In this task, you will use the
chi-square test in Stata to determine whether gender and blood pressure cuff size
are independent of each other. The chi-square statistics is
requested from the Stata command *svy:tabulate*.

WARNING

There are several things you should be aware of while analyzing NHANES data with Stata. Please see the Stata Tips page to review them before continuing.

Remember that you need to define the SVYSET before using the SVY series of commands. The general format of this command is below:

svyset [w=weightvar], psu(psuvar) strata(stratavar) vce(linearized)

To define the survey design variables for your blood pressure cuff size (*bpacsz*) analysis, use the weight variable
for four-yours of MEC data (*wtmec4yr*), the PSU variable (*sdmvpsu*),
and strata variable (*sdmvstra*) .The* vce* option specifies the
method for calculating the variance and the default is "linearized" which is
Taylor linearization. Here is the *svyset* command for
four years of MEC data:

svyset [w= wtmec4yr], psu(sdmvpsu) strata(sdmvstra) vce(linearized)

In this example, a new variable (*cuff_size*) is created to
regroup blood pressure cuff size (*bpacsz*) from five categories to four
categories. This collapses the infant (1) and child (2) groups. Use the* gen*
command to create a new variable.

gen cuff_size=1 if bpacsz==1 | bpacsz==2

replace cuff_size=2 if bpacsz==3

replace cuff_size=3 if bpacsz==4

replace cuff_size=4 if bpacsz==5

Now, that the svyset has been defined you can use the Stata command, *svy:
tabulate,*
to produce two-way tabulations with tests of independence.
Some of the options for the *tab* command include:

*column*and*row*to display column and row percentages (if you do not specify this you will get cell proportions);*obs*lists the number of observations in each cell;*count*lists the weighted n in each cell and by adding*format(%11.0fc)*you will display the counts with commas rather than scientific notation;*ci*gives the confidence interval around each estimate, but can only be used with either*row*or*column*, not both; and- the Pearson (Rao-Scott correction
F-statistic) chi-square (
*pearson*), null-based (*null*), and Wald (*wald*) test statistics.

The general command for generating two-way tabulations is below.

svy:__tab__ulate varname,
subpop(if condition) options

Use the *svy : tabulate *command* *to produce
two-way tabulations for gender (*riagendr*) and blood
pressure cuff size (*cuff_size*) with tests of independence
for people age 20 years and older. (See Section 5.4 of Korn and Graubard
Analysis of Data from Health Surveys, pp 207-211). Use the *subpop(* )
option to select a subpopulation for analysis, rather than
select the study population in
the Stata program while preparing the data file. This
example uses an *if *statement to define the
subpopulation based on the age variable's (*ridageyr*)
value. Another option is to create a dichotomous variable where the
subpopulation of interest is assigned a value of 1, and everyone
else is assigned a value of 0. The options specified for this
example, use the *column*, *rows*, *obs*, *percent*,
*pearson*, *null* and *wald *test statistic options.

svy:tab riagendr cuff_size, subpop (if ridageyr >=20 & ridageyr<.) column row obs percent pearson null wald

Here is a table summarizing the output:

Variable |
Men age 20 and older (n=4312) |
Women age 20 and older (n=4782) |
p value |
---|---|---|---|

Cuff size | |||

(1) Infant | 0% | 0% | <0.0001 |

(2) Child | 1.5% | 5% | |

3 Adult | 29% | 44% | |

4 Large | 58% | 41% | |

5 Thigh |
12% |
10% |

Men have a larger cuff size than women – for example, 70% of men had cuff size of 4 or 5 compared to 51% of women. Cuff size varies significantly according to gender (p<0.0001). NOTE: The grayed cells have too few observations to create stable estimates and should probably not be reported.

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