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Longitudinal Data Analysis in Stata

Longitudinal Data Analysis in Stata. William Johnston. March 5, 2013. Accessing Workshop Materials. Go to: isites.harvard.edu / research_technologies Click on Workshops tab (on the left) and then the Longitudinal Analysis with Stata folder ( near the bottom )

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Longitudinal Data Analysis in Stata

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  1. Longitudinal Data Analysis in Stata William Johnston March 5, 2013

  2. Accessing Workshop Materials Go to: isites.harvard.edu/research_technologies • Click on Workshopstab (on the left) and then the Longitudinal Analysis with Statafolder(near the bottom) • Save all of the files to the desktop (right click and ‘Save Link As’)

  3. Agenda Pre-Estimation Data Setup xtmixed Estimation xtmelogit Estimation & Post-Estimation

  4. Data Preparation for xt___ Commands Data needs to be in long format • The append command adds the new data file to the bottom, rather than the right, of your current dataset. • Prior to appending, make sure time-varying variables are in this format: • var1 (in the first wave) • var2 (in the second wave) • var3 (in the third wave), etc.

  5. Data Preparation, cont’d Please refer to the syntax and Powerpoint slides from my Data Management workshop for more info • Merging to make a wide data file. • Reshaping data from wide to long or vise-versa. • Looping commands over multiple variables • Many other tips and tricks

  6. Is Missing Data an Issue? Chances are, you have missing data. The bad news: • You can’t ignore it, entirely. • If it’s non-random it can lead to bias. • There is no one right way to handle this issue. The good news • mi estimate works with most xt commands • The multilevel model for change can accommodate missingness.

  7. Estimation with xtmixed Anatomy of an xt command: xtmixed y x || id: z, cov(un) variance mle outcome Variable across which random slopes are estimated Display variance rather than variation Divider between fixed and random portions predictor Maximum likelihood estimation (default) Allow covariances to be estimated Cluster variable (random intercepts)

  8. An example of xtmixed Chapter 4 from ALDA / Stice et al. (1997) • Outcome: alcuse • Predictors: age, coa, peer Model A: unconditional means (p. 92) • xtmixedalcuse || id: , variance mle Model B: unconditional growth model (p. 99) • xtmixedalcuseage_14|| id: age_14 , cov(un) variance mle Model F: final conditional model with centered predictors cpeer and ccoa (p. 114) • xtmixedalcuseccoacpeerage_14 c.cpeer#c.age_14 || id: age_14, cov(un) variance mle

  9. Interpreting Results To the handout!!!

  10. An example of xtmelogit The data -- toenails!!!! This is the example from chapter 10 of Rabe-Hesketh & Skrondal (2012). Multilevel and Longitudinal Data Analysis Using Statav. 2 outcome: outcome (onycholysis--separation of nail plate from nail bed) treatment: treatment (0: itraconazole; 1: terbinafine) visit: visit number (1, 2, 3, .... 7) month: exact timing of visit in months

  11. xtmelogit, continued xtdescribe what are the missing data patterns? • MLE allows for all data to be used...no list-wise deletion! (MAR is assumed) What is the nature of the outcome, across the treatment groups? • See .do file

  12. xtmelogit, continued We want to relax the assumption of conditional independence among the outcome responses for each person • We want a patient-specific random intercept gen trt_month = treatment*month xtmelogit outcome treatment month trt_month || patient: , The or option provides odds ratios rather than log odds xtmelogit outcome treatment month trt_month || patient: , or

  13. xtmelogit: Post-Estimation How do the two groups compare, over time? margins treatment, at(month=(1(2)18)) predict(mu fixedonly) vsquish marginsplot Is there a difference between the two groups after 10 months? lincom1.treatment + trt_month*10, or lincom 0.treatment + trt_month*10, or (lincom1.treatment + trt_month*10) – (lincom1.treatment + trt_month*10), or

  14. An example of xtmepoisson The data - Breslow & Clayton (1993) use http://www.stata-press.com/data/r12/epilepsy subject (n=59) seizures: number of seizures treat: 1=medication 0=placebo visit: doctor visit lage: log of age lbas: log(.25*baseline seizures) lbas_trt: lbas x treatment v4: 4th visit indicator

  15. xtmepoisson, continued Model 1: Random intercepts, fixed effects for 4th visit xtmepoisson seizures treat lbaslbas_trtlage v4 || subject: Model 2: Remove fixed effect of v4, add random slopes for each visit xtmepoisson seizures treat lbaslbas_trtlage visit || subject: visit, cov(unstructured) intpoints(9)

  16. xtmepoisson, continued Other display possibilities variance option will display variances instead of variation irr option will display incidence-rate ratios Interpretation of Model 1: Significant drop in number of seizures at the fourth visit Treatment group has fewer seizures than placebo group

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