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From |
Steve Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Calculate and Test Adjusted Mean Differences |

Date |
Thu, 27 Sep 2012 17:43:51 -0400 |

On Sep 27, 2012, at 2:26 PM, Elizabeth Prezio wrote: > "I thought about using lincom, but decided that was incorrect because I > don't want to subtract coefficients" Yes you do, Elizabeth. Your notion that you can do independent sample t-tests is wrong. You don't have independent estimates of marginal means in the two groups. The estimates are correlated because the model specified main effects of the other predictors. (Estimates of variance components are also shared). -margins- with a -post- option will put the marginal means into e(b), a vector of "coefficients". You can use -lincom- on these. After the (first) -margins- command, immediately run ******************* margins, coeflegend ******************* This will show you the names of the coefficients to give -lincom-. To stay on the good side of many people here, I also suggest that you not spell "Stata" as "STATA". See the FAQ. Section 4.1. Steve First I apologize if this question is too simple for statalist. I have read all the STATA help and I have been through the statalist archives before posting this question and I am need of some advice/help. My advisors have been unable to help me. I am using xtmixed to obtain adjusted mean values of hemoglobin A1c repeated measures. I used margins to obtain adjusted means. What I want to do is calculate the mean change (time 5 -time 1) and compare this mean change between the two patient groups (patgrp) using an independent samples ttest. Here is my output followed by additional comments to more thoroughly explain what I am trying to do: . xtmixed test i.patgrp##i.time duration blmeds medchange hba1_bl || id: Performing EM optimization: Performing gradient-based optimization: Iteration 0: log likelihood = -1369.6252 Iteration 1: log likelihood = -1369.6252 Computing standard errors: Mixed-effects ML regression Number of obs = 790 Group variable: id Number of groups = 159 Obs per group: min = 4 avg = 5.0 max = 5 Wald chi2(13) = 367.58 Log likelihood = -1369.6252 Prob > chi2 = 0.0000 ---------------------------------------------------------------------------- -- test | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------------- -- 1.patgrp | .0416097 .2301982 0.18 0.857 -.4095705 .4927899 | time | 2 | -.75 .1942519 -3.86 0.000 -1.130727 -.3692734 3 | -.6725 .1942519 -3.46 0.001 -1.053227 -.2917733 4 | -.72875 .1942519 -3.75 0.000 -1.109477 -.3480233 5 | -.9198457 .1956848 -4.70 0.000 -1.303381 -.5363106 | patgrp#time | 1 2 | -.1601266 .2755816 -0.58 0.561 -.7002566 .3800034 1 3 | -.4426899 .2755816 -1.61 0.108 -.98282 .0974401 1 4 | -.72693 .2766192 -2.63 0.009 -1.269094 -.1847663 1 5 | -.6758705 .2771046 -2.44 0.015 -1.218985 -.1327556 | duration | .0376279 .0149067 2.52 0.012 .0084113 .0668446 blmeds | .3366165 .1089313 3.09 0.002 .1231152 .5501179 medchange | .3238106 .0687527 4.71 0.000 .1890578 .4585633 hba1_bl | .4035153 .0386847 10.43 0.000 .3276947 .4793358 _cons | 4.234591 .3608965 11.73 0.000 3.527247 4.941936 ---------------------------------------------------------------------------- -- ---------------------------------------------------------------------------- -- Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+---------------------------------------------- -- id: Identity | sd(_cons) | .7701702 .0663079 .6505825 .91174 -----------------------------+---------------------------------------------- -- sd(Residual) | 1.228557 .0345879 1.162602 1.298253 ---------------------------------------------------------------------------- -- LR test vs. linear regression: chibar2(01) = 89.51 Prob >= chibar2 = 0.0000 . margins i.patgrp##i.time, atmeans post Adjusted predictions Number of obs = 790 Expression : Linear prediction, fixed portion, predict() at : 0.patgrp = .5037975 (mean) 1.patgrp = .4962025 (mean) 1.time = .2012658 (mean) 2.time = .2012658 (mean) 3.time = .2012658 (mean) 4.time = .1987342 (mean) 5.time = .1974684 (mean) duration = 4.50981 (mean) blmeds = 1.258228 (mean) medchange = 1.235443 (mean) hba1_bl = 8.798228 (mean) ---------------------------------------------------------------------------- -- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------------- -- patgrp | 0 | 8.165327 .1059938 77.04 0.000 7.957583 8.373071 1 | 7.807682 .1067051 73.17 0.000 7.598543 8.01682 | time | 1 | 8.798743 .1149933 76.52 0.000 8.57336 9.024125 2 | 7.969287 .1149933 69.30 0.000 7.743905 8.19467 3 | 7.906579 .1149933 68.76 0.000 7.681196 8.131961 4 | 7.709288 .1156044 66.69 0.000 7.482708 7.935869 5 | 7.543528 .1159077 65.08 0.000 7.316353 7.770703 | patgrp#time | 0 1 | 8.778096 .1621954 54.12 0.000 8.460198 9.095993 0 2 | 8.028096 .1621954 49.50 0.000 7.710198 8.345993 0 3 | 8.105596 .1621954 49.97 0.000 7.787699 8.423493 0 4 | 8.049346 .1621954 49.63 0.000 7.731448 8.367243 0 5 | 7.85825 .1638905 47.95 0.000 7.537031 8.179469 1 1 | 8.819705 .1632058 54.04 0.000 8.499828 9.139583 1 2 | 7.909579 .1632058 48.46 0.000 7.589701 8.229456 1 3 | 7.704515 .1632058 47.21 0.000 7.384638 8.024393 1 4 | 7.364025 .164965 44.64 0.000 7.0407 7.687351 1 5 | 7.223989 .1640654 44.03 0.000 6.902427 7.545551 ---------------------------------------------------------------------------- -- I need to calculate the change in the adjusted means found under heading patgrp#time in the margins output from time 5 to time 1 and then test to see if this change is significant between the two groups. I tried using the over option for margins but get an error that says margins cannot work with its own posted results. I used pwcompare, but this appears to be the wrong approach due to the issue of multiple comparisons, and also pwcompare would not give me a p-value unless I used the bonferroni adjustment. I thought about using lincom, but decided that was incorrect because I don't want to subtract coefficients, I want to subtract adjusted means. I also tried to use predict, but all I could get was yhat. The values of yhat are not the same as margins. My advisors told me to use yhat at time 5 and subtract time 1 and then do a ttest. That sounded simple until I ran into the problem of trying to subtract two values within the same variable. There must be a simple way to get this result and I am just too novice to see it. Beth Prezio * * 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/ * * 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/

**Follow-Ups**:**RE: st: Calculate and Test Adjusted Mean Differences***From:*"Elizabeth Prezio" <eaprezio@sbcglobal.net>

**References**:**st: Calculate and Test Adjusted Mean Differences***From:*"Elizabeth Prezio" <eaprezio@sbcglobal.net>

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