In article <BqUz4.32511$[EMAIL PROTECTED]>, "JP" <R E M O V E [EMAIL PROTECTED]> wrote: > Hi all, > > I am in the process of analyzing data of such type: > We have data on 1800 doctors over 49 months:few dependent variables (a > particular drug prescription level), few independent (some time related > (severity of patients seen in the months, practice volume,...) and some > constant over time: university, sex, and years of practive (which can also > be considered as time dependant)). > > id month y timeind1 ... timedep1 ... > 1 1 4 1 30 > 1 2 6 1 36 > 1 3 6 1 38 > ... > 1 48 7 1 35 > 1 49 6 1 36 > 2 1 3.6 0 62 > 2 2 3 0 58 > 2 3 5 0 68 > ... > 2 48 5 0 75 > 2 49 2 0 70 > 3 .... > > Basically, a bulletin was introduced at month 37, we want to assess if this > bulletin had an effect on a particular drug prescription pattern (y). > What we plan to do is to model y in terms of the dependant variables based > on the first 36 months, and then forecast (including a CI) on the last 13 > months. We will compare the forecast to the observed values. > I have a report from another team who did quite the same type of analysis. > They used the PROC TSCSREG in SAS. > The options were RANTWO: 2 random factors (time and MD), that's ok. > And the Parks option which allows for a first-order autoregressive term in > the model, which we need has the autocorrelation is present. > Time independant variables were introduced by stratified analysis and not > directly introduced in the model. > > Basically, I introduced in the model few explicative variables (number of > patients,... the month (1 to 36) for historical trend, and few dummy > variables (january (1/0, february... november) for seasonnality variations. > > I have several problems: the main one is the interpretation of the > parameters: I have 2 axis: time and individual. > For example: > Do a positive parameter mean: MD's with a high level of this X tend to have > a high level of Y? is that true a any time? > Or does is mean that: when X increases over time, so Y increases over time. > I am messed up with those 2 dimensions. > I am interested in the evolution of Y along time, taking into account > doctors' characteristics. > > My other problem is more technical: including the AR(1) term (the > autocorrelation is often around 0.75 for each doctor), I easily get a r^2 of > 0.99, with very few variables in the model (Y is quite stationnary over > time). Does that means that Y is mainly explained by the autocorrelation, > and that any slightly correlated variable just finish to wrap the left > variance up. > > I also have problem with the estimations: 1 computed matrix is singular, > and this matrix is used for estimations. It happens as the sample size > increases. I cannot see how I will deal with that. i am supposed to stratify > the analyses per doctors characterictics (sex and university (and age))., so > it may be the way to get away with it. > > So, anyone has experience with such type of analyses? Some hints or views to > share? > > Thanks in advance, > > JP > > PS: we plan other analyses, not related to the bulletin impact, but more on > trends over the 49 months. Are the trends similar for the different types of > MD's. > We are more interested in the trends rather than in the actual level (at any > time) for each group of MD's as the relation between mds' characterictics > and level of prescription are already known. > > PS2: does anyone know a good biostat/epidemiology newsgroup > > Your question is quite interesting . We have done work in this regard and you can look at some material on pooled cross-sectional time series at http://www.autobox.com/t2a3.html Detecting the significance of an event when you know the point of intervention is a straighforward GLM application. However sometimes the actual point of intervention is different from the "known point" due to either pre-release effects or delay. AUTOBOX can detect the presence of an intervention effect via residual diagnostic checking ( http://www.autobox.com/t2b2.html and http://www.autobox.com/t2b3.html and http://www.autobox.com/t2b4.html ) In my opinion you might be better served by visiting time series sites and forecasting/modelling conferences as the problem you allude to arises in different forms. If you wish me to help in specific I would be glad to ( 215-675-0652 ) . regards Dave Reilly Automatic Forecasting Systems http://www.autobox.com Sent via Deja.com http://www.deja.com/ Before you buy. =========================================================================== This list is open to everyone. Occasionally, less thoughtful people send inappropriate messages. Please DO NOT COMPLAIN TO THE POSTMASTER about these messages because the postmaster has no way of controlling them, and excessive complaints will result in termination of the list. 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