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Negative Binomial Regression | SAS Data Analysis Examples

Negative binomial regression is for modeling count variables, usually for
over-dispersed count outcome variables.


Please note: The purpose of this page is to show how to use various data
analysis commands. It does not cover all aspects of the research process which
researchers are expected to do.  In particular, it does not cover data
cleaning and checking, verification of assumptions, model diagnostics or
potential follow-up analyses.

This page was updated using SAS 9.2.

Examples of negative binomial regression

Example 1.  School administrators study the attendance behavior of high
school juniors at two schools.  Predictors of the number of days of absence
include the type of program in which the student is enrolled and a standardized
test in math.

Example 2.  A health-related researcher is studying the number of hospital
visits in past 12 months by senior citizens in a community based on the
characteristics of the individuals and the types of health plans under which
each one is covered.  

Description of the data

Let’s pursue Example 1 from above.

We have attendance data on 314 high school juniors from two urban high
schools in the file https://stats.idre.ucla.edu/wp-content/uploads/2016/02/nb_data.sas7bdat. The
response variable of interest is days absent, daysabs. The variable
math gives the standardized math score for each student.  The variable
prog is a three-level nominal variable indicating the type of instructional
program in which the student is enrolled.

Let’s look at the data.  It is always a good idea to start with descriptive
statistics and plots.

Each variable has 314 valid observations and their distributions seem quite reasonable. The mean of our outcome variable is much lower than its variance.

Let’s continue with our description of the variables in this dataset. The table below shows the average numbers of days absent by program type and seems to suggest that program type is a good candidate for predicting the number of days absent, our outcome variable, because the mean value of the outcome appears to vary by
prog. The variances within each level of prog are higher than the
means within each level. These are the conditional means and variances. These
differences suggest that over-dispersion is present and that a Negative Binomial
model would be appropriate.

Analysis methods you might consider

Below is a list of some analysis methods you may have
encountered.  Some of the methods listed are quite reasonable, while others have
either fallen out of favor or have limitations. 

Negative binomial regression analysis

Negative binomial models can be estimated in SAS using proc genmod. On the class statement we list the variable prog. 
After prog, we use two options, which are given in parentheses.  The
param=ref option changes the coding of prog from effect coding,
which is the default, to reference coding.  The ref=first option
changes the reference group to the first level of prog.  We have
used two options on the model statement.  The type3 option is
used to get the multi-degree-of-freedom test of the categorical variables listed
on the class statement, and the dist = negbin option is used to
indicate that a negative binomial distribution should be used. 

We can also see the results as incident rate ratios by using estimate statements with the exp option. 

proc genmod data = nb_data;
  class prog (param=ref ref=first);
  model daysabs = math prog / type3 dist=negbin;
  estimate 'prog 2' prog 1 0 / exp;
  estimate 'prog 3' prog 0 1 / exp;
  estimate 'math'   math 1   / exp;
run;
< - some output omitted - >
                                    Contrast Estimate Results

                 Mean         Mean           L'Beta  Standard                L'Beta          Chi-
Label        Estimate   Confidence Limits  Estimate     Error   Alpha   Confidence Limits  Square

prog 2         0.6435    0.4500    0.9204   -0.4408    0.1826    0.05   -0.7986   -0.0829    5.83
Exp(prog 2)                                  0.6435    0.1175    0.05    0.4500    0.9204
prog 3         0.2784    0.1874    0.4136   -1.2787    0.2020    0.05   -1.6745   -0.8828   40.08
Exp(prog 3)                                  0.2784    0.0562    0.05    0.1874    0.4136
math           0.9940    0.9892    0.9989   -0.0060    0.0025    0.05   -0.0109   -0.0011    5.71
Exp(math)                                    0.9940    0.0025    0.05    0.9892    0.9989

The output above indicates that the incident rate for prog=2 is 0.64
times the incident rate for the reference group (prog=1). Likewise, the
incident rate for prog=3 is 0.28 times the incident rate for the
reference group holding the other variables constant. The percent change in the
incident rate of daysabs is a 1% decrease (1 - .99) for every unit
increase in math.

The form of the model equation for negative binomial regression is the same
as that for Poisson regression. The log of the outcome is predicted with a
linear combination of the predictors:

log(daysabs) = Intercept + b1(prog=2) + b2(prog=3)
+ b3math.

