{"id":4443,"date":"2023-11-21T06:43:31","date_gmt":"2023-11-21T06:43:31","guid":{"rendered":"https:\/\/www.editage.com\/insights\/12-key-precautions-for-biomedical-researchers-analyzing-longitudinal-data\/"},"modified":"2026-05-05T18:23:43","modified_gmt":"2026-05-05T12:53:43","slug":"12-key-precautions-for-biomedical-researchers-analyzing-longitudinal-data","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/12-key-precautions-for-biomedical-researchers-analyzing-longitudinal-data","title":{"rendered":"How to analyze longitudinal data appropriately: Tips for biomedical researchers"},"content":{"rendered":"

What is a longitudinal study?<\/h2>\n

Longitudinal studies involve the collection of data from the same subjects at multiple time points. These studies play a critical role in understanding the dynamics of health and disease over time. To ensure the validity and reliability of your findings, it’s essential to take specific precautions during statistical analysis.<\/p>\n

Which are the best statistical tests for longitudinal data?<\/h2>\n

The table below summarizes popular statistical tests that are used in longitudinal biomedical research.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Test\/model<\/strong><\/td>\nOutcome type<\/strong><\/td>\nExample<\/strong><\/td>\nAdvantages<\/strong><\/td>\nLimitations<\/strong><\/td>\nBest for<\/td>\n<\/tr>\n<\/thead>\n
Linear mixed-effects model (LMM)<\/td>\nContinuous<\/td>\nTracking HbA1c levels every 3 months in a type 2 diabetes cohort with dropout<\/em><\/td>\n\n
    \n
  • Handles unbalanced \/ missing data under MAR<\/li>\n
  • Accounts for within-subject correlation<\/li>\n
  • Allows time-varying covariates<\/li>\n
  • Models individual random trajectories<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Assumes normality of residuals<\/li>\n
  • Misspecified random effects can bias estimates<\/li>\n<\/ul>\n<\/td>\n
Repeated measures with random individual trajectories<\/td>\n<\/tr>\n
Repeated measures ANOVA (RM-ANOVA)<\/td>\nContinuous<\/td>\nComparing FEV\u2081 at baseline, 6 months, and 12 months across three treatment arms in a balanced asthma trial<\/em><\/td>\n\n
    \n
  • Simple to run and interpret<\/li>\n
  • Widely understood by clinicians and reviewers<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Requires complete cases \u2014 listwise deletion biases results<\/li>\n
  • Sphericity assumption often violated<\/li>\n
  • Poor with many time points<\/li>\n<\/ul>\n<\/td>\n
Small, balanced designs with few time points<\/td>\n<\/tr>\n
Growth curve model (GCM)<\/td>\nContinuous<\/td>\nModelling individual cognitive decline trajectories (e.g. MMSE score) over 10 years in an Alzheimer’s cohort<\/em><\/td>\n\n
    \n
  • Captures nonlinear and heterogeneous growth<\/li>\n
  • Separates within- and between-person variance<\/li>\n
  • Can test predictors of trajectory shape<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Requires adequate sample size<\/li>\n
  • Complex specification<\/li>\n
  • Interpretation less intuitive than regression<\/li>\n<\/ul>\n<\/td>\n
Modelling individual trajectories over time<\/td>\n<\/tr>\n
Generalised estimating equations (GEE)<\/td>\nContinuous
\nBinary
\nCount<\/td>\n		Estimating the population-average effect of a statin on systolic blood pressure across clinic visits in a cardiology registry<\/em><\/td>\n\n
    \n
  • Robust to misspecification of correlation structure<\/li>\n
  • Population-level inference<\/li>\n
  • Flexible across outcome types<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Less efficient than LMM when model is correct<\/li>\n
  • Requires MAR<\/li>\n
  • No subject-specific inference<\/li>\n<\/ul>\n<\/td>\n
Population-average effects in cohort studies<\/td>\n<\/tr>\n
GLMM \u2014 logistic (random-effects logistic)<\/td>\nBinary<\/td>\nAssessing whether HIV-positive patients achieve viral suppression (<200 copies\/mL) at quarterly visits over 2 years of ART<\/em><\/td>\n\n
    \n
  • Subject-specific odds ratios<\/li>\n
  • Handles missing data under MAR<\/li>\n
  • Flexible covariance structures<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Computationally intensive<\/li>\n
  • OR scale can be hard to communicate<\/li>\n
  • Requires large n for stable estimates<\/li>\n<\/ul>\n<\/td>\n
Binary outcomes with repeated measures<\/td>\n<\/tr>\n
McNemar’s test<\/td>\nBinary<\/td>\nTesting whether depression screening status (positive\/negative on PHQ-9) changes from pre- to post-intervention in a paired sample<\/em><\/td>\n\n
    \n
  • Simple and well-suited to paired pre\/post designs<\/li>\n
  • No distributional assumptions<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Only two time points<\/li>\n
  • No covariates<\/li>\n
  • Does not generalise to >2 measurements<\/li>\n<\/ul>\n<\/td>\n
Two matched time points only<\/td>\n<\/tr>\n
Marginal structural model (MSM)<\/td>\nBinary
\nContinuous<\/td>\n		Estimating the causal effect of time-varying corticosteroid use on bone mineral density in a lupus cohort, where disease activity confounds both treatment and outcome<\/em><\/td>\n\n
    \n
  • Adjusts for time-varying confounders affected by prior treatment (via IPTW)<\/li>\n
  • Estimates causal, not merely associational, effects<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Sensitive to weight model misspecification<\/li>\n
