Building on these ideas, we used rich data on selection into and out of neighborhoods to formulate a cross-classified
multilevel model designed to estimate causal effects when contextual treatments, outcomes, and confounders all potentially vary over time (32, 33, 48).
Not exact matches
Individual growth curve
models were developed for
multilevel analysis and specifically
designed for exploring longitudinal data on individual changes over time.23 Using this approach, we applied the MIXED procedure in SAS (SAS Institute) to account for the random effects of repeated measurements.24 To specify the correct
model for our individual growth curves, we compared a series of MIXED
models by evaluating the difference in deviance between nested
models.23 Both fixed quadratic and cubic MIXED
models fit our data well, but we selected the fixed quadratic MIXED
model because the addition of a cubic time term was not statistically significant based on a log - likelihood ratio test.
Design, Setting, and Patients We assessed the relationship between quality and racial disparity using
multilevel multivariable regression
models.
The data was analyzed using generalized linear
models and generalized estimating equations, which are specifically used to address the
multilevel design of data in which schools with participating schoolchildren were randomized (rather than individual participants).