Epiliptic Seizures

Description

The seizure data frame has 59 rows and 7 columns. The dataset has the number of epiliptic seizures in each of four two-week intervals, and in a baseline eight-week inverval, for treatment and control groups with a total of 59 individuals.

Usage

seizure

Format

This data frame contains the following columns:

y1

the number of epiliptic seizures in the 1st 2-week interval

y2

the number of epiliptic seizures in the 2nd 2-week interval

y3

the number of epiliptic seizures in the 3rd 2-week interval

y4

the number of epiliptic seizures in the 4th 2-week interval

trt

an indicator of treatment

base

the number of epilitic seizures in a baseline 8-week interval

age

a numeric vector of subject age

Source

Thall, P.F. and Vail S.C. (1990) Some covariance models for longitudinal count data with overdispersion. Biometrics 46: 657–671.

References

Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1994) Analysis of Longitudinal Data. Clarendon Press.

Examples

data(seizure)
## Diggle, Liang, and Zeger (1994) pp166-168, compare Table 8.10
seiz.l <- reshape(seizure,
                  varying=list(c("base","y1", "y2", "y3", "y4")),
                  v.names="y", times=0:4, direction="long")
seiz.l <- seiz.l[order(seiz.l$id, seiz.l$time),]
seiz.l$t <- ifelse(seiz.l$time == 0, 8, 2)
seiz.l$x <- ifelse(seiz.l$time == 0, 0, 1)
m1 <- geese(y ~ offset(log(t)) + x + trt + x:trt, id = id,
            data=seiz.l, corstr="exch", family=poisson)
summary(m1)
m2 <- geese(y ~ offset(log(t)) + x + trt + x:trt, id = id,
            data = seiz.l, subset = id!=49,
            corstr = "exch", family=poisson)
summary(m2)

## Thall and Vail (1990)
seiz.l <- reshape(seizure, varying=list(c("y1","y2","y3","y4")),
                  v.names="y", direction="long")
seiz.l <- seiz.l[order(seiz.l$id, seiz.l$time),]
seiz.l$lbase <- log(seiz.l$base / 4)
seiz.l$lage <- log(seiz.l$age)
seiz.l$v4 <- ifelse(seiz.l$time == 4, 1, 0)
m3 <- geese(y ~ lbase + trt + lbase:trt + lage + v4, 
            sformula = ~ as.factor(time) - 1, id = id,
            data = seiz.l, corstr = "exchangeable", family=poisson)
## compare to Model 13 in Table 4, noticeable difference
summary(m3)

## set up a design matrix for the correlation
z <- model.matrix(~ age, data = seizure)  # data is not seiz.l
## just to illustrate the scale link and correlation link
m4 <- geese(y ~ lbase + trt + lbase:trt + lage + v4,
            sformula = ~ as.factor(time)-1, id = id,
            data = seiz.l, corstr = "ar1", family = poisson,
            zcor = z, cor.link = "fisherz", sca.link = "log")
summary(m4)