topic Re: Understanding each predictors weight on response variables in Statistical Procedures

Understanding each predictors weight on response variables

Sweeteacha — Fri, 24 Apr 2020 23:35:37 GMT

Hi All

I have hospital's average LOS and want to reduce this by 5% in coming Fiscal year.

1.I want to understand how much each group ( 15 hospital groups ie cluster of physicans) should individually reduce to meet the goal of 5% reduction.

2. Also want to understand where/which area ( payor, physicians or groups etc.) should we focus to reduce the LOS?

I want to tell that if we focus on ABC physicians group and the payer/insurance xyz then we can reduce LOS by 5 % because ABC group when dealing with XYZ insurance has higher likelihood of having higher LOS

Any guidance on this will be highly appreciated

Re: Understanding each predictors weight on response variables

PaigeMiller — Sat, 25 Apr 2020 12:16:56 GMT

I find it difficult to relate the problem description to the title. The title talks about some kind of model, the description doesn't mention a model at all.

I want to tell that if we focus on ABC physicians group and the payer/insurance xyz then we can reduce LOS by 5 % because ABC group when dealing with XYZ insurance has higher likelihood of having higher LOS

No model needed here, just descriptive statistics about LOS.

Re: Understanding each predictors weight on response variables

Sweeteacha — Sat, 25 Apr 2020 14:02:38 GMT

Hi Miller

I apologize for confusing title.

To summarize here is my two cases:

1. I want to understand how much each group has to reduce in an average to meet the goal of 5% reduction in average LOS.

Firstly I need to run a model how each group contribute to have an average overall LOS. And then .. now how much each group had to reduce compared to its weighted factor( some group have higher influence in los because some group consist of 50% of total patients and some have just 3 patients under that groups.. which means not all groups should be treated equal)

2. Now we need to find out which area should we focus among these top 3 contributer group? Theymay think we have higher LOS because we treat high risk patient or we treat medicare patients. So we have to run a model where if insurance is same, age of patient is same, severity is same, how each groups have a influence in LOS.

In general we want to know where should we focus to be honest and we are making up all these hypothesis among groups to first target.

Does it make sense?

Thank you in advance

Re: Understanding each predictors weight on response variables

StatDave — Sat, 25 Apr 2020 15:36:58 GMT

Of course, it would be best to find a consulting statistician who can take some time with you to fully understand the nature of the hospital process you want to model and to determine the best model and methods. Having said that, it sounds to me like you could fit a model to the LOS response observed on your patients. The input data set would have one observation (row) for each patient and contain variables (columns) which include the patients' LOS values and their values on all of the relevant predictors that you propose for LOS. There is also the question of how the LOS response is distributed in any single population. Clearly, it is positive in value and presumably has many small values, so it is likely not approximately normal. If LOS is discrete, such as a number of days, then you might consider using the Poisson or negative binomial distribution. Or, you could consider using a continuous distribution, like the gamma or inverse Gaussian, whether it is discrete or a continuous measure. There is a also the question of whether the LOS values might be correlated in clusters. For example, are the LOS values from patients with the same physician, or maybe in the same group, considered to be more alike? If so, the model should take this clustering into account to get appropriate tests.

With all this information, you could fit a model in a procedure like GLIMMIX, GEE, or GENMOD. For example, assuming that LOS values are considered to be correlated within groups but independent across groups, and that LOS can be considered to have a gamma distribution, then a model like the following might be a reasonable start - but that is something you will have to explore and decide on. Any categorical predictors should appear in both the CLASS and MODEL statements. I've made up some variable names based on your description. The LSMEANS statement will produce estimated LOS values for the groups and pairwise tests among them. Note that those estimates are adjusted for all of the predictors in the model. So, in particular, they are adjusted for the group sizes because of its inclusion in the model. But what variables are in the model and the form of the model (such as containing interactions), is something you would need to explore.

proc gee data=myLOSdata;

class group medicare insurance;

model LOS = group groupsize risklevel medicare insurance age severity / dist=gamma;

repeated subject=group / type=exch;

lsmeans group / diff ilink cl;

run;