Introduction to Statistical Computing Methods in Clinical Research July 2000

Health Service Cost Review Commission (HSCRC) Data Discharge data on patients who underwent abdominal aortic surgery in one of 52 non- federal hospitals in MD. Data also obtained on the ICU organizational characteristics for hospitals in which patients were treated: Subset includes 490 patients

Types of Data Collected Outcomes (e.g. length of stay, mortality) Patient Characteristics (e.g. age, race) Comorbid Diseases (e.g. dementia, diabetes) Complications (e.g.aspiration, septicemia) Surgeon and Hospital Volume Organizational Characteristics (e.g. nurse- patient ratio, frequency of morbidity review)

Motivating Question: How are patient characteristics related to –length of stay (los)? –total charges ( totchg) ? –days in ICU ( icuday) ? –mortality ( death )?

What variables do we have to work with? describe inspect

What are the distributions of the outcomes we are considering? summarize centile hist graph tab dotplot stem

What does the patient population look like? age ( age ) race ( nonwhite ) gender ( sex )

Do the outcomes differ by gender? boxplot graph table by sex: summarize

Do the outcomes differ by race? boxplot histo table by nonwhite: summarize

Generating New Variables Length of Stay ( los ) appears “skewed” We want to “normalize” it by taking the natural log. How do we make a new variable: log(los)? generate loglos=log(los) or gen loglos=log(los)

Generating New Variables What if we want to create a categorical variable of length of stay: short versus long stay? gen longstay=1 if los>10 replace longstay=0 if los<=10 or gen longstay=cond(los>10,1,0) replace longstay=. if los==.