A graphical approach to assess the goodness-of-fit of random-effects linear models when the goal is to measure individual benefits of medical treatments in severely ill patients.

BMC Medical Research Methodology
Zhiwen Wang, Francisco J Diaz

Abstract

Two-dimensional personalized medicine (2-PM) models are tools for measuring individual benefits of medical treatments for chronic diseases which have potential applications in personalized medicine. These models assume normality for the distribution of random effects. It is necessary to examine the appropriateness of this assumption. Here, we propose a graphical approach to assessing the goodness-of-fit of 2-PM models with continuous responses. We propose benefit quantile-quantile (BQQ) plots which compare the empirical quantiles of individual benefits from a patient sample predicted through an empirical Bayes (EB) approach versus the quantiles of the theoretical distribution of individual benefits derived from the assumption of normality for the random effects. We examine the performance of the approach by conducting a simulation study that compared 2-PM models with non-normal distributions for the random effects versus models with comparable normal distributions. Cramer-von Mises discrepancies were used to quantify the performance of the approach. The approach was illustrated with data from a clinical trial of imipramine for patients with depression. Simulations showed that BQQ plots were able to capture deviations from the n...Continue Reading

References

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Apr 22, 2010·Biostatistics·Ariel Alonso AbadGeert Molenberghs
Mar 16, 2012·Journal of Biopharmaceutical Statistics·Francisco J DiazJose de Leon
Mar 8, 2013·Current Pharmacogenomics and Personalized Medicine·Francisco J DiazJose de Leon
Mar 19, 2013·Therapeutic Drug Monitoring·Francisco J Diaz, Jose de Leon
May 3, 2013·BMC Medical Research Methodology·Kazem NasserinejadEmmanuel Lesaffre
Dec 30, 2014·Statistical Methods in Medical Research·Achmad EfendiGeert Molenberghs
Jul 6, 2016·Biometrics·Reza DrikvandiGeert Molenberghs
Nov 10, 2017·Statistical Methods in Medical Research·Francisco J Diaz

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