Data on seizure counts for 59 epileptics.
A data frame with 236 observations on the following 6 variables.
a numeric vector, identification number for each patient
a numeric vector, seizure counts
a numeric vector, visit number
a numeric vector, treatment: progabide (1) or placebo (0)
a numeric vector, baseline 8 week seizure count
a numeric vector, age of patient
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.
P.F Thall, and S.C. Vail (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics, 46, 657-671,
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.
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# 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])
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