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Analysis of RCT Data with More Than One Follow-Up Measurement

In: Analysis of Data from Randomized Controlled Trials

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  • Jos W. R. Twisk

    (Amsterdam UMC)

Abstract

In this chapter the different methods for the analysis of RCT data with more than one follow-up measurement are discussed. A distinction is made between GLM for repeated measures and regression-based methods. First, it is shown that GLM for repeated measures has some major disadvantages and that regression-based methods can better be used to estimate intervention effects in RCTs with more than one follow-up measurement. Regarding the regression-based methods, the longitudinal analysis of covariance is shown to be the most appropriate way to estimate the intervention effect. In this method, each follow-up measurement is adjusted for the baseline value. Because data is gathered in more than one follow-up measurement, mixed model analysis must be used to take into account the dependency of the observations within the subject over time. In this chapter also an alternative repeated measures (mixed model) analysis is introduced, in which it is also possible to estimate treatment effects in an RCT with more than one follow-up measurement adjusting for the baseline value.

Suggested Citation

  • Jos W. R. Twisk, 2021. "Analysis of RCT Data with More Than One Follow-Up Measurement," Springer Books, in: Analysis of Data from Randomized Controlled Trials, chapter 0, pages 15-47, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-81865-4_3
    DOI: 10.1007/978-3-030-81865-4_3
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