Research Project
The research analysis project for the course requires that you apply appropriate descriptive statistical methods to summarize, present and report results. The primary objective of this assignment is to provide you with hand on experience on evidence based public health practice. Students will obtain experience in summarizing, analyzing, interpreting and presenting of epidemiologic data. The dataset is available on Blackboard in formats for SAS. The assignment is written in a way that assumes you are using SAS.
The purpose of the study was to summarize the data in the dataset appropriately to identify disparities among different racial groups (RIDRETH1) by variety of variables. Specifically, we wanted to use the NHANES data from 2015-2016 cycle to see whether there are differences among different racial groups on different variables.
Finish CITI training and submit CITI certificate (Bonus 10 points)
The variables of interest include education (DMDEDUC2), Ratio of family income to poverty(INDFMPIR) , BMI(BMXBMI), systolic blood pressure (BPXSY3), Diastolic: Blood pres (3rd rdg) mm Hg (BPXDI3), Ever used marijuana or hashish (DUQ200), Ever used cocaine/heroin/methamphetamine (DUQ240), Fasting Glucose (mg/dL)( LBXGLU), Ever used heroin(DUQ290)
Data Cleaning
Data cleaning is first thing to do when we have a dataset.
1. Run the proc means for continuous variables to check whether this is unusual value for each continuous variable.
For example, blood pressure cannot be 0. If there is 0, we should record it as missing. Of course, in reality, we should investigate whether it has 0: entry errors or typos or no recording.
At this time, we just record it as missing since we cannot check.
data nhanes.nh1516; set nhanes.nh1516 ;
if BPXDI3=0 then BPXDI3_1 = .;
else BPXDI3_1 = BPXDI3;
run;
The code above is to say creating a new variable as BPXDI3_1. For all observations with BPXDI3 = 0, the new variables BPXDI3_1 = . (missing. ). All other observations , we let BPXDI3_1 = BPXDI3.
2. To have meaningful test results, we need to clean categorical variables as well. we saw the records with “refused to answer” or “do not know” or else.
Usually we need to recode those records as missing because in the dataset they are treated as a category, but they are no real meaning.
To do that, run proc freq to check which variables have those codes and need to recode .
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