|Title||SNP_NLMM: A SAS Macro to Implement a Flexible Random Effects Density for Generalized Linear and Nonlinear Mixed Models.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Vock, David M., Marie Davidian, and Anastasios A. Tsiatis|
|Journal||J Stat Softw|
|Date Published||2014 Jan 01|
Generalized linear and nonlinear mixed models (GMMMs and NLMMs) are commonly used to represent non-Gaussian or nonlinear longitudinal or clustered data. A common assumption is that the random effects are Gaussian. However, this assumption may be unrealistic in some applications, and misspecification of the random effects density may lead to maximum likelihood parameter estimators that are inconsistent, biased, and inefficient. Because testing if the random effects are Gaussian is difficult, previous research has recommended using a flexible random effects density. However, computational limitations have precluded widespread use of flexible random effects densities for GLMMs and NLMMs. We develop a SAS macro, SNP_NLMM, that overcomes the computational challenges to fit GLMMs and NLMMs where the random effects are assumed to follow a smooth density that can be represented by the seminonparametric formulation proposed by Gallant and Nychka (1987). The macro is flexible enough to allow for any density of the response conditional on the random effects and any nonlinear mean trajectory. We demonstrate the SNP_NLMM macro on a GLMM of the disease progression of toenail infection and on a NLMM of intravenous drug concentration over time.
|Alternate Journal||J Stat Softw|
|Original Publication||SNP_NLMM: A SAS macro to implement a flexible random effects density for generalized linear and nonlinear mixed models.|
|PubMed Central ID||PMC3969790|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA085848 / CA / NCI NIH HHS / United States
R37 AI031789 / AI / NIAID NIH HHS / United States
SNP_NLMM: A SAS Macro to Implement a Flexible Random Effects Density for Generalized Linear and Nonlinear Mixed Models.