+------------
# Cases | 30 60 | 90
# Person-time | 54308 51477 | 105785
# ------------+------------------------+------------
# | |
# Incidence rate | .0005524 .0011656 | .0008508
# | |
# | Point estimate | [95% Conf. Interval]
# |------------------------+------------------------
# Inc. rate diff. | -.0006132 | -.0009682 -.0002581
# Inc. rate ratio | .4739357 | .2951285 .7464182 (exact)
# Prev. frac. ex. | .5260643 | .2535818 .7048715 (exact)
# Prev. frac. pop | .2700714 |
# +-------------------------------------------------
# (midp) Pr(k<=30) = 0.0003 (exact)
# (midp) 2*Pr(k<=30) = 0.0006 (exact)
ex <- prec_rateratio(n1 = 54308, rate1 = 30/54308, rate2 = 60/51477, r = 51477/54308)
expect_equal(c(ex$lwr, ex$upr), c(.2951285, .7464182) , tolerance = .05, scale = 1)
ex <- prec_rateratio(rate1 = 30/54308, rate2 = 60/51477, prec.level = 2.4, r = 51477/54308)
expect_equal(ex$n1, 54308, tolerance = 150, scale = 1) # 150 patient-year difference is less than 0.3% difference
})
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