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 =========================================================================== 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. For information about this list, including information about the problem of inappropriate messages and information about how to unsubscribe, please see the web page at http://jse.stat.ncsu.edu/ ===========================================================================
