I would like to use Interrupted time series analysis for 2007-2015 data and the policy aimed to reduce the use of certain ‘low-value’ 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 low-value 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 pre-policy period, and 1 for post-policy 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 Error t Value Approx Pr > |t| 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 Durbin-Watson 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 low-value 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 Error t Value Approx Pr > |t| 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               Durbin-Watson 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 ‘low-value’ 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.   ... View more

I would like to use Interrupted time series analysis for 2007-2015 data and the policy aimed to reduce the use of certain ‘low-value’ 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 low-value 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 pre-policy period, and 1 for post-policy 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 Error t Value Approx Pr > |t| 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 Durbin-Watson 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 low-value 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 Error t Value Approx Pr > |t| 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               Durbin-Watson 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 ‘low-value’ 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.   ... View more

I ran a glm regression with log link and gamma distribution for modeling impact of appropriate cancer care on costs. Covariates include age, race/ethnicity, location, tumor stage, tumor grade to name a few. With 'no appropriate care' and 'stage 1' as reference categories for appropriate care and tumor stage variables, respectively, I get beta estimates of 9.0663, 0.6953, 0.6669 for intercept, non-appropriate care' and 'stage 2 tumor', respectively.   When I change the reference category for tumor stage to 'stage 2', I get beta estimates of 8.7319, 0.6953, and -0.3288 for intercept, non-appropriate care, and 'stage 1 tumor' respectively.   Even though the beta estimates for the key independent variable and other covariates remain the same, the beta estimate for intercept changes everytime I change the reference values of certain variables.  Why does this happen?  Would this not change the finding for key indepedent variable everytime I change the reference group for any covariate? I would appreciate if you can help me with this and also guide with an appropriate reference. Thank you in advance.   ... View more

I was conducting GLM regression with log link and gamma distribution for modeling costs.  I see that beta value for intercept changes with change in the reference groups for the covariates.  Why does this happen?  Can someone explain this? ... View more

For the table below, I would like to count observations for each patient till days_betn_clms is less than or equal to 14.  If days_betn_clms is GE 14 then a new episode of treatment starts and would like to again count claims for the new episode till 'days_betn_clms' is LE 14.  Patient_id Claim_date Lag_date Days_betn_clms 1 05/13/2016 . 1 1 06/01/2016 05/13/2016 19 2 04/10/2015 . 1 2 4/14/2015 04/10/2015 4 2 4/21/2015 4/14/2015 7 2 10/22/2015 4/21/2015 184 2 10/30/2015 10/22/2015 8 2 11/04/2015 10/30/2015 5 3 08/09/2010 . 1 3 08/16/2010 08/09/2010 7 3 08/23/2010 08/16/2010 7 3 08/30/2010 08/23/2010 7 3 09/01/2010 08/30/2010 2 3 09/10/2010 09/01/2010 9 3 06/24/2011 09/10/2010 284 3 06/27/2011 06/24/2011 3 3 06/28/2011 06/27/2011 1 3 07/15/2011 06/28/2011 17   I would like to see the new data set to be created as below: Patient_id Count 1 1 1 1 2 3 2 3 3 6 3 3 3 1   Appreciate any help! Thanks in advance.   ... View more