Journal Article

Efficient Estimation for Patient-Specific Rates of Disease Progression Using Nonnormal Linear Mixed Models

Qu, Annie, Zhang, Peng, Song, Peter X.-K., Greene, Tom

2008

Overview

This article presents a new class of nonnormal linear mixed models that provide an efficient estimation of subject-specific disease progression in the analysis of longitudinal data from the Modification of Diet in Renal Disease (MDRD) trial. This new analysis addresses the previously reported finding that the distribution of the random effect characterizing disease progression is negatively skewed. We assume a log-gamma distribution for the random effects and provide the maximum likelihood inference for the proposed nonnormal linear mixed model. We derive the predictive distribution of patient-specific disease progression rates, which demonstrates rather different individual progression profiles from those obtained from the normal linear mixed model analysis. To validate the adequacy of the log-gamma assumption versus the usual normality assumption for the random effects, we propose a lack-of-fit test that clearly indicates a better fit for the log-gamma modeling in the analysis of the MDRD data. The full maximum likelihood inference is also advantageous in dealing with the missing at random (MAR) type of dropouts encountered in the MDRD data.

• Publisher: Blackwell Publishing Inc,Blackwell Publishing,Blackwell Publishing Ltd
• Subject: Algorithms / Biometric Methodology / Biometrics / biometry / Biometry - methods / Biostatistics / Clinical trials / Computer Simulation / Data analysis / Data Interpretation, Statistical / diet / disease course / Disease models / Disease Progression / Dropouts / Epidemiologic Methods / Estimating techniques / Gauss-Newton algorithm / Glomerular filtration rate / Humans / Inference / kidney diseases / Lack-of-fit test / Likelihood Functions / Linear Models / Log-gamma distribution / Maximum likelihood estimation / Modeling / Normal distribution / Outcome Assessment (Health Care) - methods / Parametric models / Predictive distribution / Randomized Controlled Trials as Topic - methods / Risk Assessment - methods / Risk Factors / Skewed distribution / Statistical analysis / Statistical methods / statistical models