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From |
Philip Jones <pjones8@uwo.ca> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Analysis of experiment involving baseline measurements |

Date |
Tue, 05 Jul 2011 16:50:02 -0400 |

Thank you Clyde for your helpful response. To answer your queries and to request more assistance, I provide the following information. Again, I apologize for the long email in advance! Because this intervention involves training that I expect to improve performance, and that this performance is retained to some extent, I expect the baseline measurement to be lower than *both* of the subsequent two measurements. However, I also expect the third measurement to be lower than the second (but higher than the baseline), given our natural inclination to forget details of education interventions over time. So, I would expect 'tapering', as you put it. I have done as you suggested and reshaped my data to long format, giving a table like this (where group and time variables have labels, but are really integers): +---------------------------------------+ | ID group time outcome | |---------------------------------------| 1. | 1 didactic baseline 14 | 2. | 1 didactic immediate 24 | 3. | 1 didactic six_wks 17 | |---------------------------------------| 4. | 2 simulation baseline 12 | 5. | 2 simulation immediate 23 | 6. | 2 simulation six_wks 22 | |---------------------------------------| 7. | 3 didactic baseline 18 | 8. | 3 didactic immediate 24 | 9. | 3 didactic six_wks 19 | |---------------------------------------| 10. | 4 simulation baseline 16 | 11. | 4 simulation immediate 23 | 12. | 4 simulation six_wks 23 | |---------------------------------------| [snip] I subsequently ran the model as: -- xtmixed outcome i.group##i.time || ID: -- Which gives me the following output: [snip] ------------------------------------------------------------------------------ outcome | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.group | -1.25 1.837117 -0.68 0.496 -4.850684 2.350684 | time | 1 | 8.5 1.660405 5.12 0.000 5.245666 11.75433 2 | 4 1.660405 2.41 0.016 .7456661 7.254334 | group#time | 1 1 | 1.25 2.348167 0.53 0.594 -3.352323 5.852323 1 2 | 4.5 2.348167 1.92 0.055 -.1023231 9.102323 | _cons | 14 1.299038 10.78 0.000 11.45393 16.54607 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ ID: Identity | sd(_cons) | 1.111805 .8665695 .2413128 5.122442 -----------------------------+------------------------------------------------ sd(Residual) | 2.348167 .4793176 1.573904 3.50332 ------------------------------------------------------------------------------ LR test vs. linear regression: chibar2(01) = 0.55 Prob >= chibar2 = 0.2282 I want to compare the groups to each other (for the 'outcome') at the two "test" time points (non-baseline for time). i.e. I want to compare whether or not the didactic group at 2.time is different from the simulation group at 2.time. I am doing this as so: -- test 2.time = (1.group + 2.time + 1.group#2.time) -- which gives me a simplified version and a P value: ( 1) - [outcome]1.group - [outcome]1.group#2.time = 0 chi2( 1) = 3.13 Prob > chi2 = 0.0769 I am obtaining the confidence interval for the 'outcome' at varying time points as such: -- ci outcome if group==1 & time==2 -- Questions: =========== 1) Is the -- test -- command as I have structured it above the correct way to obtain the P values for between-group comparisons at varying time points? 2) What command can I use for a confidence interval of the *difference* in 'outcome' between the two groups at a certain time point? 3) In the --xtmixed-- command, am I really 'controlling' for baseline values in the traditional sense, or am I just including that (baseline) time in the model? In other words, at 1.time and 2.time, are these parameter estimates actually adjusted for baseline performance for the specific group as they would be in OLS regression? Am I actually including baseline time 'outcome' as a covariate as Clyde suggests in his message below? 4) How can I meaningfully make use of the random effects portion of this model? Many thanks in advance for any assistance. Phil > Phil Jones asks for advice in adjusting for baseline measurements when > analyzing data with two follow-up points. > > You need to first think about what theory underlies the intervention and > the implications for how the outcome score will evolve over time--the > modeling will depend on that. Do you expect both groups to improve from > pre to post and continue to improve at 6wks? If so, will they continue to > improve at the same rate as from pre- to post-, or will there be a > tapering off (or an acceleration)? Or do you expect the scores to > deteriorate somewhat at 6 wks? > > If you -reshape- your data into long format, you can model any of these > possibilities using -xtmixed- or -xtreg-. The independent variables > specification may involve a single degree-of-freedom specification of > time, or time as a factor variable, or perhaps as a spline. And your > representation of time will then have interaction terms with group. You > will also have the option of either including the baseline value as a > covariate (and not analyzing time = pre observations) or not. But you > have to have a model of the time-trajectories of the output in mind to > make the corresponding decisions. > > Hope this helps you make progress. > > > Clyde Schechter > Department of Family & Social Medicine > Albert Einstein College of Medicine > Bronx, NY, USA * * 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/

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