nparLD (version 2.2)

respiration: Respiratory disorder study

Description

Measurements of health status in an ordinal scale from 0 to 4 from a group of patients with a respiratory disorder.

Usage

data(respiration)

Format

Longitudinal data of health status of 111 patients with a respiratory disorder from both the control and treatment groups (57 and 54 patients, respectively) taken at each of their 5 visits in 2 different centers (56 and 55 patients, respectively).

Details

Researchers are often concerned with ordinal responses in clinical trials. Koch et al. (1990) discuss the problem with such ordinal data arising from a clinical trial for patients with a respiratory disorder with multiple whole-plot factors. In this dataset, a total of 111 paties from two centers were randomly assigned to either the control or treatment group, and their responses in an ordinal scale from 0 to 4, indicating their health status, were recorded over their 5 visits.

References

Brunner, E., Domhof, S., and Langer, F. (2002). Nonparametric Analysis of Longitudinal Data in Factorial Experiments, Wiley, New York.

Brunner, E. and Langer, F. (1999). Nichtparametrische Analyse longitudinaler Daten, R. Oldenbourg Verlag, Munchen Wien.

Koch, G.G., Carr, G.J., Amara, I.A., Stokes, M.E., and Uryniak, T.J. (1990). Categorical Data Analysis. Statistical Methodology in the Pharmaceutical Sciences, D. A. Berry ed., pp.389-473.

Noguchi, K., Gel, Y.R., Brunner, E., and Konietschke, F. (2012). nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments. Journal of Statistical Software, 50(12), 1-23.

Examples

## Analysis using F1-LD-F2 design ##
data(respiration)
attach(respiration)
ex.f2f1<-f2.ld.f1(y=resp, time=time, group1=center, group2=treatment, 
subject=patient, time.name="Time", group1.name="Center", 
group2.name="Treatment", description=FALSE)
# F2 LD F1 Model 
# ----------------------- 
# Check that the order of the time, group1, and group2 levels are correct.
# Time level:   1 2 3 4 5 
# Group1 level:   1 2 
# Group2 level:   A P 
# If the order is not correct, specify the correct order in time.order, 
# group1.order, or group2.order.

## Wald-type statistic 
ex.f2f1$Wald.test

#                       Statistic df     p-value
#Center                10.2569587  1 0.001361700
#Treatment              9.3451482  1 0.002235766
#Time                  17.4568433  4 0.001575205
#Center:Treatment       1.2365618  1 0.266134717
#Center:Time            8.7200395  4 0.068491057
#Treatment:Time        17.5434583  4 0.001515158
#Center:Treatment:Time  0.2898785  4 0.990458142

## ANOVA-type statistic
ex.f2f1$ANOVA.test

#                        Statistic       df      p-value
#Center                10.25695866 1.000000 0.0013616998
#Treatment              9.34514819 1.000000 0.0022357657
#Time                   4.43527016 3.320559 0.0028528788
#Center:Treatment       1.23656176 1.000000 0.2661347165
#Center:Time            1.60699585 3.320559 0.1802120504
#Treatment:Time         5.46185031 3.320559 0.0005867392
#Center:Treatment:Time  0.05915234 3.320559 0.9866660535

## ANOVA-type statistic for the whole-plot factors and 
## their interaction
ex.f2f1$ANOVA.test.mod.Box

#                 Statistic df1      df2     p-value
#Center           10.256959   1 104.9255 0.001803091
#Treatment         9.345148   1 104.9255 0.002836284
#Center:Treatment  1.236562   1 104.9255 0.268676117

## The same analysis can be done using the wrapper function "nparLD" ##

ex.f2f1np<-nparLD(resp~time*center*treatment, data=respiration, 
subject="patient", description=FALSE)
# F2 LD F1 Model 
# ----------------------- 
# Check that the order of the time, group1, and group2 levels are correct.
# Time level:   1 2 3 4 5 
# Group1 level:   1 2 
# Group2 level:   A P 
# If the order is not correct, specify the correct order in time.order, 
# group1.order, or group2.order.