tweeDEseq (version 1.18.0)

seizure: Epileptic seizure counts

Description

Data on seizure counts for 59 epileptics.

Usage

data(seizure)

Format

A data frame with 236 observations on the following 6 variables.

id

a numeric vector, identification number for each patient

count

a numeric vector, seizure counts

visit

a numeric vector, visit number

trx

a numeric vector, treatment: progabide (1) or placebo (0)

baseline

a numeric vector, baseline 8 week seizure count

age

a numeric vector, age of patient

Source

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

Details

The data are from a placebo-controlled clinical trial of 59 epileptics. Patients with partial seizures were enrolled in a randomized clinical trial of the anti-epileptic drug, progabide. Participants in the study were randomized to either progabide or a placebo, as an adjuvant to the standard anti-epileptic chemotherapy. Progabide is an anti-epileptic drug whose primary mechanism of action is to enhance gamma-aminobutyric acid (GABA) content; GABA is the primary inhibitory neurotransmitter in the brain. Prior to receiving treatment, baseline data on the number of epileptic seizures during the preceding 8-week interval were recorded. Counts of epileptic seizures during 2-week intervals before each of four successive post-randomization clinic visits were recorded.

References

P. Hougaard, M.L. Ting Lee, and G.A. Whitmore (1997): Analysis of overdispersed count data by mixtures of poisson variables and poisson processes. Biometrics 53, pages 1225-1238.

See Also

testPoissonTweedie mlePoissonTweedie

Examples

# Although this is not a differential expression dataset, it is appropriate
# to illustrate the application of the Poisson-Tweedie in
# epidemiological studies

data(seizure)
summary(seizure)

# Aggregate
aggCounts <- aggregate(x = cbind(seizure$count, seizure$trx), by =
list(seizure$id), FUN = sum)

# Estimation of the three parameters for all individuals
mleSeizure <- mlePoissonTweedie(x = aggCounts[,2], a.ini = 0, D.ini = 10)
mleSeizure

#Poisson-Tweedie test
testPoissonTweedie(x = aggCounts[,2], group = aggCounts[,3])