Homework 5_2014.docx - HOMEWORK 5 Due Tuesday Variable Name Age City Description Age of subject in years City of hospital discharge Values Continuous 1

# Homework 5_2014.docx - HOMEWORK 5 Due Tuesday Variable Name...

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HOMEWORK 5 Due Tuesday, April 29, 2014 Variable Name Description Values Age Age of subject, in years Continuous City City of hospital discharge 1 = Chicago 2 = NYC Fuptime Follow up time in years Continuous Mitype Type of myocardial infarction 0 = unknown 1 = anterolateral wall 2 = other anterior wall 3 = inferolateral wall 4 = inferoposterior wall 5 = other inferior wall 6 = other lateral wall 7 = true posterior wall 8 = subendocardial 9 = other specified sites 10 = unspecified sites Race Race 1 = White 2 = Black 3 = Other 4 = Asian 5 = Hispanic 6 = NA Native 0 = Unknown readmcount Number of readmissions Ordinal Sex Sex 1 = Male 2 = Female Days_CC Days in coronary intensive care unit ordinal Days_IC Days in medical intensive care unit ordinal yearadm1 Year of admission 1985-2003 Part I We will be using the Medicare Discharge Data from two cities , Chicago and New York (available at course web page Lab 2014/ Lab 5. The data looks at persons aged 65 or more, who were discharged alive after having a myocardial infarction. The variable readmcount is the number of subsequent re-admissions of that person. We are particularly interested in looking at the comparison between subendocardial infarction (mitype=8) and other anterior wall infarction (mitype=2) .
1. Set up the dataset in the following way and show your SAS code as the answer of this question. a. Add data from both cities together Create a new variable for race that is in 3 categories (White=0, Black=1, Asian, Hispanic, NA Native, and other =2, Unknown = missing). b. Create offset = log(fuptime) c. Restrict the dataset to those with mitype = 2 or 8 d. Restrict the dataset to those observations with non-missing data on readmcount, mitype, race, sex, age (you will be doing a complete case analysis, therefore restrict your data to non-missing values of all the covariates that you will be using in your analyses). e. Create a new variable for age so that the reference value is age 65 (the age when one can enter the dataset at the start of Medicare Discharge data). /* Q1 */ data chicago; set "P:\Spring 2014\EPI204\chicagofinal" ; city = 0 ; run ; data nyc; set "P:\Spring 2014\EPI204\nycfinal" ; city = 1 ; run ; data hw5; set chicago nyc; if race = 1 then racevar = 0 ; else if race = 2 then racevar = 1 ; else if 3 <= race <= 6 then racevar = 2 ; else racevar = . ; if (mitype = 2 or mitype = 8 ); if (age ne . and mitype ne . and racevar ne . and readmcount ne . and sex ne . ); offset = log(fuptime); age65 = age - 65 ; male = 2 - sex; if mitype = 2 then mi = 0 ; else if mitype = 8 then

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