Posted by
Angshu Bhowmik on
Jan 25, 2007; 11:19pm
URL: http://spssx-discussion.165.s1.nabble.com/no-subject-tp1073249p1073257.html
----- Original Message -----
From: "Art Kendall" <
[hidden email]>
To: <
[hidden email]>
Sent: Wednesday, January 24, 2007 1:40 PM
Subject: Re: effect of diseases on length of stay
>A lot depends on the nature of the problem that you are considering. Do
> patients with the condition routinely walk out of the hospital if they
> do not have co-morbidities? or Are they still in bed outside the
> hospital? etc.
Patients with the condition normally get discharged within about 3 - 5 days
if they do not have co-morbidities and may well walk out. They are not
usually bed bound once they have recovered from their acute illness. In
almost all cases, it is co-morbidities which seem to prolong length of stay.
I shall try your suggestions below.
Thank you all very much for your advice.
Angshu
>
> First, do scatter plots of LOS by N_of_Comorbidities with different
> colored markers for ambulatory/not ambulatory. (perhaps 4 levels
> ambulatory, home bed/assisted, skilled nursing facility, deceased.??)
>
> There are notes interspersed below.
>
> Richard Ristow wrote:
>> At 04:27 PM 1/23/2007, Angshu Bhowmik wrote:
>>
> <snip>
>>> I suppose one way to simplify it might be to categorise the lengths of
>>> stay into 3 or 4 groups e.g. very short (0) , short (1), long (2) and
>>> extra-long (3) or something like that and then use ordinal multinomial
>>> logistic regression (if I am right). Would this be an acceptable way
>>> to do it?
>>
>> No. That throws away information, and doesn't gain you anything. It
>> does NOT simplify the model for analysis, even though it means fewer
>> numbers written down.
> Very well put.
>>
>>> <snip>
>>
>> Well, it may come down to,
>> . 5-way by 2-level ANOVA, the grouping variables being presence/absence
>> of the five co-morbidity categories, probably suppressing all
>> interaction terms.
>> . Non-parametric analog of the above
>> . Survival-analysis analog of the above, if there is one
>>
>> Hey, methodologists! Help! Can you see that I'm getting near the edge
>> of what I can say confidently?
>>
>>
> In the 5 way ANOVA it will be necessary to ignore (suppress) 5 way,
> likely 4 way, and possibly 3 way interactions. You might be able to get
> the 2-way.
> By ignore I mean pool them into the error(residual) term.
>
> An additional way to look at the data is to use correlations.
> You could create 2 additional variables. N_of_comorbidities. And
> discharged ambulatory/not ambulatory.
> 1) Use CORRELATIONS to get the simple (aka zero-order) correlations of
> LOS with each of the other variables "ignoring" the other independent
> variables.
> 2) Use PARTIAL CORR to get the partials correlations of LOS with each
> of the other variables "eliminating" the 2 additional variables.
>
> put the results in a table with a row for each independent variable a
> column for the name of the variable and columns for the zero order and
> partial correlations. Of course the first two rows representing the 2
> new variables will have n/a for the partial correlation.
>
>
> Take your results with a grain of salt because your number of cases is
> so small.
>
> Art Kendall
> Social Research Consultants
>