Abstract
BackgroundRenal transplant patients are mandated to have continuous assessment of their kidney function over time to monitor disease progression determined by changes in blood urea nitrogen (BUN), serum creatinine (Cr), and estimated glomerular filtration rate (eGFR). Multivariate analysis of these outcomes that aims at identifying the differential factors that affect disease progression is of great clinical significance. Thus our study aims at demonstrating the application of different joint modeling approaches with random coefficients on a cohort of renal transplant patients and presenting a comparison of their performance through a pseudo-simulation study. The objective of this comparison is to identify the model with best performance and to determine whether accuracy compensates for complexity in the different multivariate joint models.Methods and resultsWe propose a novel application of multivariate Generalized Linear Mixed Models (mGLMM) to analyze multiple longitudinal kidney function outcomes collected over 3 years on a cohort of 110 renal transplantation patients. The correlated outcomes BUN, Cr, and eGFR and the effect of various covariates such patient’s gender, age and race on these markers was determined holistically using different mGLMMs. The performance of the various mGLMMs that encompass shared random intercept (SHRI), shared random intercept and slope (SHRIS), separate random intercept (SPRI) and separate random intercept and slope (SPRIS) was assessed to identify the one that has the best fit and most accurate estimates. A bootstrap pseudo-simulation study was conducted to gauge the tradeoff between the complexity and accuracy of the models. Accuracy was determined using two measures; the mean of the differences between the estimates of the bootstrapped datasets and the true beta obtained from the application of each model on the renal dataset, and the mean of the square of these differences. The results showed that SPRI provided most accurate estimates and did not exhibit any computational or convergence problem.ConclusionHigher accuracy was demonstrated when the level of complexity increased from shared random coefficient models to the separate random coefficient alternatives with SPRI showing to have the best fit and most accurate estimates.
Highlights
Renal transplant patients are mandated to have continuous assessment of their kidney function over time to monitor disease progression determined by changes in blood urea nitrogen (BUN), serum creatinine (Cr), and estimated glomerular filtration rate
Higher accuracy was demonstrated when the level of complexity increased from shared random coefficient models to the separate random coefficient alternatives with separate random intercepts (SPRI) showing to have the best fit and most accurate estimates
2 Compared to shared random intercept (SHRI), 83% of the estimates generated under SPRI had associated mean difference that is 40% smaller on average, and mean squared difference that are close in value (15% reduction in mean squared differences under SPRI in 50% of the estimates)
Summary
Renal transplant patients are mandated to have continuous assessment of their kidney function over time to monitor disease progression determined by changes in blood urea nitrogen (BUN), serum creatinine (Cr), and estimated glomerular filtration rate (eGFR) Multivariate analysis of these outcomes that aims at identifying the differential factors that affect disease progression is of great clinical significance. An alternative approach that is not constrained by the issue of non-normality is the joint modeling of multivariate longitudinal outcomes discussed in [22] In this approach the interdependence among outcomes is captured by using shared latent variable(s) or the correlation between random coefficients corresponding to each outcome and the non-normality is solved using distributions and link functions from the list of general linear models. The application and assessment of performance for these models were limited to health services outcomes in extremely large datasets and were not applied on clinical outcomes from a relatively small sample size
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