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From |
"Hoffman, George" <ghoffman@mcw.edu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: RE: RE: rmanova or anova with repeated command, what to use? |

Date |
Sun, 9 Oct 2011 14:52:18 -0500 |

Pieter - I think you want: anova y subject group/subject|group##condition time, rep(time) bse(subject) I also suggest inspecting your data graphically. I suggest 'grby' grby y group time, mean ci(95) this will help you decide if the statistics pass the smell test. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Pieter-Jan Sent: Sunday, October 09, 2011 4:05 AM To: statalist@hsphsun2.harvard.edu Subject: st: RE: RE: rmanova or anova with repeated command, what to use? Hello George, As you suggested i split 'group' into treatment group (air =1, oxygen=2) and condition (normobaric=1, hyperbaric=2). Furthermore, I transformed time started with 0h and ending 25h. When I used the command anova y group##condition, rep(time) I got error message: term not in model r 147. So I changed the command to anova y group##condition time, rep(time) which lead to the error message: could not determine between-subject basic unit; use bseunit() option r(422). When using either group or condition in the rep() term still got this r422 message. Finally I used anova y subject group##condition time, rep(subject time) and that lead to: Number of obs = 198 R-squared = 0.8626 Root MSE = 2.65175 Adj R-squared = 0.8496 Source | Partial SS df MS F Prob > F --------------------+---------------------------------------------------- Model | 7945.33764 17 467.372802 66.47 0.0000 | Subject | 7766.101 10 776.6101 110.44 0.0000 group | 15.1777131 1 15.1777131 2.16 0.1435 condition | 93.324133 1 93.324133 13.27 0.0004 group#condition | 0 0 time | 84.7484617 5 16.9496923 2.41 0.0382 | Residual | 1265.7239 180 7.03179944 --------------------+---------------------------------------------------- Total | 9211.06154 197 46.7566576 Between-subjects error term: group#condition Levels: 3 (0 df) Lowest b.s.e. variable: group Covariance pooled over: condition (for repeated variables) Repeated variable: subject Huynh-Feldt epsilon = . Greenhouse-Geisser epsilon = 0.1000 Box's conservative epsilon = 0.1000 ------------ Prob > F ------------ Source | df F Regular H-F G-G Box --------------------+---------------------------------------------------- subject | 10 110.44 0.0000 . 0.0000 0.0000 Residual | 180 ------------------------------------------------------------------------- Repeated variable: time Huynh-Feldt epsilon = . Greenhouse-Geisser epsilon = . Box's conservative epsilon = 0.2000 ------------ Prob > F ------------ Source | df F Regular H-F G-G Box --------------------+---------------------------------------------------- time | 5 2.41 0.0382 . . 0.1293 Residual | 180 ------------------------------------------------------------------------- If I interpreted this right there is no significant difference between treatment groups but there is between conditions. Time itself has also no significant effect. But before I am going to use these results can you tell me if I used the right command as you suggested or should I use another format to look for treatment effect. Once again many thanks Sincerely Yours, Pieter-Jan van Ooij -----Oorspronkelijk bericht----- Van: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Namens Hoffman, George Verzonden: 5 oktober 2011 12:35 Aan: statalist@hsphsun2.harvard.edu Onderwerp: st: RE: rmanova or anova with repeated command, what to use? You might think about, and then organize, the dataset a little differently. You want to compare the effects of two treatments on lung function, measured repeatedly over time, before and after initiation of treatment. You have two treatment groups (air, O2) and two (or three) 'conditions' (baseline, active day 2, active day 3). (question: is day 2 hyperbaric, and day 3 normobaric? Or are both under the same conditions, in which case you experiment would have only two conditions) In either case, you need to split your 'group' variable into two variables that identify the treatment group (air, O2) and condition (baseline, active). ID group condition hour 1,2...13 1,2 1,2 (3) 0,2,....22 This would allow you to use anova y group##cond, rep(hour) to look at treatment effects. Alternatively, you could code time as sequential hours form 0-72, then xtdes, i(id) t(hour) and use the xt functions. I hope this helps a