Can proc sql embedded in sas macros dynamically merge to data-sets, simulating residential treatment placement decisions for trouble youth? - sql

Question

Good afternoon and happy Friday, folks
I’m trying to automate a placement simulation of youth into residential treatment where they will have the highest likelihood of success. Success is operationalized as “not recidivating” within 3 years of entering treatment. Equations predicting recidivism have been generated for each location, and the equations have been applied to each individual in the scenario (based on youth characteristics like risk, age, etc., LOS). Each youth has predicted success rates for every location, which throws in a wrench: youth are not qualified for all of the treatment facilities for which they have predicted success rates. Indeed, treatment locations have differing, yet overlapping qualifications.
Let’s take a made-up example. Johnny (ID # 5, below) is a 15-year-old boy with drug charges. He could have “predicted success rates” of 91% for location A, 88% for location B, 50% for location C, and 75% for location D. Johnny is most likely to be successful (i.e., not recidivate within three years of entering treatment) if he is treated at location A; unfortunately, location A only accepts youth who are 17 years old or older; therefore, Johnny would not qualify for treatment here. Alternatively, for Johnny, location B is the next best location. Let us assume that Johnny is qualified for location B, but that all of location-B beds are filled; so, we must now look to location D, as it is now Johnny’s “best available” option at 75%.
The score so far: We are matching youth to available beds in location for which they qualify and might enjoy the greatest likelihood of success. Unfortunately, each location only has a certain number of available beds, and the number of available beds different across locations. The qualifications of entry into treatment facilities differ, yet overlap (e.g., 12-17 year-olds vs 14-20 year-olds).
In order to simulate what placement decisions might look like based on success rates, I went through the scenario describe above for over 400 youth, by hand, in excel. It took me about a week. I’d like to use PROC SQL imbedded in a SAS MACRO to automate these placement scenarios with the ultimate goals of a) obtain the ability to bootstrap iterations in order to examine effect sizes across distributions, b) save time, and c) prevent further brain damage from banging my head again desk and wall in frustration whilst doing this by hand. Whilst never having had the necessity—nay—the privilege of using SQL in my typical roll as a researcher, I believe that this time has now come to pass and I’m excited about it! Honestly. I believe it has the capacity I’m looking for. Unfortunately, it is beating the devil out of me!
Here’s what I’ve got cookin’ so far: I want to create and automate the placement simulation with the clever use of merging/joining/switching/or something like that.
I have two datasets (tables). The first dataset contains all of the youth information (one row per youth; several columns with demographics, location ranks, which correspond to the predicted success rates). The order of rows in the youth dataset (was/will be randomly generated (to simulate the randomness with which youth enter the system and are subsequently place into treatment). Note that I will be “cleaning” the youth dataset prior to merging such that rank-column cells will only be populated for programs for which a respective youth qualifies. This should take the “does the youth even qualify for the program” problem out of the equation.
However, it still leaves the issue of availability left to be contended with in the scenario.
The second dataset containing the treatment facility beds, with each row corresponding to an available bed in one of the treatment location; two columns contain bed numbers and location names. Each bed (row) has only one location cell populated, but locations will populate several cells.
Thus, in descending order, I want to merge each youth row with the available bed that represents his/her best chance of success, and so the merge/join/switch/thing should take place
on youth.Rank1= distinct TF.Location,
and if youth.Rank1≠ TF.location then
merge on youth.Rank2= TF.location,
if youth.Rank2≠ TF.location then merge at
youth.Rank3 = TF.location, etc.
Put plainly: “Merge on rank1 unless rank1 location is no longer available, then merge on rank2, unless rank2 location is no longer available, and on down the line, etc., etc., until all option are exhausted and foster care (i.e., alternative services). Is the only option.
I’ve had no success getting this to work. I haven’t even been successful getting the union function to work. About the only successful thing I’ve done in SQL so far is create a view of a single dataset. It’s pretty sad. I’ve been following this guidance, but I get hung up around the “where” command:
proc sql; /Calls the SQL procedure*/;
create table x as /*Tells SAS to create a table called x*/
select /*Specifies the column(s) to be selected*/
from /*Specificies the tables(s) (data sets) to be queried*/
where /*Subjests the data based on a condition*/
group by /*Classifies the data into groups based on the specified
column(s)*/
order by /*Sorts the resulting rows observations) by the specified
column(s)*/
; quit; /*Ends the proc sql procedure*/
Frankly, I’m stuck and I could use some advice. This greenhorn in me is in way over his head.
I appreciate any help or guidance anyone might lend.
Cheers!
P

Answer 1

The process you describe (and to be honest I skiped to the end so I might of missed something) does not lend itself to SQL because each step could affect the results of the next one. However, you want to get the most best results for the most kids. (I think a lot of that text was to convince us how important it is to help out). You don't actually give us anything we can really use to help since you don't give any details of your data model, your data, or expected results. There really is no way to answer this question. But I don't care -- I'm going to go forward with some suggestions because it is a friday and I've never done a stream of consciousness answer to a stream of consciousness question before. I will suggest you don't formulate your solution just in sql, but instead use a higher level program and engage is a process like the one described below -- because this a DB questions I've noted the locations where the DB might be involved.
Generate a list kids (this can be in a table -- called NEEDY-KID)
Have a list of locations to assign (this can also be a table LOCATION)
Run your matching for best fit from KID to location -- at this point don't worry about assign more than one kid to a location -- there can be duplicates (put this in table called KID2LOC using a query)
Check KID2LOC for locations assigned twice -- use some method to remove the duplicate ones so each loc is only assigned once. (remove from the KID2LOC using a query)
Prune the LOCATION list to remove assigned locations (once again -- a query)
If kids exist without a location go to 3 with new pruned location list.
Done.