Posted by
Björn Türoque on
Jan 19, 2007; 3:18pm
URL: http://spssx-discussion.165.s1.nabble.com/no-subject-tp1073249p1073264.html
I think Brock is correct, a good resource on how discrete time survival
analysis works is located here.
http://www.ats.ucla.edu/stat/mplus/seminars/DiscreteTimeSurvival/default.htmWatch
the movies, they are informative, they use Mplus instead of SPSS but the
concepts and ideas are similar. Additionally the references at the end of
the slideshow and lecture point you to some good books on the topic.
Don
On 1/18/07, Richard Ristow <
[hidden email]> wrote:
>
> Well, I'll start, but others can add more than I can.
>
> At 11:59 AM 1/16/2007, Angshu Bhowmik wrote:
>
> >I have data on lengths of hospital stay (LOS, in days) in about 100
> >patients with Disease A (always present i.e. 1). There are various
> >co-morbidities: Disease B, Disease C etc which may be present or
> >absent. There are in total 18 co-morbidities, but they could be
> >grouped into 5 groups if that makes it easier to perform a more
> >sensible analysis.
>
> That grouping is the only way to do it. It gives you a mean of 20
> patients per group, which is reasonable. Keep all 18 co-morbidities,
> and you have a mean of 100/18=5.5, which isn't going to be enough.
>
> Then, you're comparing means between groups: that's a one-way analysis
> of variance. In SPSS, command MEANS is a good place to start. The
> syntax is easy, and it allows a lot of descriptive statistics by cell.
> For descriptives, I'd select COUNT, MEAN, STDDEV, MEDIAN, MIN and MAX;
> with /STATISTICS=ANOVA. From the menus: Analyze>Compare means>means.
> Don't do a test for linearity; it's not meaningful, for you.
>
> Moving on,
>
> . If the F-test says the groups differ, you'll likely want to know
> which groups have significantly higher or lower means, than which
> others. That's called multiple comparison analysis, and is available in
> command ONEWAY (Menu Analyze>Compare means>ANOVA). Select "post hoc",
> and pick a test - try BONFERRONI first, but that's something others on
> the list will know more about than I do.
>
> . I don't know the shape of your length-of-stay distributions: Do they
> cluster around a value? Or are there a lot of short stays, and a small
> proportion of much longer ones? When you run your ANOVA, look for cell
> means substantially larger than the medians; that can point to the
> latter. If it's the case, you may want to use a non-parametric ANOVA.
> Or, I'd seriously consider log-transforming the data. There may be
> other opinions on that, though. A lot of people, including me, are very
> cautious about transforming data to make it look 'nice'. That said, the
> log transform still feels like something to try, if the distributions
> show lengthy tails.
>
> Now, is that enough to get you going?
>
> -Best of luck,
> Richard
>