# Epilepsy Attacks Data Set

### Description

Data from a clinical trial of 59 patients with epilepsy (Breslow, 1996) in order to illustrate diagnostic techniques in Poisson regression.

### Usage

1 |

### Format

A data frame with 59 observations on the following 11 variables.

`ID`

Patient identification number

`Y1`

Number of epilepsy attacks patients have during the first follow-up period

`Y2`

Number of epilepsy attacks patients have during the second follow-up period

`Y3`

Number of epilepsy attacks patients have during the third follow-up period

`Y4`

Number of epilepsy attacks patients have during the forth follow-up period

`Base`

Number of epileptic attacks recorded during 8 week period prior to randomization

`Age`

Age of the patients

`Trt`

a factor with levels

`placebo`

`progabide`

indicating whether the anti-epilepsy drug Progabide has been applied or not`Ysum`

Total number of epilepsy attacks patients have during the four follow-up periods

`Age10`

Age of the patients devided by 10

`Base4`

Variable

`Base`

devided by 4

### Details

Thall and Vail reported data from a clinical trial of 59 patients with epilepsy, 31 of whom were randomized to receive the anti-epilepsy drug Progabide and 28 of whom received a placebo. Baseline data consisted of the patient's age and the number of epileptic seizures recorded during 8 week period prior to randomization. The response consisted of counts of seizures occuring during the four consecutive follow-up periods of two weeks each.

### 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.

Breslow N. E. (1996)
Generalized linear models: Checking assumptions and strengthening
conclusions.
*Statistica Applicata* **8**, 23–41.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(epilepsy)
str(epilepsy)
pairs(epilepsy[,c("Ysum","Base4","Trt","Age10")])
Efit1 <- glm(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy)
summary(Efit1)
## Robust Fit :
Efit2 <- glmrob(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy,
method = "Mqle",
tcc=1.2, maxit=100)
summary(Efit2)
``` |