Continuous NHANES Web Tutorial: Clean & Recode Data: Task 3

Task 3a: How to Identify Outliers and Evaluate Their Impact Using SAS

In this task, you will check for outliers and their potential impact using the following steps:

Run a univariate analysis to obtain all default descriptive statistics.
Plot survey weight against the distribution of the variable.
Identify outliers and compare the outlier-deleted estimates with the original estimates that include the outliers.

Step 1 Check distributions by running a univariate analysis

Before you analyze your data, it is very important that you check the distribution and normality of the data and identify outliers for continuous variables.

Program to Plot Distribution of Continuous Variable
Statements	Explanation
proc univariate data =demo_BP2b	Use the proc univariate procedure to get all default descriptive statistics, such as mean, minimum and maximum values, standard deviation, and skewness, etc.
where ridstatr=2 and ridageyr>=20;	Use the where statement to select the participants who were interviewed and examined in the MEC and who were age 20 years and older.
normal plot ;	Use the normal plot statement to obtain a plot of normality.
id seqn;	Use the id statement to list the sequence numbers associated with extreme values in the output.
var lbxtc;	Use the var statement to list the variables of interest.

Highlighted items from the univariate analysis output :

In this example, five outlier values with serum cholesterol values over 475 mg/dl are identified in the distribution.
The id statement allows you to link the extreme values with identifier sequence numbers (SEQN). These sequence numbers are useful if you decide to delete these outlier cases.

Step 2: Plot a Graph of Survey Weight Against the Distribution of the Variable

In this example, you will plot the 4-year MEC survey weight (wtmec4yr) against the distribution of the cholesterol variable to determine whether the extreme observations are outliers.

Plot Exam Weight Against Cholesterol
Statements	Explanation
symbol1 value =dot height = .2; proc gplot data =demo_BP2b; where ridstatr=2 and ridageyr>=20; plot wtmec4yr*lbxtc/ frame ; title 'NHANES 1999-2002, adults age 20 years and older' ; run ;	Use the proc gplot procedure to plot the total serum cholesterol (lbxtc) by the corresponding sample weight for each observation in the dataset. Symbol and height are option statements used to format the output of the plot. Use the where statement to select the participants who were interviewed and examined in the MEC and who were age 20 years and older.

Highlighted items from plotting the survey weight against the distribution of the cholesterol variable:

Three outliers with serum cholesterol values higher than 600 mg/dl are identified from the plot.
None of these three observations has an extremely large survey weights.

Watch animation of program and output

Step 3: Identify Outliers and Compare Estimates with Outliers Deleted Against the Original Estimates with Outliers Included

In this step you will:

delete the three outliers identified in the plot above using the SEQN numbers; and
compare the mean of the new dataset without the outliers against the mean of the original dataset that includes the outliers to check the impact of the outlier observations.

Program to Create Dataset Without Outliers and Output Means of Both Datasets
Statements	Explanation
data exclu_3SPs; set demo_BP2b;	Use the data and set statements to refer to your analytic dataset.
if seqn in (10494, 13996, 17821) then delete;	Use the if, then statements to delete the outliers using their SEQN previously identified in the plot of survey weight versus distribution of the variable. The SEQNs associated with these outliers are listed in the proc univariate output under extreme observations.
proc format; value race 1='Mexican American' 2='Other Hispanic' 3='Non-Hispanic White' 4='Non-Hispanic Black' 5='Other Race - Including Multi-Racial'; run;	Use the proc format procedure to give easily understood labels to your race/ethnicity variable values.
proc means data=demo_BP2b mean stderr maxdec=1;	Use the proc means procedure to determine the mean and standard error for the dataset with the outliers.
where ridstatr=2 and ridageyr>=20;	Use the where statement to select the participants who were interviewed and examined in the MEC and who were age 20 years and older.
var lbxtc;	Use the var statement to indicate the variable of interest.
class ridreth1;	Use the class statement to group the variable of interest by race/ethnicity categories.
weight wtmec4yr;	Use the weight statement to account for the unequal probability of sampling and non-response. In this example, the MEC weight for four years of data is used.
format ridreth1 race.;	Use the format statement to label your race variable with English labels you defined in the proc format statement.
proc means data=exclu_3SPs mean stderr maxdec=1;	Use the proc means procedure to determine the mean and standard error for the dataset without the outliers.
var lbxtc;	Use the var statement to indicate the variable of interest.
class ridreth1;	Use the class statement to group the variable of interest by race/ethnicity categories.
weight wtmec4yr;	Use the weight statement to account for the unequal probability of sampling and non-response. In this example, the MEC weight for four years of data is used.
format ridreth1 race.;	Use the format statement to label your race variable with easily understood labels you defined in the proc format statement.

Highlighted items from comparison of the results with and without outliers:

In this example, the outliers do not significantly affect mean cholesterol values of the race/ethnicity subgroups or the overall mean. Therefore, you will use the dataset with the outliers for your analysis.

Watch animation of program and output

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