OM t of c.iii - Vision Research Coordinating Center - Washington ...

2/1/99 Chapter 2 Study Design page 2-17
To address the above problems, our analysis of data which addresses Aims B-D
will employ four analytic strategies that are specifically designed to analyze the data on
an eye-specific basis while formally accounting for the correlation between two eyes in
the same individual. These analytic methods are as follows.
2.9.2 Cross-sectional Regression Analyses
Investigators may not independently analyze or present CLEK Study data
collected at their CLEK Participating Clinic without specific prior approval from the
Executive Committee.
Cross-sectional regression analyses (of baseline data) with paired outcomes will
be performed using the procedures described by Rosner (1984). Rosner describes
regression approaches that generalize both linear and logistic regression analyses so as
to account for the intraclass correlation coefficient between eyes of the same patient. The
linear generalization requires normally distributed outcome measures. Both the linear
and the logistic generalizations permit eye-specific and patient-specific covariates. Both
models can also be applied when data are available on only one eye of some patients.
The CLEK Coordinating Center has copies of software, written by Professor Rosner and
his staff, which will implement these methods.
2.9.3 Longitudinal Regression Analyses with Paired Continuous Outcomes
Longitudinal regression analyses with paired continuous outcomes will be
performed using the random-effects models described by Laird and Ware (1982). The
Laird and Ware approach fits a linear regression model to each subject and assumes
that both error terms and regression coefficients are normally distributed in these lines.
It then uses the EM algorithm to combine the individual lines and generate empirical
Bayes, maximum likelihood, and restricted maximum likelihood estimates of model
parameters. Because lines are generated for individuals, the model is more general than
repeated measures analysis of variance in that it has no requirements about common
measurement times between subjects and can deal easily with missing data. The Laird
and Ware model can handle the correlated outcome measures that will be characteristic
of CLEK Study data and is appropriate for continuous, dichotomous, and timedependent
covariates. The CLEK Coordinating Center has substantial experience
implementing this model using both PROC MIXED in SAS (SAS Institute, 1992) and the
original software (Stram, 1986). PROC MIXED will be employed in analyzing CLEK
Study data.
2.9.4 Longitudinal Regression Analyses with Paired Dichotomous Outcomes
Longitudinal regression analyses with paired dichotomous outcomes will be
performed using the generalized linear models of Liang and Zeger (1986). The Liang
and Zeger model eliminates the normality assumptions of the Laird and Ware model