### sas >> Repeated measures & random effects

Can anyone explain which occasions it would be appropriate to use Random
effects as opposed to fixed effects (in a regression). In an experimental
design sense a fixed effect is when we are only concerned about making
inferences about the particular treatments such as 10mg, 20 mg, 30mg. A
random effect seeks to make inferences about all treatments (population)
based on a random sample of 10mg, 20mg 30mg. This concept is
understandable, but a random effect's use in a regression model isn't.

Problem. If we are looking to model the amount of revenue expected to be
collected given a bill amount (y being the amount paid on that bill) or
the probability of paying a bill, using many other variables as
predictors. The bill amounts are taken for a 3 year period per account.
Note that all accounts may not have 3 years worth of data. This appears
to be a repeated measure problem. What models or procs would be needed to
model these two models or models like it? Would you treat this as a
random or a fixed effect? If one chooses random wouldn't it only be
because the bill amounts were chosen from a population of bill amounts per
account? If one uses all possible amounts per customer would it then be a
fixed effect? What models are procs should be used for eithr case?

Is a repeated measure model the only way to model this?

DW

### sas >> Repeated measures & random effects

Are there repeated measures on other independent variables besides the
bill amounts?

Phil

### sas >> Repeated measures & random effects

On Thu, 28 Apr 2005 22:11:51 -0400, Darryl Wilson < XXXX@XXXXX.COM >

Are there other repeated measures other than the billing amounts?

Phil

### sas >> Repeated measures & random effects

On Fri, 29 Apr 2005 09:59:46 -0400, Phil Jackson

experimental
to
per
a

Yes there are other repeated measures variables such as minutes of use per
month, but there are also variable values taken only once such as days of
rate plan, total days of service, and other demographic variables such as
sex and income.

DW

```Can anyone explain which occasions it would be appropriate to use Random
effects as opposed to fixed effects (in a regression).  In an experimental
design sense a fixed effect is when we are only concerned about making
inferences about the particular treatments such as 10mg, 20 mg, 30mg.  A
random effect seeks to make inferences about all treatments (population)
based on a random sample of 10mg, 20mg 30mg.  This concept is
understandable, but a random effect's use in a regression model isn't.

Problem.  If we are looking to model the amount of revenue expected to be
collected given a bill amount (y being the amount paid on that bill) or
the probability of paying a bill,  or to build an attrition model using
many other variables as predictors (some predictors are repeated measures
such as monthly usage while other aren't such as rate or salary.  The bill
amounts are taken for a 3 year period per account.
Note that all accounts may not have 3 years worth of data.  This appears
to be a repeated measure problem.  What models or procs would be needed to
model these two models or models like it?  Would you treat this as a
random or a fixed effect?  If one chooses random wouldn't it only be
because the bill amounts were chosen from a population of bill amounts per
account?  If one uses all possible amounts per customer would it then be a
fixed effect?  What models are procs should be used for eithr case?

Is a repeated measure model the only way to model this?

Thanks
DW
```

```Hi everyone,

I'm fairly new to SAS (and repeated measures analysis and mixed
models), so bear with me here:

The dataset I'm working with consists of counts of birds flying into n
= 27 study sites, on multiple days, in multiple years (note: no more
than one observation per day at a given site). The tricky part is that
the data are unbalanced: Not all sites were surveyed in all years,
with some sites surveyed in more years than others. Likewise, not all
sites were surveyed on the same day, with some sites surveyed on more
days than others in a given year. What I like to know, is whether
there are significant year and day effects on the number of birds
observed flying into each site (i.e., I'd like to examine both daily
and yearly variation). The model would therefore look something like
this:

IBijk = Yeari + Day(Yeari )j + Eijk

where IBijk = the number of incoming birds in year i on day j at site
k.

With missing data and unequal time intervals, my options for analysis
are either PROC mixed or PROC Glimmix (if I don't want entire
observations dropped if one value is missing, which I don't). For PROC
mixed, I stuck on how to specify a day (i.e., Julian date) and year
time period in the repeated statement (i.e., the epeated-effect.
Right now I have:

PROC mixed DATA = dataset;
CLASS SiteName Year JulDay;
MODEL IB= Year JulDay(Year);
REPEATED< /subject= SiteName type=un;
run;

However, I don know what to put in the<, or if the
code above is even appropriate. I've been all through the literature
and forums, but can't seem to find an example where there are two
within-subject effects (i.e., where measurements on the subject are
repeated, and being examined, over both day and year, as above, or
something similar), and where one of these effects is nested in the
other.

Any help would be greatly appreciated. Please let me know if you need
more details.

Cheers,
Jenn
```

```Hi all,

I wonder what kind of analysis technique I should use in the following
situation:

We had baseline data + repeated mesures on covariates and also on the
outcome.
It turned out that outcome measures were highly variables (there also
was a change in the reading procedure).

So we decided to have a clinician judge the overall evolution of each
patient's outcome and decide if its status regress or not.

I still have the baseline info.
My question is regarding the repeated covariates measures.
Each patient had a different number of visits (it goes from 5 to 15
visits), each time, some measurements were done (some variables were
also measured more often than some other). We would like to use that
info.

What kind of model I should go with?
It's the repeated covariates but single outcome thing that messes me
up.

Thanks for you input (Here and private mail, if possible).

Thanks,

JP
```

```Hi,

Random effect model and random coefficient model are two different
models. However, I am confused by SAS code how to specify them. Anyone
has simple codes to distinguish them? Thanks.

Specifically, I am interested in the following specification

Y=X*beta + Z*gamma + e,
where X,Z are covariates, beta is a fixed coefficient, gamma is a
random coefficient, which equals T*alpha + u, both u and e are error
term

By simplification, Y=X*beta+Z*T*alpha+Z*u+e

This is a random coefficient model, isn't it? Can I get estimates for
both gamma, and alpha?

Thanks very much.
```