smoking: Subsample (N=133) of data on FEV1 from the...
In ALA: Data sets and examples for the book "Applied Longitudinal Analysis"

Description Usage Format Details Note Source References Examples

In an epidemiologic study conducted in two different areas in the Netherlands, the rural area of Vlagtwedde in the north-east and the urban, industrial area of Vlaardingen in the south-west, residents were followed over time to obtain information on the prevalence of and risk factors for chronic obstructive lung diseases.

1	data(smoking)

A data frame with 771 observations on the following 4 variables.

id: a factor with 133 levels
smoker: a factor with levels former and current
year: a numeric vector; years since baseline measurement
FEV1: a numeric vector; forced expiratory volume

This dataset is based on the sample of men and women from the rural area of Vlagtwedde. The sample, initially aged 15-44, participated in follow-up surveys approximately every 3 years for up to 21 years. At each survey, information on respiratory symptoms and smoking status was collected by questionnaire and spirometry was performed. Pulmonary function was determined by spirometry and a measure of forced expiratory volume (FEV1) was obtained every three years for the first 15 years of the study, and also at year 19.

The dataset is comprised of a sub-sample of 133 residents aged 36 or older at their entry into the study and whose smoking status did not change over the 19 years of follow-up. Each study participant was either a current or former smoker. Current smoking was defined as smoking at least one cigarette per day. In this dataset FEV1 was not recorded for every subject at each of the planned measurement occasions. The number of repeated measurements of FEV1 on each subject varied from 1 to 7.

Original variable names have been adapted to R conventions.

http://biosun1.harvard.edu/~fitzmaur/ala

van der Lende R, Kok TJ, Peset R, Quanjer PH, Schouten JP, Orie NGM (1981) Decreases in VC and FEV1 with time: Indicators for effects of smoking and air pollution. Bulletin of European Physiopathology and Respiration 17:775-792

str(smoking)
summary(smoking)

if (require(lme4)) {
    ## Assuming this is the random effect structure in p. 152
    lmer(FEV1 ~ smoker * year + (year | id), data=smoking,
         control=list(maxIter=2000))
}