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From |
"Ariel Linden" <[email protected]> |

To |
<[email protected]> |

Subject |
re: st: Fwd: psmatch2 kernel matching |

Date |
Fri, 1 Nov 2013 12:01:11 -0400 |

Hi Christina, Welcome to the statalist. Please make sure to read the statalist FAQ (http://www.stata.com/support/faqs/resources/statalist-faq) before posting so that you can learn about how to post queries most effectively. For example, you need to state where you download a user-written program from, such as -psmatch2- (a user written program from http://fmwww.bc.edu/RePEc/bocode/p). Also, it's "Stata", not "STATA". As for your question below, you provided information on the first part of your analysis (ie., using kernel matching), but you did not describe what you are using it for in the outcome analysis. In any case, it appears that you are measuring the difference between groups in the time to a given event, and the output appears to show that there is no statistically significant difference between groups. Yes, you can use the _weight generated from -psmatch2- as a pweight in the outcome model. I would suggest you review the _weights to see if everyone got a weight (or else they will be dropped from the analysis). Also, it looks like there was some problem to review when you stset your data... This is probably as much as I can comment on, given what you provided... Ariel Date: Thu, 31 Oct 2013 18:21:20 -0400 From: Christina Wei <[email protected]> Subject: st: Fwd: psmatch2 kernel matching Hi all, I have a question about kernel matching/propensity match analysis. I have a database with ~ 400 pts, there are 2 groups of patients (treated and untreated), whom I am trying to match them by key covariates. I decided to go with kernel matching because it gave me the greatest amount of metabias reduction, compared to other matching algorithms. The command I used was: . psmatch2 treat_status age_1 female income_1 smoker_1 bmi_1 waist_1 edu_1 if cohort==0 & dmstatus _11==0, kernel k(epan) com logit Logistic regression Number of obs = 379 LR chi2(7) = 20.50 Prob > chi2 = 0.0046 Log likelihood = -231.45013 Pseudo R2 = 0.0424 - ---------------------------------------------------------------------------- -- treat_status | Coef. Std. Err. z P>|z| [95% Conf. Interval] - -------------+-------------------------------------------------------------- -- age_1 | .0407305 .0108218 3.76 0.000 .0195202 .0619408 female | -.2388684 .31013 -0.77 0.441 -.8467301 .3689933 income_1 | -.1101363 .0899938 -1.22 0.221 -.286521 .0662483 smoker_1 | -.0553164 .2924599 -0.19 0.850 -.6285272 .5178944 bmi_1 | .0393986 .0238286 1.65 0.098 -.0073045 .0861017 waist_1 | -.0023815 .0100084 -0.24 0.812 -.0219976 .0172346 edu_1 | .0305265 .0567433 0.54 0.591 -.0806883 .1417412 _cons | -2.340632 1.441815 -1.62 0.105 -5.166537 .4852736 - ---------------------------------------------------------------------------- -- ---------------------------------------------------------------------------- -- Then I checked covariate balance using pstest to make sure that each covariate are evenly balanced between the groups. Then I used the _weight generated from the psmatch2 command to help match up my patients in the following cox regression: . stset DM_yr_conversion if cohort==0 & dmstatus_11==0 [pw=_weight], failure(DM_censor) id(unqid_c ohort) id: unqid_cohort failure event: DM_censor != 0 & DM_censor < . obs. time interval: (DM_yr_conversion[_n-1], DM_yr_conversion] exit on or before: failure weight: [pweight=_weight] if exp: cohort==0 & dmstatus_11==0 - ---------------------------------------------------------------------------- -- 1156 total obs. 758 ignored at outset because of -if <exp>- 5 event time missing (DM_yr_conversion>=.) PROBABLE ERROR 29 weights invalid PROBABLE ERROR - ---------------------------------------------------------------------------- -- 364 obs. remaining, representing 364 subjects 54 failures in single failure-per-subject data 2131.652 total analysis time at risk, at risk from t = 0 earliest observed entry t = 0 last observed exit t = 9.745206 . end of do-file . do "C:\Users\CHRIST~1\AppData\Local\Temp\STD0h000000.tmp" . stcox i.treat_status hba1c_1 glucose_1 if _support==1 & cohort==0 failure _d: DM_censor analysis time _t: DM_yr_conversion id: unqid_cohort weight: [pweight=_weight] (sum of wgt is 4.7067e+02) Iteration 0: log pseudolikelihood = -288.35 Iteration 1: log pseudolikelihood = -266.04 Iteration 2: log pseudolikelihood = -265.86 Iteration 3: log pseudolikelihood = -265.86 Refining estimates: Iteration 0: log pseudolikelihood = -265.86194 Cox regression -- Breslow method for ties No. of subjects = 470.66 Number of obs = 360 No. of failures = 60.15 Time at risk = 2727.483 Wald chi2(3) = 47.38 Log pseudolikelihood = -265.86194 Prob > chi2 = 0.0000 (Std. Err. adjusted for 360 clusters in unqid_cohort) - ---------------------------------------------------------------------------- -- | Robust _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] - -------------+-------------------------------------------------------------- -- 1.treat_status | 1.315519 .527333 0.68 0.494 .5996365 2.886063 hba1c_1 | 5.451966 1.915138 4.83 0.000 2.738719 10.85322 glucose_1 | 1.03018 .0161385 1.90 0.058 .9990303 1.062302 - ---------------------------------------------------------------------------- -- I hope this is adequate to perform the intended analysis. I am new to STATA and propensity match analysis and your assistance is greatly appreciated. Sincerely, Christina * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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