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Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials

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Abstract

Objective

Typical methods of analyzing data from clinical trials have shortcomings, notably comparisons of group means, use of change scores from pre- and post-treatment assessments, ignoring intervening assessments, and focusing on direct effects of treatment. A comparison of group means disregards the likelihood that individuals have different trajectories of change. Moreover, change scores ignore intervening assessments that may provide useful information about change. This paper compares results from traditional regression-based methods for analyzing data from a clinical trial (e.g., regression with change scores) with those of latent growth curve modeling (LGM).

Methods

LGM is a method that uses structural equation modeling techniques to model individual change, assess treatment effects and the relationship among multiple outcomes simultaneously, and model measurement error. The consequence is more precise parameter estimates while using data from all available time points.

Results

Results demonstrate that LGM can yield stronger parameter estimates than the traditional regression-based approach and explain more variance in the outcome. In trials where there is a true effect, but it is non-significant or marginally significant using the traditional methods, LGM may provide evidence of this effect.

Conclusions

Analysts are encouraged to consider LGM as an additional and informative tool for analyzing clinical trial or other longitudinal data.

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Acknowledgements

The author thanks Drs. Karl Kosloski and Kyle Kercher for their extremely helpful comments on earlier drafts of this manuscript, and the comments and suggestions of two anonymous reviewers that led to a more didactic and comparative paper. Two versions of summary data (means, variances, and covariances for listwise missing and full information maximum likelihood methods of estimation of missing data) can be downloaded from the Quality of Life Research Web site or upon request to the author. The summary matrices can be used to run the example analysis using Mplus. The names of the two files are “11136_2007_9290_MOESM1.dat” and “11136_2007_9290_MOESM2.dat”.

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Authors and Affiliations

  1. Research Scientist, Center for Health Outcomes Research, United BioSource Corporation, 7101 Wisconsin Ave, Suite 600, Bethesda, MD, 20814, USA

    Donald E. Stull

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  1. Donald E. Stull
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Correspondence to Donald E. Stull.

Electronic supplementary material

Appendix: Mplus 4.2 syntax for evaluation of the model in Fig. 3

Appendix: Mplus 4.2 syntax for evaluation of the model in Fig. 3

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Stull, D.E. Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials. Qual Life Res 17, 47–59 (2008). https://doi.org/10.1007/s11136-007-9290-5

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