  • Extreme weights inflate variance<\/li>\n
  • Requires no unmeasured confounding<\/li>\n<\/ul>\n<\/td>\n
Causal inference with time-varying confounding<\/td>\n<\/tr>\n
Negative binomial mixed model<\/td>\nCount<\/td>\nModelling number of COPD exacerbations per quarter per patient over a 2-year follow-up, with high between-patient variability<\/em><\/td>\n\n
    \n
  • Handles over-dispersion better than Poisson<\/li>\n
  • Subject-level random effects<\/li>\n
  • Suitable for skewed count distributions<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • More parameters to estimate<\/li>\n
  • Zero-inflated data may need additional modelling<\/li>\n<\/ul>\n<\/td>\n
Over-dispersed repeated count outcomes<\/td>\n<\/tr>\n
Poisson mixed model<\/td>\nCount<\/td>\nCounting seizure episodes per month in an epilepsy drug trial with an exposure offset for days at risk<\/em><\/td>\n\n
    \n
  • Natural model for rates and counts<\/li>\n
  • Includes exposure offset<\/li>\n
  • Interpretable incidence rate ratios<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Assumes mean = variance<\/li>\n
  • Under-fits over-dispersed data<\/li>\n
  • Can produce biased SEs if dispersion ignored<\/li>\n<\/ul>\n<\/td>\n
Repeated count data with modest dispersion<\/td>\n<\/tr>\n
Cox proportional hazards model<\/td>\nTime-to-event<\/td>\nTime from cancer diagnosis to first recurrence in a breast cancer surgery trial, adjusting for age, stage, and receptor status<\/em><\/td>\n\n
    \n
  • Semi-parametric \u2014 no distributional assumption for baseline hazard<\/li>\n
  • Handles censoring naturally<\/li>\n
  • Widely used and understood<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Proportional hazards assumption must hold<\/li>\n
  • Cannot easily model recurrent events<\/li>\n
  • Struggles with time-varying hazard shapes<\/li>\n<\/ul>\n<\/td>\n
Time to a single event (death, relapse, first hospitalisation)<\/td>\n<\/tr>\n
Frailty model (random-effects Cox)<\/td>\nTime-to-event<\/td>\nModelling recurrent UTI episodes in elderly care-home residents, accounting for unmeasured individual susceptibility<\/em><\/td>\n\n
    \n
  • Accounts for unmeasured between-subject variability<\/li>\n
  • Extends Cox model to recurrent events<\/li>\n
  • Appropriate for clustered survival data<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Frailty distribution assumption required<\/li>\n
  • Interpretation of frailty term not always straightforward<\/li>\n<\/ul>\n<\/td>\n
Clustered or recurrent event data<\/td>\n<\/tr>\n
Competing risks model (Fine\u2013Gray)<\/td>\nTime-to-event<\/td>\nEstimating cumulative incidence of graft-versus-host disease after bone marrow transplant, where non-relapse mortality is a competing event<\/em><\/td>\n\n
    \n
  • Models cumulative incidence directly<\/li>\n
  • Avoids overestimation of event probability when competing risks exist<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Subdistribution hazard less interpretable than cause-specific hazard<\/li>\n
  • Assumes independent censoring<\/li>\n<\/ul>\n<\/td>\n
Outcomes where other events preclude the primary event<\/td>\n<\/tr>\n
Multi-state model<\/td>\nTime-to-event<\/td>\nMapping transitions between remission, relapse, and death in a multiple sclerosis cohort over 15 years<\/em><\/td>\n\n
    \n
  • Models all transitions simultaneously<\/li>\n
  • Captures full disease course including reversible states<\/li>\n
  • Can estimate transition probabilities and sojourn times<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • High complexity<\/li>\n
  • Requires large sample for stable transition rate estimates<\/li>\n
  • Markov assumption often required<\/li>\n<\/ul>\n<\/td>\n
Complex disease trajectories with multiple states<\/td>\n<\/tr>\n
Interrupted time series (ITS) analysis<\/td>\nContinuous
\nCount<\/td>\n		Evaluating the impact of a national antibiotic prescribing guideline on monthly prescription rates across GP practices before and after implementation<\/em><\/td>\n\n
    \n
  • Quasi-experimental \u2014 controls for pre-existing trend<\/li>\n
  • Useful with aggregate or routinely collected data<\/li>\n
  • Can detect both level and slope changes<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Requires sufficient pre- and post-intervention time points<\/li>\n
  • Autocorrelation must be modelled<\/li>\n
  • Assumes no other contemporaneous changes<\/li>\n<\/ul>\n<\/td>\n
Population-level impact of an intervention at a known time point<\/td>\n<\/tr>\n
Latent class growth analysis (LCGA)<\/td>\nContinuous<\/td>\nIdentifying distinct pain trajectory subgroups (e.g. persistent, resolving, delayed-onset) in a post-surgical recovery cohort<\/em><\/td>\n\n
    \n
  • Uncovers heterogeneous subpopulations<\/li>\n
  • No assumption of a single trajectory for all participants<\/li>\n
  • Can associate class membership with predictors<\/li>\n<\/ul>\n<\/td>\n
\n
    \n
  • Class number selection is subjective<\/li>\n
  • Classes are probabilistic, not deterministic<\/li>\n
  • Requires large n for stability<\/li>\n<\/ul>\n<\/td>\n
Identifying distinct subgroups with different longitudinal trajectories<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

