Quote:
Originally Posted by mistersunnyd
So I still went with the 2.5 ALOS method because if my data had special cases where the patient had a very low LOS(moved to another clinic or passed away) or very high(who knows why), my average would be affected by these extreme values, and I didn't want that to happen. After this, I also checked each condition's percentage in the clinic data and compared it to the condition's percentage in the benchmark data. I multiplied the clinic's condition's LOS if the benchmark had a greater percentage of the condition and divided the LOS otherwise. The deadline has already passed, so I can't do much now. What do you think about my overall method?

Um, well, I, uh... <awkward pause> You see, the thing is... What you need to understand is that...
I'll lay it out straight, you gave them a meaningless number. You did do some calculations and used some fancy formulas but there is no meaning behind the adjustments you made. What you did was adjust the LOS for each record which is not what case adjusting is. I think what you were attempting to adjust the LOS because you thought that cause the average for the clinic population to move closer to the mean of the benchmark population. What you need to do was adjust for the mix of conditions, which doesn't change the LOS at all and you don't need to worry about outliers (unless the population size is very small).
Here is a simple example, pretend you want to know the average age of two populations and if they are similar. One group has 50 children and 50 adults. The average age of the children is 12 and the average age of the adults is 42. Therefore the average age of group one is 27. The second group has 30 children and 70 adults with the same average ages as group one. However, the average age for group two is 33. We want to know if the ages of the groups are materially different. The quick answer is yes, the second group is older then the first. Since we can recognize that a group with more adults is going to naturally be older, we may want to adjust the numbers to compare the average age without the impact of the mix of adult versus child. What we really want to do is find a way to tell if the average age is different because of the population mix or something else. In this simple example, we know that the difference is due only to the population mix since the average age for the children and the adults are the same in the two populations. If we case adjust either group one or group two the average ages will come up identical.
If I try to replicate that with your method this is what happens. We'll break group one into four parts, each with 25 people. The average for part 1 is 8, part 2 is 16, part 3 is 38 and part 4 is 46. The average for the children is still 12 and the adults is 42 with the overall average of 27. We'll break the second group in a similar fashion. Same average ages for each part as group one but part 1 and part 2 have 15 children with part 3 and 4 having 35 adults. The average age for group two is still 33. We know that the two groups are in fact identical in age and the only difference is due to the mix of children and adults. If we apply your method (multiplying or dividing by the part number and then multiplying or dividing by 2.5) we end up with an adjusted age of 23.5. That would suggest that group two is younger then group one which we know to be untrue.
I hate to be the bearer of bad news but your methodology is broken. If I was you, I'd properly case adjust the study and see what the result is. If the results are materially different then what your method produced, I'd get in front of that before someone makes a decision based upon your bad numbers.