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Topic: URGENT Help needed with LCA model selction
Replies: 0

 Danielle Posts: 1 Registered: 6/29/06
URGENT Help needed with LCA model selction
Posted: Jun 29, 2006 7:31 PM

Hello.
I am working on my MS thesis, and a committee member and I are
disagreeing about how I have selected the best model. Based on my
readings and the LatentGold LCA manual, I think this is reasonable. I
would appreciate anyone agreeing or disagreeing with this method and
letting me know why (time is of the essence for me to complete my
thesis). Thanks!
Danielle

Quick overview of project: I sought to determine if the symptoms of
individuals experiencing a major depressive episode (MDE), with a
diagnosis of recurrent unipolar or bipolar I disorder, would cluster
into meaningful and clinically interpretable subgroups of depression.
Outpatients (N = 293; Unipolar n = 195, Bipolar n = 98) who met DSM-IV
diagnostic criteria for a MDE were assessed at intake to treatment with

the 25-item Hamilton Rating Scale for Depression 25-item (HRS-D-25). I

applied latent class analysis (LCA) to the 25 HRS-D items in the
unipolar depressed sample and then the unipolar sample combined with
the bipolar I depressed sample.

Data analysis plan: I entered the HRS-D-25 items as coded, ordinal
variables (rather than dichotomized absent/present). I applied LCA to a

195row x 25-column data matrix and 293row x 25-column data matrix with
Latent Gold 4.0. For each model, gender was entered as an active
covariate.

I first fit a one-class solution followed by a two-class solution, and
so on, until I reached a seven-class solution, or the "best"
solution. I stopped the analyses at seven classes because I felt that
more than seven classes would provide little clinical or practical
significance. I defined the best solution by the following criteria:
(1) the solution fit the data significantly better than the previous
solution and (2) the estimated parsimony of the model was associated
with a significant p-value.

First, I estimated the parsimony of each possible model solution, using

the bootstrap p-value test statistic. Since I have a small sample size

and the chi-squared estimation may be problematic, I used the bootstrap

p-value. Then, I sought to determine if each level of a restricted
model (a solution with a greater number of classes) was a significant
improvement of the less restricted model (a solution with a smaller
number of classes). I thought that if the bootstrap -2LL Diff test
statistic has a significant p-value, then the less restricted model is
more parsimonious than the more restricted model. For instance, if the

bootstrap -2LL Diff statistic is not significant, then a 3-class
solution may be accepted as a good fit and as a more parsimonious model

than a 2-class solution. Conversely, if the bootstrap -2LL Diff
p-value is significant, then a 2-class solution should be accepted as
the more parsimonious model. Next, I evaluated how well the selected
model permitted predictions of class membership based on the observed
indicator variables with entropy R-squared.

Results: Based on the Bootstrap -2LL Diff statistic, a six-class
solution was the most parsimonious solution and was able to adequately
predict class membership (entropy R2 = 0.85). The Bootstrap -2LL
Diff statistic between a 6- and 7-class solution was significant. This

suggests that the less restricted 6-class model was preferrable to the
more restricted 8-class model. In the combined unipolar-bipolar
sample, a 5-class solution was the most parsimonious solution and was
able to adequately predict class membership based on the HRS-D-25 items

(entropy R-squared = 0.87).

UNIPOLAR MODEL
L2 Bootstrap p Entropy R2 BIC (L2)
Bootstrap
-2LL Diff
1 3457.31 .33 1.0 2592.54
2 3262.55 .25 .91 2534.87
3 3174.07 .20 .83 2583.49
4 3098.52 .17 .84 2645.04
5 3027.51 .07 .86 2711.13
6 2978.08 .03 .85 2798.80 6v5, p = .10
7 2930.77 .01 .87 2888.59 7v6, p = .04

BIPOLAR MODEL
1 5310.86 .32 1.0 3834.01
2 4976.12 .27 .93 3681.04
3 4835.15 .18 .86 3721.83
4 4742.17 .10 .84 3810.62
5 4635.77 .05 .89 3885.99
6 4568.08 .04 .87 4000.07 6v5, p = .01
7 4507.73 .01 .86 4121.48 7v6, p = .00