How to analyze longitudinal data?<\/h2>\n

Here are 10 key precautions for biomedical researchers conducting longitudinal studies:<\/p>\n

Data Quality Control<\/h3>\n

Implement rigorous data quality control measures to address issues like missing data, outliers, and inconsistencies. Data cleaning<\/a> is crucial for maintaining data integrity.<\/p>\n

Select Appropriate Statistical Techniques<\/h3>\n

Choose statistical methods that are suitable for longitudinal data, such as mixed-effects models, generalized estimating equations, or growth curve models. Using the wrong methods can lead to biased results.<\/p>\n

Longitudinal Data Structures<\/h3>\n

Recognize the different data structures in longitudinal studies, such as unbalanced, balanced, or irregularly spaced data. Your analysis plan should accommodate these structures.<\/p>\n

Account for Time<\/h3>\n

Time is a critical factor in longitudinal studies. Consider time as a covariate, and assess time trends and patterns within your data. This allows you to explore how outcomes change over time.<\/p>\n

Handling Missing Data<\/h3>\n

Develop a strategy for handling missing data<\/a>, whether through imputation or other techniques. Be transparent about your approach in your research report to ensure reproducibility.<\/p>\n

Multiple Comparisons<\/h3>\n

Be cautious about multiple comparisons<\/a>. Adjust significance levels or use methods like the Bonferroni correction to account for the increased risk of Type I errors when analyzing data at multiple time points.<\/p>\n

Control for Confounders<\/h3>\n

Identify potential confounding variables that may influence your results. Include these in your models to ensure the validity of your findings.<\/p>\n

Explore Interactions<\/h3>\n

Investigate interactions between variables, especially the interaction between predictors and time. This can reveal how the relationships<\/a> change over the course of the study.<\/p>\n

Model Assumptions<\/h3>\n

Check the assumptions of the chosen statistical models, such as linearity, independence, and homoscedasticity<\/a>. Violations of these assumptions can affect the validity of your results.<\/p>\n

Robustness Checks<\/h3>\n

Conduct sensitivity analyses to assess the robustness<\/a> of your findings. This involves testing different models or approaches to ensure the consistency of results.<\/p>\n

Data Visualization<\/h3>\n

Use data visualization techniques to explore your data before, during, and after analysis. This helps identify trends, outliers, and potential issues that may require further investigation.<\/p>\n

Transparent Reporting<\/h3>\n

Document your analytical procedures thoroughly and report all relevant details in your research papers. Transparency<\/a> is crucial for reproducibility and peer review.<\/p>\n

Looking for a trusty collaborator to help you design a longitudinal study and analyze the data? Check out Editage\u2019s <\/em>Statistical Analysis & Review Services<\/em><\/a>.<\/u><\/em><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

What is a longitudinal study? Longitudinal studies involve the collection of data from the same subjects at multiple time points. These studies play a critical role in understanding the dynamics of health and disease over time. To ensure the validity and reliability of your findings, it’s essential to take specific precautions during statistical analysis. Which […]<\/p>\n","protected":false},"author":15,"featured_media":45516,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2420,2415,2403],"tags":[2622,2540,1319,2778,366],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-4443","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis","category-data-storage-management","category-publication-support-services","tag-analysisofdata","tag-big-data","tag-statistical-analysis","tag-statistical-analysis-and-review","tag-statistical-reporting"],"acf":[],"yoast_head":"\nHow to analyze longitudinal data | Editage Insights<\/title>\n<meta name=\"description\" content=\"Learn 10 key precautions for biomedical researchers conducting longitudinal studies.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.editage.com\/insights\/12-key-precautions-for-biomedical-researchers-analyzing-longitudinal-data\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to analyze longitudinal data | Editage Insights\" \/>\n<meta property=\"og:description\" content=\"To ensure the validity and reliability of your findings, it's essential to take specific precautions during statistical analysis. 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