cmm (version 0.12)

Smoking: Smoking cessation after experimental intervention

Description

Data from an experiment designed for the investigation of the effectiveness of a particular expert system intervention to convince people to quit smoking. \(N = 4,144\) subjects were randomly assigned to either the control (assessment only) or the experimental condition (TTM intervention). Information was collected on their smoking habits and their attitudes towards smoking at the start of the study, at the sixth, twelfth, eighteenth, and twenty-fourth month. For more detailed information see Bergsma et al. (2009) and Prochaska et al. (2001).

The data are tabulated in Bergsma, Croon, and Hagenaars (2009, Tables 5.11 to 5.14).

Section 5.2.3 in Bergsma, Croon and Hagenaars (2009).

Usage

data(Smoking)

Arguments

Format

A data frame with 4144 observations on the following variables.

Group

(factor): 1 = TTM intervention; 2 = Assessment only.

smst00

Behavior at beginning (ordered): 1 = Precontemplation; 2 = Contemplation; 3 = Preparation; 4 = Action; 5 = Maintenance.

smst06

Behavior after 6 months (ordered): see smst00

smst12

Behavior after 12 months (ordered): see smst00

smst18

Behavior after 18 months (ordered): see smst00

smst24

Behavior after 24 months (ordered): see smst00

References

Examples in book: http://stats.lse.ac.uk/bergsma/cmm/R%20files/Smoking.R

Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudinal categorical data. New York: Springer.

Prochaska, J. O., Velicer, W. F., Fava, J. L. Rossi, J. S., & Tosh, J. Y. (2001). Evaluating a population-based recruitment approach and a stage-based expert system intervention for smoking cessation. Addictive Behaviors, 26, 583-602.

Examples

Run this code
####################################################################################
# Read data
data(Smoking)

if (FALSE) {

dat <- Smoking

####################################################################################
# Table TXBR
# matrix producing 4x2x3x6 table TXBR

atTXBR <- MarginalMatrix(var = c("X", "B", "R1", "R2", "R3", "R4"), 
 marg = list(c("X", "B", "R1"), c("X", "B", "R2"), c("X", "B", "R3"), c("X", "B", "R4")), 
 dim = c(2, 3, 5, 5, 5, 5))
bt  <- ConstraintMatrix(var = c("T", "X", "B", "R"), suffconfigs = list(c("T", "X", "B"), c("R")), 
 dim = c(4, 2, 3, 5))
model = list(bt, "log", atTXBR)

fit = MarginalModelFit(dat = dat, model = model, MaxStepSize = .3, MaxSteps = 100, 
 ShowProgress = 5)
}

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