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. 2022 Sep 2;62(4):ezac429.
doi: 10.1093/ejcts/ezac429.

Statistical primer: an introduction to the application of linear mixed-effects models in cardiothoracic surgery outcomes research-a case study using homograft pulmonary valve replacement data

Xu Wang  1 , Eleni-Rosalina Andrinopoulou  2   3 , Kevin M Veen  1 , Ad J J C Bogers  1 , Johanna J M Takkenberg  1
Affiliations

Statistical primer: an introduction to the application of linear mixed-effects models in cardiothoracic surgery outcomes research-a case study using homograft pulmonary valve replacement data

Xu Wang et al. Eur J Cardiothorac Surg. .

Abstract

Objectives: The emergence of big cardio-thoracic surgery datasets that include not only short-term and long-term discrete outcomes but also repeated measurements over time offers the opportunity to apply more advanced modelling of outcomes. This article presents a detailed introduction to developing and interpreting linear mixed-effects models for repeated measurements in the setting of cardiothoracic surgery outcomes research.

Methods: A retrospective dataset containing serial echocardiographic measurements in patients undergoing surgical pulmonary valve replacement from 1986 to 2017 in Erasmus MC was used to illustrate the steps of developing a linear mixed-effects model for clinician researchers.

Results: Essential aspects of constructing the model are illustrated with the dataset including theories of linear mixed-effects models, missing values, collinearity, interaction, nonlinearity, model specification, results interpretation and assumptions evaluation. A comparison between linear regression models and linear mixed-effects models is done to elaborate on the strengths of linear mixed-effects models. An R script is provided for the implementation of the linear mixed-effects model.

Conclusions: Linear mixed-effects models can provide evolutional details of repeated measurements and give more valid estimates compared to linear regression models in the setting of cardio-thoracic surgery outcomes research.

Keywords: Homograft; Mixed-effects model; Pulmonary valve replacement.

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Figures

Figure 1:
Figure 1:
Flowchart of linear mixed-effects models construction.
Figure 2:
Figure 2:
Explanations of random intercepts, random slopes and random nonlinear slopes.
Figure 3:
Figure 3:
Sixteen patients’ evolutions of square root of right ventricular outflow tract peak gradient showed nonlinearity.
Figure 4:
Figure 4:
Effect plots of the finally fitted model, with considerations of interactions and nonlinearities.
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