{"id":4423,"date":"2023-11-01T04:43:20","date_gmt":"2023-11-01T04:43:20","guid":{"rendered":"https:\/\/www.editage.com\/insights\/do-you-have-longitudinal-multilevel-or-repeated-measures-data-take-advantage-of-mixed-effects-models\/"},"modified":"2026-03-19T08:28:11","modified_gmt":"2026-03-19T02:58:11","slug":"do-you-have-longitudinal-multilevel-or-repeated-measures-data-take-advantage-of-mixed-effects-models","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/do-you-have-longitudinal-multilevel-or-repeated-measures-data-take-advantage-of-mixed-effects-models","title":{"rendered":"Do you have longitudinal, multilevel, or repeated measures data? Take advantage of mixed effects models"},"content":{"rendered":"

Mixed effects models are statistical tools designed to handle complex data structures commonly encountered in biomedical research. They\u2019re like your Swiss Army knife for analyzing data with repeated measurements or nested data, such as\u00a0longitudinal studies<\/a> or experiments involving multiple subjects or centers.<\/p>\n

At their core, mixed effects models can simultaneously capture both fixed effects (like treatment effects or covariates) and random effects (variations between subjects, time points, or any other grouping factor). This makes them incredibly versatile and powerful.<\/p>\n

Types of Mixed Effects Models<\/strong><\/h2>\n

Let\u2019s take a look at some of the most popular mixed effects models used in biomedical research.<\/p>\n

Linear Mixed Effects Model<\/i><\/h3>\n

A linear mixed effects model combines elements of both linear regression (fixed effects) and mixed effects models (random effects). Fixed effects represent population-level parameters and capture the average relationships between predictor variables and the response variable. Random effects account for subject-specific variations or hierarchical structure in the data. Random effects allow for the modeling of correlations within groups, such as repeated measurements on the same subjects. Take a look at how\u00a0Murphy et al. (2022)<\/a> used linear mixed effects models to analyze longitudinal data on growth trajectories in mice.<\/p>\n

Nonlinear Mixed-Effects Model (NLMEM)<\/i><\/h3>\n

This model allows for non-linear relationships between predictors and responses. It\u2019s used when the relationship between variables isn\u2019t linear. NLMEM includes both fixed effects (population-level parameters) and random effects (subject-specific variations). It\u2019s commonly used for modeling longitudinal data with non-linear trends. For example, see how\u00a0Bilgel et al. (2016)<\/a> used NLMEM to analyze longitudinal neuroimaging data.<\/p>\n

Generalized Linear Mixed Model (GLMM)<\/i><\/h3>\n

A GLMM is a flexible modeling approach used when the response variable is not normally distributed or when there are non-constant variances. GLMMs are useful for analyzing data with hierarchical or nested structures, count data, binary data, or other non-Gaussian response types while accounting for random variability. Take a look at how\u00a0Wang et al. (2017)<\/a> used GLMM to investigate urban-rural differences in factors affecting colorectal cancer screening.<\/p>\n

These three models cater to different types of data and research questions, providing researchers with a toolbox of options to choose from when analyzing and interpreting their data.<\/p>\n

Assumptions Underlying Mixed Effects Models<\/strong><\/h2>\n

Despite being very versatile, mixed effects models need certain conditions to be fulfilled if you want robust and reliable results. Here are some crucial assumptions you need to check for:<\/p>\n

    \n
  1. \n

    Independence of Observations: The observations within each group (defined by random effects, such as subjects or clusters) should be independent of each other. Violating this assumption can lead to biased parameter estimates and incorrect inferences.<\/p>\n<\/li>\n

  2. Homoscedasticity: This assumption implies that the variance of the errors (residuals) is constant across all levels of the random effects. Heteroscedasticity<\/a>[MR1]<\/a>\u00a0 (varying variance) can impact the accuracy of parameter estimates, so it\u2019s important to check for this.<\/li>\n
  3. Independence of Fixed and Random Effects: The fixed effects (population-level parameters) and random effects (subject-specific variations) should be independent of each other. This assumption ensures that random effects capture unique variations beyond the fixed effects.<\/li>\n
  4. Absence of Multicollinearity: Just like in traditional linear models, multicollinearity (high correlation between predictor variables) can be problematic. It can make it challenging to estimate the individual effects of predictors accurately.<\/li>\n
  5. Non-informative Missing Data: If\u00a0data are missing<\/a>, it\u2019s assumed to be missing at random, meaning that the probability of missingness is unrelated to the unobserved values themselves. Biased missing data can affect model estimates.<\/li>\n<\/ol>\n

    Keep in mind that while these assumptions are important to consider, mixed effects models are relatively robust and flexible, allowing for some deviations from these assumptions. But it\u2019s good practice to assess and\u00a0report<\/a> on the extent to which these assumptions hold in your specific dataset, as violations can impact the reliability of your results and interpretations.<\/p>\n

    Advantages of Mixed Effects Models<\/strong><\/h2>\n

    What makes mixed effects models so appealing to biomedical researchers? Take a look at some of their advantages:<\/p>\n