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  "path": "/t/power-calculations-in-longitudinal-mixed-effects-from-two-measurements-to-three-measurements/28699#post_8",
  "publishedAt": "2026-04-10T12:16:24.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "JorgeTeixeira:\n\n> The previous software also indicates that when moving from compound symmetry to AR(1), the power actually decreases.\n\nI don’t think that is possible without a significant lack of fit of AR(1).\n\nJorgeTeixeira:\n\n> Regarding questions involving two, three, or four follow-up measurements: if I understood correctly, your preference would be for GLS or Markov models?\n\nYes and if you want to hedge your bets regarding goodness of fit of the correlation structure use Markov models with random intercepts.\n\nJorgeTeixeira:\n\n> In those models, do you have to manually specify the correlation matrix during analysis, or does the models handle that automatically in the background?\n\nFor Markov you specify how the current observations depend on past observations from the same subject. For generalized least squares you specify the correlation structure and get maximum likelihood estimates of the parameters of that structure (one parameter for AR(1)).\n\nJorgeTeixeira:\n\n> Also, out of curiosity, do you believe GEE is better than linear mixed-effects models for parallel RCTs?\n\nNo. GEE can be inaccurate in estimating regression coefficients by assuming a nonsense working independence model. And GEE requires that dropouts and missing data are missing completely at random. Full modeling methods only require the missing at random assumption.",
  "title": "Power Calculations in Longitudinal Mixed Effects - from two measurements to three measurements"
}