I would like to use Interrupted time series analysis for 20072015 data and the policy aimed to reduce the use of certain ‘lowvalue’ medical procedure after disease diagnosis was implemented in May 2012. I am very new to the interrupted time series analysis and would need the guidance from you all experts.
My outcome, use of lowvalue medical procedure is a binary variable coded as ‘0’ for patients with no use and ‘1’ for those with use after their disease diagnosis. I created variables time in months (month_dx) (1 through 108 for 2007 to 2015 data), ‘policy’ (0 for prepolicy period, and 1 for postpolicy period), and the interaction of time and policy (time_after_policy). I ran the following program (unadjusted) and got the following output where I did not adjust for any covariates.
proc autoreg data = imaging outest=parapst covout;
model imaging = month_dx policy time_after_policy
/method=ml nlag=12 dwprob loglikl covb;
output out=pred p=predict r=resid;
run;
Autoregressive parameters assumed given  
Variable  DF  Estimate  Standard  t Value  Approx  Variable Label 
Intercept  1  0.5558  0.005242  106.04  <.0001 

month_dx  1  0.000457  0.000127  3.58  0.0003  month of cancer dx 
policy  1  0.003365  0.008428  0.40  0.6897  policy yes/no 
time_after_policy  1  0.000625  0.000297  2.10  0.0357  time since policy 
DurbinWatson value of 2.0016
How do I interpret the values for policy (0.003365) and time_after_policy (0.000625)?
In the attached document with figures for diagnostics, I found that the figure for residuals is bimodal as my outcome is binary. Is it normal to have a bimodal figure for residuals with binary outcome? Or do I have to change the outcome data to proportion of patients receiving lowvalue medical procedure?
After I adjust for the covariates (age, race/ethnicity, socioeconomic characteristics, disease severity, etc.), I get the following output:
proc autoreg data = imaging outest=parapst covout;
model imaging = month_dx policy time_after_policy agegrp region raceeth income educ grade cci gleason psa_level tstage
/method=ml nlag=12 dwprob loglikl covb;
output out=pred p=predict r=resid;
run;
Autoregressive parameters assumed given  
Variable  DF  Estimate  Standard  t Value  Approx  Variable Label 
Intercept  1  0.0551  0.0176  3.13  0.0018 

month_dx  1  0.001058  0.000176  6.00  <.0001  month of cancer dx 
policy  1  0.0434  0.008090  5.36  <.0001  policy yes/no 
time_after_policy  1  0.001558  0.000344  4.52  <.0001  time since policy 
agegrp  1  0.0176  0.001864  9.42  <.0001  Age groups 4 categories 
region  1  0.0569  0.002227  25.56  <.0001  US region 
raceeth  1  0.003842  0.002160  1.78  0.0753  1 wh 2 aa 3 hisp 4 asian 5 oth 
income  1  0.0312  0.004939  6.31  <.0001  income 
educ  1  0.0282  0.003466  8.14  <.0001  education 
grade  1  0.1567  0.003447  45.45  <.0001  grade 
cci  1  0.0528  0.002139  24.70  <.0001  cci index 
gleason  1  0.0561  0.003938  14.25  <.0001  gleason 
psa_level  1  0.0302  0.001399  21.59  <.0001  psa_level 
tstage  1  0.008183  0.002040  4.01  <.0001  T stage 







DurbinWatson value of 2.0008
I see that the estimate for intercept becomes negative in the adjusted regression. Also the estimate of ‘time after policy’ becomes positive (p<0.0001) indicating that policy change increased the use of ‘lowvalue’ medical procedure.
Please let me know if I followed the correct steps. Also please let me know if we need to include all the covariates in the adjusted regression for interrupted time series. Any guidance will be greatly appreciated.
Hi @am12scorp ,
Although PROCs AUTOREG and ARIMA can be used to fit interrupted time series models, they assume a continuous response variable. The following paper provides an excellent discussion of fitting interrupted time series and differenceindifference models using other SAS procedures, such as PROC MIXED and PROC GENMOD. Several examples are included in the paper, along with interpretation of output. Additional references are also provided.
https://www.sas.com/content/dam/SAS/support/en/sasglobalforumproceedings/2020/46742020.pdf
I hope this reference is helpful to you!
DW
Registration is open! SAS is returning to Vegas for an AI and analytics experience like no other! Whether you're an executive, manager, end user or SAS partner, SAS Innovate is designed for everyone on your team. Register for just $495 by 12/31/2023.
If you are interested in speaking, there is still time to submit a session idea. More details are posted on the website.
Learn how to run multiple linear regression models with and without interactions, presented by SAS user Alex Chaplin.
Find more tutorials on the SAS Users YouTube channel.