Hmisc (version 5.2-1)

mhgr: Miscellaneous Functions for Epidemiology

Description

The mhgr function computes the Cochran-Mantel-Haenszel stratified risk ratio and its confidence limits using the Greenland-Robins variance estimator.

The lrcum function takes the results of a series of 2x2 tables representing the relationship between test positivity and diagnosis and computes positive and negative likelihood ratios (with all their deficiencies) and the variance of their logarithms. Cumulative likelihood ratios and their confidence intervals (assuming independence of tests) are computed, assuming a string of all positive tests or a string of all negative tests. The method of Simel et al as described in Altman et al is used.

Usage

mhgr(y, group, strata, conf.int = 0.95)
# S3 method for mhgr
print(x, ...)
lrcum(a, b, c, d, conf.int = 0.95)
# S3 method for lrcum
print(x, dec=3, ...)

Value

a list of class "mhgr" or of class "lrcum".

Arguments

y

a binary response variable

group

a variable with two unique values specifying comparison groups

strata

the stratification variable

conf.int

confidence level

x

an object created by mhgr or lrcum

a

frequency of true positive tests

b

frequency of false positive tests

c

frequency of false negative tests

d

frequency of true negative tests

dec

number of places to the right of the decimal to print for lrcum

...

addtitional arguments to be passed to other print functions

Author

Frank E Harrell Jr

Details

Uses equations 4 and 13 from Greenland and Robins.

References

Greenland S, Robins JM (1985): Estimation of a common effect parameter from sparse follow-up data. Biometrics 41:55-68.

Altman DG, Machin D, Bryant TN, Gardner MJ, Eds. (2000): Statistics with Confidence, 2nd Ed. Bristol: BMJ Books, 105-110.

Simel DL, Samsa GP, Matchar DB (1991): Likelihood ratios with confidence: sample size estimation for diagnostic test studies. J Clin Epi 44:763-770.

See Also

logrank

Examples

# Greate Migraine dataset used in Example 28.6 in the SAS PROC FREQ guide
d <- expand.grid(response=c('Better','Same'),
                 treatment=c('Active','Placebo'),
                 sex=c('female','male'))
d$count <- c(16, 11, 5, 20, 12, 16, 7, 19)
d
# Expand data frame to represent raw data
r <- rep(1:8, d$count)
d <- d[r,]
with(d, mhgr(response=='Better', treatment, sex))

# Discrete survival time example, to get Cox-Mantel relative risk and CL
# From Stokes ME, Davis CS, Koch GG, Categorical Data Analysis Using the
# SAS System, 2nd Edition, Sectino 17.3, p. 596-599
#
# Input data in Table 17.5
d <- expand.grid(treatment=c('A','P'), center=1:3)
d$healed2w    <- c(15,15,17,12, 7, 3)
d$healed4w    <- c(17,17,17,13,17,17)
d$notHealed4w <- c( 2, 7,10,15,16,18)
d
# Reformat to the way most people would collect raw data
d1 <- d[rep(1:6, d$healed2w),]
d1$time <- '2'
d1$y <- 1
d2 <- d[rep(1:6, d$healed4w),]
d2$time <- '4'
d2$y <- 1
d3 <- d[rep(1:6, d$notHealed4w),]
d3$time <- '4'
d3$y <- 0
d <- rbind(d1, d2, d3)
d$healed2w <- d$healed4w <- d$notHealed4w <- NULL
d
# Finally, duplicate appropriate observations to create 2 and 4-week
# risk sets.  Healed and not healed at 4w need to be in the 2-week
# risk set as not healed
d2w      <- subset(d, time=='4')
d2w$time <- '2'
d2w$y    <- 0
d24      <- rbind(d, d2w)
with(d24, table(y, treatment, time, center))
# Matches Table 17.6

with(d24, mhgr(y, treatment, interaction(center, time, sep=';')))

# Get cumulative likelihood ratios and their 0.95 confidence intervals
# based on the following two tables
#
#          Disease       Disease
#          +     -       +     -
# Test +   39    3       20    5
# Test -   21   17       22   15

lrcum(c(39,20), c(3,5), c(21,22), c(17,15))