help for predxcon

Predicted means, medians, or proportions for a continuous X variable

15

predxcon yvar [if exp] [in range],
xvar(xvar)
from(#)
to(#)
inc(#)
[ adjust(covlist)
poly(#)
class(classvar)
model
graph
xsectional
level(#)
linear
median
nolist
cluster(cluster_var)
savepred(filename)
graph_options ]

Description

predxcon calculates and prints predicted values (or proportions) and 95%
confidence intervals for linear, quantile, or logistic model estimates for a
continuous X variable, adjusted for covariates. Default prints predicted values
and confidence intervals; model estimates and graph can be shown. Optionally a
quadradic or a quadratic and cubic term can be added to the model. An
interaction between X and a nominal variable can be estimated and graphed.
Indicator variables are created for the nominal class variable with the lowest
category defaulting to the reference group.

Variables and options required

yvar is the dependent variable

If yvar is continuous, defaults to linear regression

If yvar is binary (0,1), defaults to logistic regression

xvar(xvar) -- continuous independent variable (interval or ordinal)

from(#) -- bottom value for xvar

to(#) -- top value for xvar

inc(#) -- increment size between bottom and top values (defaults to 1)

Options

adjust(covlist) lists any covariates. If none are specified,
unadjusted means, medians, or probabilities/proportions are reported.
Covariates are set to their mean, based on observations used in the analysis,
or can be set to user specified values (e.g., age=50 gender=1). (Note: if
an adjust variable is nominal with more than 2 categories, it must be
defined with indicator variables in the adjust list, whereas indicator
variables are created automatically in the class option)

poly(2 or 3) -- polynomial terms added: 2=quadratic 3=quadradic and cubic
(will not work with the median option)

class(classvar) -- nominal variable for an xvar by
classvar interaction

model -- for display purposes only, this option prints the regression table

graph -- displays graph of predicted values (or proportions) and 95% confidence
intervals; if class() is requested, confidence intervals are not displayed

xsectional -- option for y axis label on graph for cross-sectional studies;
does not use the term "probabilties" in the label on the y axis

level(#) -- specifies the confidence level, in percent, for
calculation of confidence intervals (default=95%)

linear -- requests linear regression when yvar is binary (0,1); if not
specified, logistic regression is assumed

median -- requests quantile regression when yvar is continuous

nolist -- does not print list of predicted values and 95% CIs

cluster(cluster_var) -- Name of cluster variable; adjusts standard
errors for intraclass correlation

savepred(filename) -- saves adjusted values and CI's to a Stata file

Examples

. predxcon chol, xvar(age) from(20) to(80) inc(5) adjust(sys hra)

Using linear regression, calculates the predicted cholesterol values for
5-year increments of age from 20 years to 80 years (i.e., age=20,25,30,...,80)
adjusted for systolic blood pressure and heart rate; displays predicted values,
but does not display model or graph

. predxcon chol, xvar(sys) f(60) t(280) i(20) adj(hra) graph class(race)

Using linear regression, calculates the predicted cholesterol values for
systolic blood pressure readings from 60 to 280 in 20mm/hg increments by
categories of race, adjusted for heart rate; tests for an age by race
interaction; displays graph and list of predicted values

. predxcon htn, xvar(chol) f(100) t(300) i(20) class(race) graph xsectional

Using logistic regression, calculates the unadjusted proportion of hypertension
for cholesterol from 100 to 300 in 20-unit increments, by categories of race;
displays a likelihood ratio test for interaction; graphs the results

. predxcon htn, xvar(age) f(40) t(80) poly(3) adj(chol=250) graph

Using logistic regression, estimates the probability of hypertension for
1-yr increments of age; terms for age-squared and age-cubed included in model
using the poly(3) option; also adjusted to a cholesterol level of 250; displays
graph

Author

J.Garrett, Professor, School of Medicine, University of North Carolina,
Chapel Hill, NC.
Email: joanne_garrett@med.unc.edu