Multilevel modeling versus cross‐sectional analysis for assessing the longitudinal tracking of cardiovascular risk factors over time

Abstract

Correlated data are obtained in longitudinal epidemiological studies, where repeated measurements are taken on individuals or groups over time. Such longitudinal data are ideally analyzed using multilevel modeling approaches, which appropriately account for the correlations in repeated responses in the same individual. Commonly used regression models are inappropriate as they assume that measurements are independent. In this tutorial, we use multilevel modeling to demonstrate its use for analysis of correlated data obtained from serial examinations on individuals. We focus on cardiovascular epidemiological research where investigators are often interested in quantifying the relations between clinical risk factors and outcome measures (X and Y, respectively), where X and Y are measured repeatedly over time, e.g., using serial observations on participants attending multiple examinations in a longitudinal cohort study. For instance, it may be of interest to evaluate the relations between serial measures of left ventricular mass (outcome) and of its potential determinants (i.e., body mass index, blood pressure etc.), both of which are measured over time. In this tutorial, we describe the application of multilevel modeling to cardiovascular risk factors and outcome data (using serial echocardiographic data as an example of an outcome). We suggest an analytical approach that can be implemented to evaluate relations between any potential outcome of interest and risk factors, including assessment of random effects and non-linear relations. We illustrate these steps using echocardiographic data from the Framingham Heart Study with SAS PROC MIXED.

The Clinical Spectrum of De Novo Donor-Specific Antibodies in Pediatric Renal Transplant Recipients

“…To allow for analysis of the relationship between GFR and DSA over time, a multilevel linear model was employed . This enables analysis of fluctuating MFI levels and its effect on GFR rather than treating DSA as a positive or negative result.…”

“…Adult studies also cannot automatically be applied to the pediatric population and specifically in this case, B cell subpopulations in children mature as the child gets older . We address these concerns in our prospective cohort study of pediatric RTR using multivariable analysis and multilevel linear modeling to assess longitudinal data on effects of DSA on GFR and graft outcome .…”

Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk

“…Typically, landmark-age models are constructed using Cox proportional hazards models with the last observed risk factor values. We propose an extension to this, whereby we replace the last observed values with error-free risk factor values estimated from a multivariate linear mixed-effects model using all available repeated measures of past risk factor values for each landmark age ( 8 ). Multivariate mixed-effects models intrinsically handle unobserved data and sporadically recorded repeat measures ( 9 ) and their measurement errors ( 10 ).…”

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

“…However, there are modeling approaches available that allow modeling of longitudinal changes estimated from all available repeated measurements, including regression calibration approaches ( 12 ) that estimate “usual” risk factor levels. Another approach is longitudinal modeling that offers the advantage of being able to model intraindividual differences over time while still accounting for the correlation in repeated measurements ( 13 ). This approach was utilized in a population study to incorporate information on blood pressure trajectories into CVD risk prediction models ( 14 ) and in a recent simulation study that examined the potential of joint longitudinal and survival models for modeling systolic and diastolic blood pressure trajectories ( 15 ).…”