This implies:

daysabs = exp(Intercept + b1(prog=2) + b2(prog=3)+
b3math) = exp(Intercept) * exp(b1(prog=2)) * exp(b2(prog=3))
* exp(b3math)

The coefficients have an additive effect in the log(y) scale and the
IRR have a multiplicative effect in the y scale. The dispersion
parameter in negative binomial regression does not effect the expected counts,
but it does effect the estimated variance of the expected counts.

For additional information on the various metrics in which the results can be
presented, and the interpretation of such, please see Regression Models for
Categorical Dependent Variables Using Stata, Second Edition by J. Scott Long
and Jeremy Freese (2006).

Below we use estimate statements to calculate the predicted number of events at each level of
prog, holding all other variables (in this example, math) in the
model at their means. 

proc genmod data = nb_data;
  class prog (param=ref ref=first);
  model daysabs = math prog / type3 dist=negbin;
  estimate 'prog 1' intercept 1 prog 0 0 math 48.2675 / exp;
  estimate 'prog 2' intercept 1 prog 1 0 math 48.2675 / exp;
  estimate 'prog 3' intercept 1 prog 0 1 math 48.2675 / exp;
run;

< - some output omitted - >

                                    Contrast Estimate Results

                 Mean         Mean           L'Beta  Standard                L'Beta          Chi-
Label        Estimate   Confidence Limits  Estimate     Error   Alpha   Confidence Limits  Square

prog 1        10.2369    7.4291   14.1058    2.3260    0.1636    0.05    2.0054    2.6466  202.22
Exp(prog 1)                                 10.2369    1.6744    0.05    7.4291   14.1058
prog 2         6.5879    5.5916    7.7618    1.8852    0.0837    0.05    1.7213    2.0492  507.76
Exp(prog 2)                                  6.5879    0.5512    0.05    5.5916    7.7618
prog 3         2.8501    2.2720    3.5753    1.0473    0.1157    0.05    0.8207    1.2740   82.00
Exp(prog 3)                                  2.8501    0.3296    0.05    2.2720    3.5753

In the output above, we see that the predicted number of
events for level 1 of prog is about 10.24, holding math at its
mean.  The predicted number of events for level 2 of prog is lower at
6.59, and the predicted number of events for level 3 of prog is about
2.85. Note that the predicted count of level 2 of prog is
(6.5879/10.2369) = 0.64 times the predicted count for level 1 of prog.
This matches what we saw in the after in the incident rate ratio output table.

We can similarly obtain the predicted number of events for values of math
while holding prog constant.

proc genmod data = nb_data;
  class prog (param=ref ref=first);
  model daysabs = math prog / type3 dist=negbin;
  estimate 'math 20' intercept 1 prog 0 0 math 20 / exp;
  estimate 'math 40' intercept 1 prog 0 0 math 40 / exp;
run;

                                    Contrast Estimate Results

                  Mean         Mean           L'Beta  Standard                L'Beta          Chi-
Label         Estimate   Confidence Limits  Estimate     Error   Alpha   Confidence Limits  Square

math 20        12.1267    8.6305   17.0391    2.4954    0.1735    0.05    2.1553    2.8355  206.80
Exp(math 20)                                 12.1267    2.1043    0.05    8.6305   17.0391
math 40        10.7569    7.8092   14.8172    2.3755    0.1634    0.05    2.0553    2.6958  211.38
Exp(math 40)                                 10.7569    1.7576    0.05    7.8092   14.8172

The table above shows that when prog held at its reference level and
math at 20, the predicted count (or average number of days absent) is about
12.13;  when prog held at its reference level and math at 40, the
predicted count is about 10.76. If we compare the predicted counts at these two
levels of math, we can see that the ratio is (10.7569/12.1267) = 0.887. This
matches the IRR of 0.994 for a 20 unit change: 0.994^20 = 0.887.

You can
graph the predicted number of events using the commands below. 
Proc genmod must be run with the output statement to obtain the
predicted values in a dataset we called pred1.  We then sorted our
data by the predicted values and created a graph with proc sgplot.

The graph indicates that the most
days absent are predicted for those in program
1.  The
lowest number of predicted days absent is for those students in program 3.

proc genmod data = nb_data;
  class prog (param=ref ref=first);
  model daysabs = math prog / type3 dist=negbin;
  output out = nb_pred predicted = pred1;
run;

proc sort data = nb_pred;
  by pred1;
run;

proc sgplot data = nb_pred;
  series x=math y=pred1 / group = prog;
run;
Image sasnb2

Things to consider

References

See also