RRsc: RR score based asymptotic CI.

In ABS-dev/PF: Prevented fraction

RR score based asymptotic CI.

Description

Estimates confidence intervals for the risk ratio or prevented fraction based on the score statistic.

Usage

RRsc(
  y = NULL,
  data = NULL,
  formula = NULL,
  compare = c("vac", "con"),
  alpha = 0.05,
  pf = TRUE,
  trace.it = FALSE,
  iter.max = 18,
  converge = 1e-06,
  rnd = 3
)

Arguments

y	Data vector c(y1, n1, y2, n2) where y are the positives, n are the total, and group 1 is compared to group 2 (control or reference group).
data	data.frame containing variables of formula.
formula	Formula of the form cbind(y, n) ~ x, where y is the number positive, n is the group size, x is a factor with two levels of treatment.
compare	Text vector stating the factor levels: compare[1] is the vaccinate group to which compare[2] (control or reference) is compared.
alpha	Complement of the confidence level.
pf	Estimate RR or its complement PF?
trace.it	Verbose tracking of the iterations?
iter.max	Maximum number of iterations
converge	Convergence criterion
rnd	Number of digits for rounding. Affects display only, not estimates.

Details

Estimates are returned for three estimators based on the score statistic. The score method was introduced by Koopman (1984). Gart and Nam's modification (1988) includes a skewness correction. The method of Miettinen and Nurminen (1985) is a version made slightly more conservative than Koopman's by including a factor of (N - 1) / N. The starting estimate for the DUD algorithm is obtained by the modified Katz method (log method with 0.5 added to each cell). Both forms of the Katz estimate may be retrieved from the returned object using RRsc()$estimate.

The data may also be a matrix. In that case Y would be entered as

matrix(c(y1, n1-y1, y2, n2-y2), 2, 2, byrow = TRUE).

Value

A rrsc object with the following fields.

• estimate: matrix of point and interval estimates - see details

• estimator: either "PF" or "RR"

• y: data.frame with "y1", "n1", "y2", "n2" values.

• rnd: how many digits to round the display

• alpha: complement of confidence level

Author(s)

PF-package

References

Gart JJ, Nam J, 1988. Approximate interval estimation of the ratio of binomial parameters: a review and corrections for skewness. Biometrics 44:323-338.

Koopman PAR, 1984. Confidence intervals for the ratio of two binomial proportions. Biometrics 40:513-517.

Miettinen O, Nurminen M, 1985. Comparative analysis of two rates. Statistics in Medicine 4:213-226.

Ralston ML, Jennrich RI, 1978. DUD, A Derivative-Free Algorithm for Nonlinear Least Squares. Technometrics 20:7-14.

See Also

rrsc

Examples

# All examples represent the same observation, with data entry by using
# multiple notation options.

y_vector <- c(4, 24, 12, 28)
RRsc(y_vector)

# PF
# 95% interval estimates

# 				PF     LL    UL
# MN method    0.611 0.0251 0.857
# score method 0.611 0.0328 0.855
# skew corr    0.611 0.0380 0.876

y_matrix <- matrix(c(4, 20, 12, 16), 2, 2, byrow = TRUE)
#       [, 1] [, 2]
# [1, ]    4   20
# [2, ]   12   16

RRsc(y_matrix)

# PF
# 95% interval estimates

# 				PF     LL    UL
# MN method    0.611 0.0251 0.857
# score method 0.611 0.0328 0.855
# skew corr    0.611 0.0380 0.876
require(dplyr)
data1 <- data.frame(group = rep(c("treated", "control"), each = 2),
  y = c(1, 3, 7, 5),
  n = c(12, 12, 14, 14),
  cage = rep(paste("cage", 1:2), 2))

data2 <- data1 |>
  group_by(group) |>
  summarize(sum_y = sum(y),
    sum_n = sum(n))
RRsc(data = data2, formula = cbind(sum_y, sum_n) ~ group,
  compare = c("treated", "control"))

# PF
# 95% interval estimates

# 				PF     LL    UL
# MN method    0.611 0.0251 0.857
# score method 0.611 0.0328 0.855
# skew corr    0.611 0.0380 0.876

ABS-dev/PF documentation built on Sept. 19, 2024, 10:31 a.m.