Cluster randomization is often used in intervention trials, yet when individuals nested within clusters are considered as the units of analysis for outcome evaluation, it cannot be assumed that the observations are statistically independent. Observations that are not statistically independent also result when repeated measures are taken over time for the same individual. Ignoring clustered observations when performing data analysis can lead to the erroneous conclusion that the intervention under study had a statistically significant effect. Moreover, individual responses are often collected on ordinal scales; thus models for continuous or categorical data are usually not appropriate. We applied a random effect ordinal regression model to data sets from two randomized controlled intervention trials that measured graded scale non-independent responses. The first trial compared two school programmes for AIDS prevention in terms of impact (i.e., changes in the frequency of condom use). The second trial used the MOS-HIV questionnaire to measure the quality of life of new AIDS cases four times over a one-year follow-up period (only results of the role-functioning scale are reported). Regarding the first data set, the effect of the intervention was not significant, and the post-intervention frequency of condom use was mainly attributable to the pre-intervention frequency (p<0.01), with no differences among schools. Regarding the second data set, a borderline significant increase in the role-functioning scale scores was observed over the follow-up period; the results differed only slightly by intervention group; a significant (p<0.01) intra-individual correlation of 0.4 was found.
Copyright 2001 John Wiley Sons, Ltd.