little. rmanova and anova should give similar results set up this way. George Hoffman -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Pieter-Jan Sent: Tuesday, October 04, 2011 2:58 PM To: statalist@hsphsun2.harvard.edu Subject: st: rmanova or anova with repeated command, what to use? Dear statalisters, Some time ago we performed a randomized crossover study in which we monitored the lung function of a group of volunteers during three days. Day 1 was used to determine baseline lung function whereas day 2 and 3 were used to monitor lung function after inhaling either placebo (air) or active gas (oxygen) under hyperbaric condition. During each measurement day lung function was measured 6 times. All variables and observations were put in a dataset which initially had the following format: ID Group Time Result1 etc 1 0 0 6.19 2 0 0 5.97 . . . . . . . . 13 2 22 5.33 Etc ID exist of 13 persons (nr 1-13) Group: 0 (baseline), 1 (placebo), 2 (active) Time: pre, 0, 2, 4, 8, 12, 22 hours after exposure We want to perform a repeated measures anova as all subject performed all three test days which makes the groups not independent. The format we had in mind was to search for differences between the groups at a specific time point and to look for a correlation between Result1, etc and variable time. I found two possibilities of doing a repeated measures anova using Stata 9.2: 1. Using Ado rmanova written by George Hoffman 2. Using anova with command repeated. I tried both commands but they gave some contradictory results. To gave an example I put in the log results of one test: . rmanova FEF50 id time group ANOVA for var FEF50 by subject ID n=198 df=32 R2=.90011879 between effect: group Source | Partial SS df MS F Prob > F -------------+---------------------------------------------------- group | 94.488177 2 47.2440885 0.17 0.8420 id*group | 8196.56138 30 273.218713 within effect: time Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Time | 218.17681 17 12.83393 1.78 0.0343 Residual | 1226.78373 170 7.21637487 . anova FEF50 id group time, repeated(group time) Number of obs = 198 R-squared = 0.8668 Root MSE = 2.68633 Adj R-squared = 0.8457 Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Model | 7984.27781 27 295.713993 40.98 0.0000 | Id | 7766.101 10 776.6101 107.62 0.0000 group | 45.8076387 2 22.9038193 3.17 0.0443 time | 123.688633 15 8.24590887 1.14 0.3222 | Residual | 1226.78373 170 7.21637487 -----------+---------------------------------------------------- Total | 9211.06154 197 46.7566576 Between-subjects error term: id Levels: 11 (10 df) Lowest b.s.e. variable: id Repeated variable: group Huynh-Feldt epsilon = 0.6682 Greenhouse-Geisser epsilon = 0.6227 Box's conservative epsilon = 0.5000 ------------ Prob > F ------------ Source | df F Regular H-F G-G Box -----------+---------------------------------------------------- group | 2 3.17 0.0443 0.0651 0.0685 0.0784 Residual | 170 -----------+---------------------------------------------------- Repeated variable: time Huynh-Feldt epsilon = 0.4478 Greenhouse-Geisser epsilon = 0.2469 Box's conservative epsilon = 0.0667 ------------ Prob > F ------------ Source | df F Regular H-F G-G Box -----------+---------------------------------------------------- Time | 15 1.14 0.3222 0.3460 0.3482 0.3073 Residual | 170 -----------+---------------------------------------------------- . log close In rmanova there is a significant correlation with FEF50 and time (p=0.0343), while in the anova with repeated command test there is none such correlation. Can anyone advise us which option we should use: rmanova or anova with the repeated command? Many thanks. Sincerely Yours, Pieter-Jan van Ooij * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/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/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/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/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: RE: RE: RE: rmanova or anova with repeated command, what to use?***From:*"Hoffman, George" <ghoffman@mcw.edu>

**References**:**st: rmanova or anova with repeated command, what to use?***From:*"Pieter-Jan" <duikerarts@home.nl>

**st: RE: rmanova or anova with repeated command, what to use?***From:*"Hoffman, George" <ghoffman@mcw.edu>

**st: RE: RE: rmanova or anova with repeated command, what to use?***From:*"Pieter-Jan" <duikerarts@home.nl>

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