Hello everyone,
I am working on the National Inpatient Sample (NIS) database. There are certain types of discharges that we are interested in. We selected them based on the ICD codes for the procedure of interest. We have a variable that shows how many procedures have been done by each operator/physician and we call it simply "operator volume". Now we want to see if there is a significant correlation between the complication rates of that procedure (variable: complications) with operator's experience (variable: OPERATOR_VOLUME). I found a similar paper that used the same database and has a very similar methodology with our project. So we want to duplicate their method – hierarchical mixed-effects logistic regression – to find out about the aforementioned correlation. A very simplified version of another population just as an example to work with in here as a .dta file. Below is the methodology that we want to use:
I greatly appreciate your suggestions on how to perform and interpret this analysis and its results in Stata.
Thank you so much!
Reza
I am working on the National Inpatient Sample (NIS) database. There are certain types of discharges that we are interested in. We selected them based on the ICD codes for the procedure of interest. We have a variable that shows how many procedures have been done by each operator/physician and we call it simply "operator volume". Now we want to see if there is a significant correlation between the complication rates of that procedure (variable: complications) with operator's experience (variable: OPERATOR_VOLUME). I found a similar paper that used the same database and has a very similar methodology with our project. So we want to duplicate their method – hierarchical mixed-effects logistic regression – to find out about the aforementioned correlation. A very simplified version of another population just as an example to work with in here as a .dta file. Below is the methodology that we want to use:
- Hierarchical mixed-effects logistic regression models were generated to identify the independent multivariate predictors of postprocedural complications
- Two level hierarchical models (with patient level factors nested within hospital level factors) were created with the unique hospital identification number incorporated as random effects within the model
- In all multivariate models, we included hospital level variables such as hospital bed size, hospital region (Northeast, South, Midwest with West as referent), teaching versus nonteaching hospital, and patient level variables such as age, sex, comorbidity index, median household income, and primary payer (with Medicare/ Medicaid considered as referent) in addition to hospital procedure volume or operator procedure volume or both
- The effects of hospital volume and operator volume were studied separately by creating separating models incorporating each without the other
- Subsequently a third model was created incorporating both hospital and operator volume with a term to adjust for the interaction effect between hospital and operator volume
- Hospital identification was incorporated as a random effect in the model to account for the effect of hospital clustering (meaning that patients treated at the same hospital may experience similar outcomes as a result of other processes of care)
- Because operator identification did not remain constant across the years, we could not incorporate it as a random effect in the model
- YEAR is year of discharge
- HOSPID is the hospital identification number which is unique to each one
- TRENDWT is the frequency weight which is used to estimate the total national estimate from these (so each discharge represents more than one discharge)
- AGE is age
- gender is gender
- NIS_STRATUM is the stratum used to sample hospital
- primary_payer is the primary payer
- comorbidity_index is a continuos index for measuring comorbidities
- HOSPITAL_VOLUME is the hospital procedure volume which is devided in 1 (hospitals with less than 20 procedures per year) , 2 (20-40 procedures/year), and 3 (>40 procedures/year)
- complications is whether a complication occured or not
- HOSP_BEDSIZE is the bedsize
- HOSP_REGION is the region of the hospital
- HOSP_TEACH is the teaching status
- income is income
- OPERATOR_VOLUME is the operator procedure volume which is devided in 1 (operators who have less done 20 procedures per year) , 2 (20-40 procedures/year)
I greatly appreciate your suggestions on how to perform and interpret this analysis and its results in Stata.
Thank you so much!
Reza
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