RRstr: Gart-Nam method, CI for common RR over strata or clusters.

In ABS-dev/PF: Prevented fraction

Gart-Nam method, CI for common RR over strata or clusters.

Description

Estimates confidence intervals for the risk ratio or prevented fraction from clustered or stratified data.

Usage

RRstr(
  formula = NULL,
  data = NULL,
  compare = c("vac", "con"),
  Y,
  alpha = 0.05,
  pf = TRUE,
  trace.it = FALSE,
  iter.max = 24,
  converge = 1e-06,
  rnd = 3,
  multiplier = 0.7,
  divider = 1.1
)

Arguments

formula	Formula of the form cbind(y, n) ~ x + cluster(w), where y is the number positive, n is the group size, x is a factor with two levels of treatment, and w is a factor indicating the clusters.
data	data.frame containing variables of formula
compare	Text vector stating the factor levels: compare[1] is the control or reference group to which compare[2] is compared
Y	Matrix of data. Each row is a stratum or cluster. The columns are y2, n2, y1, n1. If data entered by formula and dataframe, Y is generated automatically.
alpha	Size of the homogeneity test and 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.
multiplier	internal control parameter for algorithm
divider	internal control parameter for algorithm

Details

Uses the DUD algorithm to estimate confidence intervals by the method of Gart.

Value

A rrstr object with the following fields:

estimate	matrix of point and interval estimates - starting value, MLE, and skewness corrected
hom	list of homogeneity statistic, p-value, and degrees of freedom, or error message if appropriate.
estimator	either "PF" or "RR"
y	data.frame of restructured input
compare	groups compared
rnd	how many digits to round the display
alpha	size of test; complement of confidence level

Note

Vignette Examples for Stratified Designs forthcoming with more examples.

Call to this function may be one of two formats: (1) specify data and formula or (2) as a matrix Y

RRstr(formula, data, compare = c('b','a'), pf = TRUE, alpha = 0.05, trace.it = FALSE,
iter.max = 24, converge = 1e-6, rnd = 3, multiplier = 0.7, divider = 1.1)

RRstr(Y, compare = c('b','a'), pf = TRUE, alpha = 0.05, trace.it = FALSE, iter.max = 24,
converge = 1e-6, rnd = 3, multiplier = 0.7, divider = 1.1)

Author(s)

PF-package

References

Gart JJ, 1985. Approximate tests and interval estimation of the common relative risk in the combination of 2 x 2 tables. Biometrika 72:673-677.
Gart JJ, Nam J, 1988. Approximate interval estimation of the ratio of binomial parameters: a review and corrections for skewness. Biometrics 44:323-338.
Ralston ML, Jennrich RI, 1978. DUD, A Derivative-Free Algorithm for Nonlinear Least Squares. Technometrics 20:7-14.

See Also

rrstr

Examples

## Table 1 from Gart (1985)
##  as data frame
## "b" is control group
RRstr(cbind(y, n) ~ tx + cluster(clus),
      Table6,
      compare = c('a', 'b'), pf = FALSE)

# Test of homogeneity across clusters

# stat     0.954
# df       3
# p        0.812

#  RR estimates

# 		        RR   LL   UL
# starting   2.66 1.37 5.18
# mle        2.65 1.39 5.03
# skew corr  2.65 1.31 5.08

## or as matrix
RRstr(Y = table6, pf = FALSE)

tst <- data.frame(y = c(0, 2, 0, 4, 0, 3, 0, 7),
n = rep(10, 8),
tx = rep(c('a', 'b'), 4),
clus = rep(paste('Row', 1:4, sep = ''), each = 2))

ABS-dev/PF documentation built on April 26, 2024, 3:29 p.m.